c© 2007 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim10.1002/14356007.a04 107.pub2

Biotechnology 1

Biotechnology

Thomas Becker, Institute of Process Analytics, University of Hohenheim, Germany

Dietmar Breithaupt, Institute of Food Chemistry, University of Hohenheim, Germany

Horst Werner Doelle, Department of Microbiology, University of Queensland, St. Lucia, Queensland 4067,Australia

Armin Fiechter, Institute of Biotechnology, Eidgenossische Technische Hochschule, Zurich, Switzerland

Martijn Griensven, Ludwig Boltzmann Institute, Wien, Austria

Cornelia Kasper, Institute of Technical Chemistry, University of Hannover, Germany

Stephan Lutz, Institute of Biotechnology 2, Research Centre Julich, Germany

Ralf Portner, Institute for Bioprocess Engineering, Technical University of Hamburg-Harburg, Germany

Hans-Gunther Schlegel, Institute of Microbiology, University of Gottingen, Gottingen, Germany

Dieter Sell, DECHEMA e. V., Frankfurt, Germany

Sakayu Shimizu, Department of Agricultural Chemistry, Kyoto University, Kyoto, Japan

Frank Stahl, Institute of Technical Chemistry, University of Hannover, Germany

Kirstin Suck, Institute of Technical Chemistry, University of Hannover, Germany

Roland Ulber, Institute of Bioprocess Engineering, University of Kaiserslautern, Germany

Joachim Wegener, Institute of Biochemistry, University of Munster, Germany

Kerstin Wurges, Institute of Biotechnology 2, Research Centre Julich, Germany

Hideaki Yamada, Department of Agricultural Chemistry, Kyoto University, Kyoto, Japan

Holger Zorn, Institute of Food Chemistry, University of Hannover, Germany

1. Introduction . . . . . . . . . . . . . . 32. Basics in Microbiology . . . . . . . 52.1. Microbiology – the Science of

Microscopic Life Forms . . . . . . 52.2. Phylogeny and Taxonomy of

Microorganisms . . . . . . . . . . . 72.2.1. Definition and Survey . . . . . . . . . 72.2.2. Bacteria . . . . . . . . . . . . . . . . . 72.2.3. Archaea . . . . . . . . . . . . . . . . . 82.2.4. Morphological and Physiological

Properties for the Identification ofProkaryotic Species . . . . . . . . . . 9

2.2.5. Eukaryotic Microorganisms . . . . . 112.2.5.1. Definition . . . . . . . . . . . . . . . . 112.2.5.2. Fungi . . . . . . . . . . . . . . . . . . . 113. Metabolism . . . . . . . . . . . . . . . 133.1. Microbial Systems Biology . . . . 143.2. Energy Production . . . . . . . . . . 143.3. Substrate Transport . . . . . . . . . 153.4. Catabolism . . . . . . . . . . . . . . . 163.4.1. Photosynthesis . . . . . . . . . . . . . 163.4.2. Chemosynthesis . . . . . . . . . . . . 16

3.4.3. Carbohydrate Metabolism . . . . . . 173.4.4. Aerobic Processes . . . . . . . . . . . 183.4.5. Fats and Fatty Acid Metabolism . . 203.4.6. Hydrocarbon Metabolism . . . . . . 203.4.7. Amino Acid Metabolism . . . . . . . 213.4.8. Anaerobic Metabolic Processes . . . 213.4.9. Single-Carbon-Compound

Metabolism . . . . . . . . . . . . . . . 213.4.10. Inorganic Metabolism . . . . . . . . . 223.5. Biosynthesis . . . . . . . . . . . . . . 233.5.1. Amino Acids . . . . . . . . . . . . . . 233.5.2. Lipids . . . . . . . . . . . . . . . . . . . 233.5.3. RNA and DNA . . . . . . . . . . . . . 243.6. Regulation . . . . . . . . . . . . . . . 244. Metabolic Engineering . . . . . . . 244.1. Analysis of the Transcriptome,

Proteome, and Metabolome . . . . 254.1.1. Gene Expression Analysis

using DNA Microarrays . . . . . . . 264.1.2. Fabrication of DNA microarrays . . 274.1.3. Proteome Analysis using Protein

Microarrays . . . . . . . . . . . . . . . 27

2 Biotechnology

4.1.4. Metabolome Analysis andMetabolite flux analysis . . . . . . . 28

4.2. Design and Production ofGenetically OptimizedStrains for Production – In VitroMutagenesis . . . . . . . . . . . . . . 30

4.3. Random Mutagenesis, Isolationand Selection of Mutants . . . . . . 31

4.4. Types of Mutants and SelectionPrinciples . . . . . . . . . . . . . . . . 32

4.4.1. Auxotrophic Mutants . . . . . . . . . 324.4.2. Regulatory Mutants . . . . . . . . . . 324.4.3. Other Selection Methods . . . . . . . 324.4.4. Targeted or Site-Directed

Mutagenesis . . . . . . . . . . . . . . . 345. Cultivation and Bioprocesses . . . 355.1. Isolation of Microorganisms . . . 355.2. Requirements for Growth . . . . . 365.2.1. Chemical Composition of Bacterial

Cells . . . . . . . . . . . . . . . . . . . 375.2.2. Carbon and Energy Sources . . . . . 375.2.3. Accessory Nutrients . . . . . . . . . . 385.2.4. Sulfur and Nitrogen . . . . . . . . . . 385.2.5. Oxygen . . . . . . . . . . . . . . . . . . 385.2.6. Complex Media . . . . . . . . . . . . 385.2.7. Solid Media . . . . . . . . . . . . . . . 385.2.8. Hydrogen Ion Concentration . . . . 385.2.9. Carbon Dioxide . . . . . . . . . . . . 385.2.10. Aeration . . . . . . . . . . . . . . . . . 385.2.11. Anaerobic Techniques . . . . . . . . 395.2.12. Media Preparation . . . . . . . . . . . 395.3. Sterilization . . . . . . . . . . . . . . 395.3.1. Moist Heat . . . . . . . . . . . . . . . 395.3.2. Dry Heat . . . . . . . . . . . . . . . . . 395.3.3. Filtration . . . . . . . . . . . . . . . . . 395.3.4. Irradiation . . . . . . . . . . . . . . . . 405.3.5. Chemical Means . . . . . . . . . . . . 405.4. Types of Bioprocesses . . . . . . . . 405.4.1. Surface Culture . . . . . . . . . . . . . 405.4.2. Submerged Culture . . . . . . . . . . 405.5. Process Layout . . . . . . . . . . . . 415.5.1. Reactors . . . . . . . . . . . . . . . . . 415.5.2. Containments for Anaerobic

Processes . . . . . . . . . . . . . . . . 415.5.3. Reactors for Aerobic Processes . . . 415.5.4. Inoculation . . . . . . . . . . . . . . . 415.5.5. Operation Modes . . . . . . . . . . . 425.6. Process and Product Overview . . 456. Biocatalysis and

Biotransformation . . . . . . . . . . 456.1. Introduction . . . . . . . . . . . . . . 456.2. Classification of Biocatalysts . . . 476.3. History . . . . . . . . . . . . . . . . . 47

6.4. Characteristics of EnzymeReactions Used inBiotransformations . . . . . . . . . 50

6.5. Types ofBiocatalysts andReactionSystems . . . . . . . . . . . . . . . . . 51

6.5.1. Biotransformation with GrowingCultures . . . . . . . . . . . . . . . . . 51

6.5.2. Biotransformation Conversion withPreviously Grown Cells . . . . . . . 52

6.5.2.1. Vegetative or Washed Cells . . . . . 526.5.2.2. Permeabilized Cells . . . . . . . . . . 526.5.2.3. Dried Cells . . . . . . . . . . . . . . . 526.5.3. Biotransformation with Spores . . . 526.5.4. Biotransformation with

Immobilized Cells . . . . . . . . . . . 536.5.5. Biotransformation with Cell-free

Enzymes or Purified Enzymes . . . 546.5.6. Multistep Reactions Using Different

Biocatalysts . . . . . . . . . . . . . . . 546.5.7. Multiphase Reaction Systems . . . . 546.6. Process Design . . . . . . . . . . . . 556.6.1. General Considerations . . . . . . . . 556.6.1.1. Evaluating Enzyme Potential . . . . 556.6.1.2. Finding Suitable Enzymes . . . . . . 576.6.1.3. Substrates . . . . . . . . . . . . . . . . 576.6.1.4. Media . . . . . . . . . . . . . . . . . . . 586.6.2. Selection of Biocatalysts . . . . . . . 586.6.2.1. Screening . . . . . . . . . . . . . . . . 586.6.2.2. Enrichment . . . . . . . . . . . . . . . 586.6.2.3. Molecular Engineering . . . . . . . . 596.7. Improvement of Conversion

Processes . . . . . . . . . . . . . . . . 596.8. Conclusion and Outlook . . . . . . 607. Downstream Processing . . . . . . 617.1. Sample Disruption . . . . . . . . . . 627.2. Solid–Liquid Separations . . . . . 637.3. Product Recovery . . . . . . . . . . 647.4. Solvent Extraction . . . . . . . . . . 658. Monitoring and Modeling

of Bioprocesses . . . . . . . . . . . . 658.1. Characteristics of Bioprocesses . 668.1.1. System Definition . . . . . . . . . . . 668.1.2. System Description . . . . . . . . . . 698.1.3. Dynamics of Biosystems and

Real-Time Considerations . . . . . . 718.2. Biotechnological Measurement

Systems . . . . . . . . . . . . . . . . . 738.2.1. Process Requirements Concerning

Measuring Quantities . . . . . . . . . 748.2.2. Online Sensing Devices . . . . . . . 758.2.3. Further Aspects Concerning

Measuring Systems . . . . . . . . . . 798.3. Cognitive Computing . . . . . . . . 808.3.1. Fuzzy Logic Systems . . . . . . . . . 82

Biotechnology 3

8.3.2. Artificial Neural Networks (ANN) . 858.4. Modeling Aspects of Biological

Systems . . . . . . . . . . . . . . . . . 888.4.1. Steps in Creating a Model . . . . . . 888.4.2. Reasons for Making a Model . . . . 918.4.3. Different Types and Basic

Approaches for Building a Model . 939. Special Applications in

Biotechnology . . . . . . . . . . . . . 969.1. Mammalian Cell Culture

Technology . . . . . . . . . . . . . . . 969.1.1. Introduction . . . . . . . . . . . . . . . 969.1.2. Products from Mammalian Cells . . 989.1.3. Cell Types . . . . . . . . . . . . . . . . 999.1.4. Growth Medium for Cell Culture . 1019.1.5. Small-Scale Culture Systems for

Routine Use . . . . . . . . . . . . . . . 1029.1.6. Types of Bioreactors . . . . . . . . . 1039.1.7. Process Strategies . . . . . . . . . . . 1079.1.8. Downstream Processes . . . . . . . . 1089.1.9. Regulatory and Safety Issues . . . . 1089.2. Tissue Engineering . . . . . . . . . . 1099.2.1. Application of Tissue Engineering . 1099.2.2. Principle of Tissue Engineering . . 1109.2.3. Strategies . . . . . . . . . . . . . . . . 1119.2.4. The Essentials . . . . . . . . . . . . . 1119.2.5. Cells . . . . . . . . . . . . . . . . . . . 1119.2.6. Biomatrices . . . . . . . . . . . . . . . 1119.2.7. Bioreactors for Tissue Engineering 112

9.2.8. Growing New from Old . . . . . . . 1129.3. Biotechnology and Food . . . . . . 1139.3.1. Production of Food Additives by

Cell Culture Systems . . . . . . . . . 1139.3.1.1. Amino Acids . . . . . . . . . . . . . . 1139.3.1.2. Organic Acids . . . . . . . . . . . . . 1149.3.1.3. Vitamins . . . . . . . . . . . . . . . . . 1149.3.1.4. Sweet Compounds . . . . . . . . . . . 1159.3.1.5. Sugar Alcohols . . . . . . . . . . . . . 1159.3.1.6. Microbial Saccharides . . . . . . . . 1169.3.1.7. Conjugated Linoleic Acids (CLA) . 1169.3.1.8. Lactulose . . . . . . . . . . . . . . . . 1169.3.2. Enzyme-Catalyzed Processes . . . . 1179.3.2.1. Starch-Modifying Enzymes . . . . . 1179.3.2.2. Lipases . . . . . . . . . . . . . . . . . . 1189.3.2.3. Pectin-Degrading Enzymes . . . . . 1189.3.2.4. Chymosin (Aspartic Protease) . . . 1199.4. Biotechnology and Health . . . . . 1209.4.1. Individualized Medicine . . . . . . . 1209.4.2. Clinical Diagnosis as Indicated in

Genetic Anomalies in Cancer . . . . 1209.4.3. Pharmaceutical Development . . . . 1209.4.4. Define Molecular Mechanisms of

Toxicity . . . . . . . . . . . . . . . . . 1229.4.5. Detection of Genetically Modified

Organisms . . . . . . . . . . . . . . . . 12210. Concluding Remarks . . . . . . . . 12211. Acknowledgement . . . . . . . . . . 12312. References . . . . . . . . . . . . . . . 123

Biotechnology can be regarded as one of thekey technologies of the 21st century. It is thecommercial application of living organisms suchas bacteria, fungi, yeasts, plant cells, viruses, andmammalian cells or their products, which in-volves the deliberate manipulation of their DNAmolecules. This article gives an introduction intothe basics in microbiology and provides an ex-haustive description of the relevant microbialspecies and metabolic pathways. Identification,analysis, and manipulation of the genome, pro-teome, and metabolome is described, and cul-tivation requirements as well as process pa-rameters discussed. Biotransformation and en-zyme technology plays a central role in indus-trial biotechnology, and a focus is given onthe development of molecular engineering tech-niques and new screening methods. Computa-tional Biochemistry comprises the definition,monitoring, and modeling of bioprocesses. Ex-amples of biotechnology applications include

mammalian cell technology, tissue engineering,and the production of relevant food additivesas well as of various medical and pharmaceu-tical products. In conclusion, biotechnology of-fersmanifold possibilities in industrial andmed-ical applications.

1. Introduction

Biotechnology can be described as “the com-mercial application of living organisms or theirproducts, which involves the deliberate manipu-lation of their DNA molecules”. This includesthe use of bacteria, fungi, yeasts, plant cells,viruses, and mammalian cells. This definitionimplies a set of laboratory techniques devel-oped within the last 20 years that have beenresponsible for the tremendous scientific andcommercial interest in biotechnology, the found-ing of many new companies, and the redirec-tion of research efforts and financial resources

4 Biotechnology

among established companies and universities.Thus, biotechnology can be regarded as one ofthe key technologies of the 21st century. How-ever, the use of microorganisms and their prod-ucts is as old as mankind. Fermented productswith yeast such as beer, wine, or sake are knownsince several thousands of years (Table 1). Theroots of modern biotechnology are coming fromthe era of microbiology, which was developedin the late 19th century. Cagniard-Latour, Pas-teur, Koch and Kuhne were important scientistswho are decoding the principles of fermentation.The use of microorganisms for the production ofbulk chemicals such as butanol or acetone andfor pharmaceuticals (antibiotics) was stronglydeveloped during World War I and II. With thebetter understanding of the gene function and itsrelation to the metabolisms of cells, the modernera of biotechnology started in the 70th of thelast century.

Nowadays, biotechnology is a cross-sectoraltechnology that has been successfully appliedin many industrial branches. One distinguishesbetween several areas or “colors” of biotechnol-ogy (the so-called red, white, green, and bluebiotechnology).

Red Biotechology. Named as red biotech-nology are applications in medicine or in thepharmaceutical industry. Red biotechnology hasalready made an impact on healthcare andwill continue to contribute to improving humanhealth and life expectancy. Today, 20% of mar-keted medicines, 50% of those in clinical tri-als, and 80% in early development are biotech-based products. Forty percent of these candidatemedicines are for the treatment of cancer. Typi-cal products of red biotechnology are recombi-nant vaccines, antibodies, blood clotting agents,and hormones. In addition, tissue engineering isa part of the area of biotechnology. Tissue en-gineering aims at the functional regeneration oftissues through implantation of tissue culturedin vitro (see Section 9.2).

White Biotechology. The potential and ap-plications of biotechnology in other sectorslargely pass unnoticed especially in the chemicalindustry. The chemical industry has producedcoal, gas, and petroleum-based goods and prod-ucts for over 150 years. This era is likely to

Table 1. Timetable of biotechnology [1, 2]

Year Biotechnological progress

3000 b.c. Fermentation of sugar containing juices tovarious alcoholic beverages

2800 b.c. brewing rooms in Mesopotamia and leaven inEgypt

1500 b.c. Use of microorganisms for the production ofcopper and production of soya sauce

300 b.c. Use of vinegar1300 Micro algae (Spirulina) as food additives

(Aztecs)1400 Production of saltpetre (potassium nitrate)

with Nitrosomas sp. and Nitrobacter sp. inGermany

1676 Anthony van Leuwenhoek observes bacteriathrough a microscope

1837 Charles Cagnaird-Latour identifies yeast ascauser of fermentation (one year laterconfirmed by Theodor Schwann and FriedrichKutzing)

1849 Industrial production of bakers yeast1856 Louis Pasteur separates brewer yeasts from

lactic bacteria1876 Kuhne creates the term “enzymes”1881 Industrial microbial production of lactic acid

(Boehringer)1890 Development of first vaccines by Pasteur and

Koch1893 Industrial production of citric acid using

Aspergillus niger1897 Starting point of enzyme technology (Eduard

Buchner)1915 Patent for enzymes in washing powder1916 Industrial fermentation process for acetone

and n-butanol (Chaim Weizmann)1928 Alexander Fleming discovers penicillin1937 Microbial production of vitamin C1941/42 Industrial production of antibiotics1957 Use of Corynebacterium glutamicum for the

production of amino acids1972/1973 Stanley Cohen and Francis Boyer develop a

procedure for in vitro recombination of DNA,using plasmid vectors

1977 First recombinant proteins from bacteria1982 First transgenic plants and animals1985 Development of polymerase chain reaction

(PCR) by Kary Mullis1990 Start of human genome project (HUGO)1996 Yeast genome is completely sequenced2000 Human genome is completely sequenced

(Craig Venter)

end within the next 100 years at the latest. Anextrapolation of oil consumption from 2002 (3.3billion tonnes), for example, yields a supply pe-riod of 50 years with estimated stocks of 165billion tonnes. The underlying reasons for a fu-ture era of new raw materials are the finite na-ture of fossil resources on the one hand, and theconnection with many environmental problemson the other hand. In the past few years condi-tions for the application of biotechnological pro-cesses in industrial production have improved

Biotechnology 5

(so-called white biotechnology). New tools suchas screening methods and metabolic engineer-ing, and also global analysismethods such as ge-nomics, proteomics, metabolomeics, and bioin-formatics are gradually becoming more widelyavailable. These new instruments make it possi-ble to reduce the time needed to develop and es-tablish new industrial biotechnological productsand processes; this was hitherto one of the ma-jor drawbacks of biotechnological, as opposedto chemical, processes. In addition, they helpto develop biocatalysts (enzymes) and microor-ganisms which render manufacturing processesmore economical and facilitate newmanufactur-ing processes. For the first time since the begin-ning of the oil age in the early fifties, one is ableto apply processes with economic potential inthe production of basic chemicals and biopoly-mers based on biotechnological processes. Inmany cases, bioprocesses operate under milderconditions (lower temperatures and pressures,etc.) and more selectively than their competitors(chemical processes) by these means biopro-cesses conserve resources and improve produc-tion processes economically and ecologically.Public debate has often emphasized the advan-tages for the environment; however, companiesface hard economic competition so that whena biotechnological process is weighed againsta classical chemical process, only the potentialeconomic advantages can affect a shift in favourof biotechnology.

Green Biotechnology. In addition, to over-come the problems in the production both ofbulk and fine chemicals but also in the produc-tion of vaccines and pharmaceuticals, the so-called green biotechnology can offer new so-lutions. This area of biotechnology involves theintroduction of foreign genes into economicallyimportant plant species, resulting in crop im-provement and the production of novel productsin plants. Green biotechnology might also pro-duce more environmentally friendly solutionsthan traditional industrial agriculture. An exam-ple of this is the engineering of a plant to expressa pesticide, thereby eliminating the need for ex-ternal application of pesticides.

Blue Biotechnology. In a very broad sense,marine or blue biotechnology can be understoodas the various means of techniques of managing

marine living systems for mankind’s profit. Thedomain covered bymarine biotechnology is vastand range over various overlapping disciplines,from developmental biology to chemistry of nat-ural substances and bioprocess engineering. Notall these fields, however, are ready for practicaland industrial applications. Biomass from fish-ing or aquaculture industry is, in fact, complex,geographically and seasonally dependent. Fur-thermore, many “natural substances”, for whichwe do not know of any terrestrial counterparts,and, therefore, present an upmost interest, existsonly in tiny amounts in rare biological speciesand their exploitation is likely to call for costlysynthesis procedures. Marine natural productsnot only display novelty but also complexity interms of chemical structures. Isolation and struc-ture elucidation represents only the emerged partof an iceberg. The question of the functionalityof new isolated molecules within the perspec-tive of challenging major public health and en-vironmental problems is crucial. Likely, in thisdomain, ecological and evolutionary approachesshould help the classical screening systems fordetermining the right target systems. In addi-tion, a better understanding of the complex in-teractions between macro- and microorganismsis necessary to be able to use these resourcesfor industrial purposes. Thus, more and moregroups are focussing on the part of bioprocessengineering and downstream processing in bluebiotechnology [3].

2. Basics in Microbiology

2.1. Microbiology – the Science ofMicroscopic Life Forms

Microbiology deals with microscopically tinylife forms which the resolution of the humaneye (approx. 20 µm) cannot detect. Many ofthe single-cell microorganisms, such as yeasts,algae, or protozoa, do not reach this size. Notuntil the invention of the microscope (Antonievan Leeuwenhoek, 1684) was the detection, andthus also the characterization, of microorgan-isms possible [4].

If microorganisms are defined by their size,many varied life forms come into this category:single-cell life forms with a real cell nucleus

6 Biotechnology

(e.g., algae, fungi, protozoa) and also somewith-out a real cell nucleus (bacteria and archaea)which are of particular interest in general mi-crobiology. Organisms with a genuine cell nu-cleus are termed eukaryotic organisms, thosewithout prokaryotic organisms [5]. Today some6500 species of prokaryotic bacteria and archaeaare known, a relatively small number comparedwith the number of the known species of eukary-otic organisms. However, it is estimated that thenumber of actually living species of prokary-otic organisms is much higher, although most ofthem are regarded as not cultivable under labo-ratory conditions. Analyses of the metagenomesof the most varied habitats (total DNA assays ofsamples from various environments, such as for-est soil, compost, pond water, etc.) have yieldedestimated values of up to one billion potentialspecies of prokaryotic organisms worldwide.

For most people, microorganisms have a badimage. The reason is not hard to find: somemicroorganisms can cause disease in man andanimals (pathogenic microorganisms). Thanksto the achievements of medical microbiology,the often devastating effects of pathogenic mi-croorganisms in earlier times have been confineddue to the development of vaccines, antibiotics,etc. [6]. Nevertheless, microorganisms still re-present a threat to health (for instance to peo-ple with a weakened immune system). The fol-lowing are a few examples of bacteria that arepathogenic to humans: Bacillus anthracis (an-thrax), Bordetella pertussis (whooping cough),Clostridium botulium (botulism), Clostridiumtetani (tetanus), Corynebacterium diphtheriae(diphtheria), Shigella dysenteriae (shigellosis),Vibro-cholerae (cholera). The development ofresistance to known antibiotics is a phenomenonthat documents the variability of pathogenic mi-croorganisms [7]. This also explains why it isnecessary to promote progress in the field ofcombating infectious diseases with microbio-logical methods.

Biotechnology is closely linked to micro-biology. Biotechnology uses microbiologicalmetabolism performance to manufacture indus-trially relevant products. The sector with theoldest tradition of applying microorganisms forproduction purposes is the food industry. Themanufacture of beer, wine, vinegar, bread, andcheese are early achievements of human history

and pertain to the field of food microbiology.Microorganisms are used in the production ofsausages (particularly salami), sauerkraut, andmilk products (lactobacilli). In Asia, a variety offermented food products are produced on thebasis of rice and soya [8, 9].The typical aro-matic properties and consistency of many foodproducts originate from the activity of the mi-croorganisms used. Nowadays, these organismsare generally used in the production process asstarter cultures. The fermentation products arealso often used as preservatives (acidification inlactic acid production by lactobacilli or alcoholproduction by yeasts).

The monitoring of food in the framework ofquality control involves excluding undesiredmi-crobial infestation or providing evidence of it.

Microorganisms contribute towards stabiliz-ing the global natural material cycles (e.g., car-bon andnitrogen cycles). Furthermore, thewasteflows produced by man are largely reintroducedinto the natural material flows through the ac-tivity of microorganisms. The field of environ-mental microbiology addresses the question ofhow microorganisms can be utilized for the re-mediation of contaminated soil,water, andwastegases. Successful industrial processes have beendeveloped and are used worldwide for wastewa-ter and drinking water purification, brownfieldclean-up, composting, and waste fermentationand also for the purification of contaminatedgases [10 – 12].

Industrial microbiology, or white biotechnol-ogy, deals with the production of industrially in-teresting substances involving production vol-umes of thousands of tons. Effective productionof substances requires on the one hand an effi-cient production strain to supply as much targetproduct as possible in the shortest time possi-ble. On the other hand, it needs a technical fa-cility where the high-performance microorgan-isms find optimum living and, therefore, produc-tion conditions and where in addition monosep-tic cultivation in large volumes is guaranteed.Bioreactors fulfill these requirements: they canbe operated with volumes of up to several hun-dred m3 and are equipped with automatic mea-surement and control systems adjusted to thegrowth needs of the microorganism [13, 14].

Examples of microbially produced industrialproducts are diverse. The list begins with mi-croorganisms that can themselves be the prod-

Biotechnology 7

uct of a biotechnological process and reachthe market as starter cultures [15]. Currentlythe quantitatively dominant product is ethanol,of which over 24× 106 (metric) tons are pro-duced worldwide. Ethanol is produced with theyeast Saccharomyces cerevisiae and is mainlyused as a substitute fuel or in admixture withother fuels. Other products include pharmaco-logically active agents (e.g., antibiotics, steroids,and alkaloids), bulk, special, and fine chemicals,food and feed additives, chiral intermediates, en-zymes, antibodies, etc.

The total value of all biotechnologically pro-duced substances worldwide (including bio-pharmaceuticals which are often manufacturedwith cultures of animal cells) must be in therealm of > 100× 109 euros annually.

2.2. Phylogeny and Taxonomy ofMicroorganisms

2.2.1. Definition and Survey

Taxonomy is the branch of biology which sub-sumes individual groups of species into a hier-archical system. For a long time, the kingdomwas the highest category of creatures. Origi-nally, a distinction was made between animalsand plants. Later, the bacteria were added andthen the fungi and plants were split up [16]. Fi-nally the archaeawere given their own kingdom.In recent years, genetic investigations have ledto a new classification with the “domain” super-imposed over the kingdom. Whereas in earliertimes the aim was to understand and deduce re-lationships among microorganisms on the basisof morphological, physiological, and cytologi-cal similarities, in the last few decades geneticsimilarity has been the criterion used to identifyrelationships directly from the genetic material.Nowadays, sequence analysis of ribosomalRNA(rRNA),whichwas developed byCarlWoese, isone of the methods by which phylogenetic treescan be generated [17].

Probably the best-known gene in the world isthe 16S rRNA (S, Svedberg unit for characteriz-ing the behavior of sedimentation) of prokary-otic organisms. This gene is composed of around1500 base pairs and in the course of evolutionwas only subjected to relatively minor changesthrough mutations. The reason is that ribosomes

are the sites of protein biosynthesis and the mo-lecular mechanism taking place is extremelysensitive to radical mutations. Thus, there areareas within 16S rRNA that are extremely wellpreserved, but also some where base exchangeshave been more frequent. An evolutionary gapbetween two organisms can be determined fromthe relative similarity of different genes for the16S rRNA of various prokaryotic organisms;this can be used as a measure of the degree ofrelationship [18].

Analogous to the investigations on prokary-otic organisms, the characterization of the de-gree of relationships of eukaryotic organisms isderived from their 18S rRNA, a molecule withapproximately 2300 bases.

Besides comparing the base sequences ofrRNA, the amino acid sequences of universalproteins may also be drawn on to establish rela-tionships. This does not always involve the samebranchings of the phylogenetic tree as with thecomparison of rRNAs. This explains why tax-onomy, which owes its motivation primarily tothe potential inherent in the newmolecular biol-ogy tools, is currently a highly dynamic area ofscience.

Today, all living organisms on earth are clas-sified into three domains: archaea, bacteria, andeukaryota. The following representation of themanifold life forms of microorganisms adheresto this phylogenetic classification.

2.2.2. Bacteria

Bacterial cells are small, generally less than 2µm in diameter and 2–5 µm long (there are onlya few giants of 5× 20µmdimensions). The cellsdo not contain a membrane-enveloped nucleus;their DNA is rather a circularly closed doublestrand lying within the cytoplasm. The bacte-rial DNA contains the total genetic informationof the cell. In many bacteria there are additionalDNAcircles, the plasmids; however, they are notrequired for growth under ordinary culture con-ditions. The ribosomes are small (sedimentationconstant in the ultracentrifuge: 70 S), and theirfunction is sensitive to various antibiotics thatdo not act upon the cytoplasmic ribosomes ofeukaryotic organisms. In contrast to eukaryoticcells, bacterial cells do not contain organelles,

8 Biotechnology

but often polyphosphates, poly(β-hydroxybu-tyric acid), or glycogen as storage substances(Fig. 1). Most bacteria are surrounded by a cellenvelope that contains peptidoglycan (murein).Many bacteria are motile by either flagellar orgliding movements.

Figure 1. Longitudinal section through a bacterial cellca) capsule; cm) cytoplasmic membrane; cp) cytoplasm;cw) cell wall; f) flagellum; gly) glycogen; li) lipid droplets;n) nuclear material or bacterial chromosome; phb) poly(β-hydroxybutyric acid); pi) pili; pl) plasmid; po) polyphos-phate; rb) ribosomes; s) sulfur granules

Classification of BacteriaAccording to thePhylogenetic System. Originally, the subdivi-sion of bacteria was based on their morphologyand physiology, in the last few years, however,their classification has been completely revisedand established on a molecular biology level.Thus, the taxonomy of bacteria, especially thehigher levels of classification, cannot yet be re-garded as definitive. Some authorities believethat the degree of variance between differentbacterial groups is sufficient to give them each“kingdom status” of their own. Thus, in somepublications reference is made to 13 kingdomsof bacteria, in others to “phyla” instead of king-doms.

The classification presented is derived fromBergey’sManual of Determinative Bacteriology[9], which the bacteria are subdivided into thefollowing phyla:

– Proteobacteria– Gram-positive bacteria– Cyanobacteria– Chlamydia– Plantomyces– Bacteroids/Flavobacteria– Green sulfur bacteria– Spirochetes– Deinococci– Green non-sulfur bacteria– Hyperthermophiles (3 kingdoms)

Bearing in mind all the postulated, noncul-tivable species, the actual number of bacterialphyla (or kingdoms) is probably far higher. Esti-mates assume that there will ultimately be about50 phyla (or kingdoms) of bacteria. The cur-rent phylogenetic tree, therefore, can only beregarded as a snapshot.

The bacterial phyla with the highest num-ber of species known today are the proteobac-teria, Gram-positive bacteria and cyanobac-teria. Cyanobacteria are phototrophic organ-isms closely related to Gram-positive bacte-ria. Gram-positive bacteria form a group ofchemo-organotrophic bacteria. The phylum ofproteobacteria is the largest and physiologi-cally most varied of all bacteria. Representa-tives of this phylum account for the majority ofknown Gram-negative bacteria of medical- andapplication-oriented interest [20].

Even the possibly best-known bacterium, Es-cherichia coli, belongs to this phylum. This ex-ample is used to demonstrate the taxonomic clas-sification of an organism:

Domain: BacteriaPhylum: ProteobacteriaClass: Gamma protobacteriaOrder: EnterobacterialesFamily: EnterobacteriaceaeGenera: EscherichiaSpecies: Escherichia coli

The binomial nomenclature is used through-out biology. Each organism has a genus nameand a species name and is thus specified unam-biguously.

2.2.3. Archaea

Several properties distinguish archaea from bac-teria and eukaryota. Archaea are single-cell or-ganisms mainly with a closed DNA moleculelying within the cytoplasm. They are of thesame size as bacteria. Archaea have neither acytoskeleton nor cell organelles, they are dis-tinguished from bacteria by their lack of pepti-doglycan and the different structure of their ri-bosomes. With over 200 species they are oftenfound where extreme conditions prevail.

Archaea have cell walls of pseudopeptido-glycan and single-layer cell membranes formedfrom ether lipids with covalent bonded chains.Many species of archaea have adapted to their

Biotechnology 9

extreme surroundings, hence some species pre-fer temperatures of over 80◦C (thermophile),others live in salt waters (halophile) or invery acidic or alkaline environments (aci-dophile/alkaliphile) [21].

Enzymes from such extremophile organisms,which are not only found among archaea, areparticularly interesting for industrial applica-tions; indeed they seem predestined for use inindustrial processes with high temperatures andsalt concentrations [22]. In the area of amylasesthermotolerant enzymes are already applied forstarch saccharification. Moreover the enzymesused in detergents (proteases, lipases, amylases,cellulases) are active and fulfill their functionsat temperatures of 60◦C and above [23].

The phylogenetic system of the archaea thatis valid at present subdivides the domains intothe following phyla [19]:

– Euryarchaeota– Crenarchaeota– Korarchaeota– (Nanoarchaeota is proposed as an additionalphylum)

– Archaeota

The Euryarchaeota are a diverse group.This phylum comprises not only methanogenicspecies, which are strictly anaerobic organisms,but also strictly aerobic organisms. Furthermorea large group of hitherto noncultivable organ-isms belong to this phylum and similarly to thatof the Crenarchaeota, which include both hy-perthermophilic representatives and mesophilicmarine species. It is only very recently that re-presentatives of the Korarchaeota phylum havebeen cultivated successfully.

2.2.4. Morphological and PhysiologicalProperties for the Identification ofProkaryotic Species

The properties of prokaryotic organisms as pre-sented here permit the identification of speciesby conventional methods if molecular biologytechniques are not available or cannot be ap-plied. The shapes ofmost species are similar, andthere are only a few morphological characteris-tics suitable for differentiation. In contrast, thediversity of metabolic features is tremendous.Prokaryotic organisms are extremely versatile.

Therefore, a great number of physiological andbiochemical properties have to be determined todescribe and identify a species unambiguously.

Properties and description of prokaryotic or-ganisms:

1) Shape.The majority of prokaryotic organisms arespheres, rods, or helices; these organisms arecalled cocci, rods, and spirilla, respectively(Fig. 2). Some organisms are encapsulatedby proteins or polysaccharides, and some arecombined to form packets, filaments, or con-sortia.

2) Flagellation.The kind of flagellation, length, type of undu-lation, and mode of attachment of flagellaeare important characteristics of prokaryoticorganisms (Fig. 3).

3) Gram Stain.The Gram stain, originally devised by CHR.GRAM (1884) to make bacteria visible in an-imal tissues, is a reliable and fundamentalcellular characteristic by which the bacteriaare divided into two parts: Gram-positive andGram-negative bacteria. It is based on the re-tention or discoloration of bacteria by ethanolafter a staining procedure involving an ani-line dye and iodine. In Gram-positive bacte-ria, the dye is retained by a cell wall consistingof a multilayered network of peptidoglycan;in Gram-negative bacteria, the dye is readilywashed out because the cell wall consists ofonly two peptidoglycan layers and a highlypermeable outer membrane envelope. The ba-sic structural differences are easily visible inultrathin sections (Fig. 4).

4) Endospores.Endospores are highly refractile, thermoresis-tant stages in the life cycle of two groupsof Gram-positive bacteria belonging to thegenera Bacillus and Clostridium. Endosporesare easily visible and tolerate pasteurization(treatment by moist heat for 10 min at 80 ◦C).Heating in an autoclave for 20 min at 120 ◦Cis required to kill the spores.

5) Need for Oxygen.Many prokaryotic organisms grow only in thepresence of atmospheric oxygen. They arestrictly aerobic organisms. In contrast, strictlyanaerobic organisms can only grow in the ab-sence of oxygen. Facultatively anaerobic or-

10 Biotechnology

ganisms can grow under both conditions. Thelast group includes aerotolerant specieswhichtolerate but do not use oxygen. Others useoxygen if it is available. Some aerobic speciesaremicroaerophilic, i.e., they grow at low par-tial pressures of oxygen but not in equilibriumwith air.

6) Energy Generation.The production of energy, i.e., regenerationof adenosine triphosphate (ATP), occurs viathree fundamental processes, namely, fermen-tation, respiration, and photosynthesis. Thestrictly anaerobic fermentative prokaryoticorganisms rely only on fermentation.All strictaerobes regenerate ATP by oxidative phos-phorylation with oxygen as the electron ac-ceptor. Mechanistically, energy generation bydenitrifying, sulfate-reducing, methanogenic,or acetogenic species is quite similar to aer-obic respiration and is therefore designatedas anaerobic respiration. All photosyntheticmicroorganisms contain light-absorbing pig-ments, such as chlorophyll derivatives andcarotenoids. The processes by which ATPis regenerated are similar in respiration andin photosynthesis; both are described aselectrophosphorylation.

7) Effects of Temperature and pH.The majority of organisms are mesophilic:they grow at their maximum rate between 20and 42 ◦C. Thermophilic bacteria and archaeareach their maximum growth rates between40 and 80 ◦C. A few of them are able togrow above 80 ◦C and up to temperatures ofover 100 ◦C; they are called extremely ther-mophilic organisms. The psychrophilic (orkryophilic) species prefer temperatures below20 ◦C.Most organisms tolerate pHvalues bet-ween 5 and 8 but prefer neutrality; a few areacid-tolerant, acidophilic, or alkali-tolerant,alkaliphilic.

8) Nutritional Types.All organisms that can use light energy forgrowth are called phototrophic. In contrast,the term chemotrophic denotes energy conver-sion by oxidation – reduction reactions thatinvolve either fermentation or respiration.Organotrophic denotes the utilization of or-ganic compounds, and lithotrophic the utiliza-tion of inorganic compounds (ammonia, ni-trite, hydrogen sulfide, sulfur, hydrogen, car-bon monoxide, iron(II)) as reductants. Au-

totrophic characterizes the ability to syn-thesize the majority of cellular constituentsfrom carbon dioxide, and heterotrophic, thederivation of cellular constituents from or-ganic compounds. The designations are usu-ally combined, e.g., Chromatium okenii isphotolithoautotrophic. Some organisms re-quire accessory nutrients, such as vitamins oramino acids, for growth.

9) Natural Habitats.The ecological habitat is the place wherean organism or a population can usually befound. Examples are marine water, the sed-iment of a lake, fertile soil, or the intestinaltract.

10) Symbiotic or Parasitic Relationships.The close relationship between two dissimilarorganisms is called symbiosis. It may be fa-vorable for both (mutualistic) or unfavorablefor one (parasitic).

Figure 2. Shapes of bacteriaA) Cocci; B) Diplococci, without and with capsule;C) Streptococci; D) Tetrads; E) Package cocci (Sarcina);F) Rods; G) Spore-forming bacteria, differing with respectto shape and localization of endospores in the mothercell; H) Short rods, coccobacilli; I) Coryneform bacte-ria; J) Mycobacteria; K) Vibrio-like bacteria; L) Spirillum(Thiospirillum); M) Spirilla (Aquaspirillum); N) Spindle-shaped bacteria; O) Trichomes of filamentous cyanobac-teria; P) Filamentous cyanobacteria with heterocyst;Q) Filamentous helically shaped cyanobacterium (Spir-ulina); R) Spirochaetes (Spirochaeta plicatilis)

Biotechnology 11

11) Composition of Cellular Macromolecules.The description of bacteria includes the Gramtype of the cell wall, the type of peptidoglycanstructure, the fraction of cytosine + guaninein the DNA, the similarity index of the 16 SrRNA, and data onDNA–DNAhybridization.

12) Antibiotic Resistance.The characterization of an organism may besupplemented by its pattern of antibiotic re-sistance.

Figure 3. Flagellation of bacteria, flagellar movement

Figure 4. Gram-positive and Gram-negative bacterial cellwalls

2.2.5. Eukaryotic Microorganisms

2.2.5.1. Definition

The cells of most eukaryotic organisms are usu-ally much larger than prokaryotic cells and havediameters of 10 µm or more [24]. They containseveral structural components, compartments,and organelles. Figure 5 shows a plant cell asan example of a eukaryotic cell. The nucleus,consisting of a set of chromosomes that dividebymitosis, is surroundedby adoublemembrane.

TheDNA is associated with histone protein.Mi-tochondria (for respiratory energy conversion)and chloroplasts (in plants for photosyntheticenergy conversion) contain a prokaryotic typeof DNA and ribosomes. Cytoplasmic ribosomesare large (80 S). Endoplasmic membranes andvarious kinds of membrane vesicles compart-mentalize the cell; they are involved in the in-gestion of nutrients (endocytosis) and the pro-duction and excretion of proteins and particles(exocytosis).

An internal skeleton, the cytoskeleton, con-sisting of contractile protein (actin) filamentsand microtubules, gives the cell its shape andconfers ability to move and to transport mem-brane vesicles within the cell. The cells of eu-karyotic microorganisms can bemotile by eitherameboid or flagellar movement.

Most eukaryotic cells and organisms are aer-obic. Energy for growth is derived from respira-tion or photosynthesis.

Eukaryotic microorganisms include the pro-tozoans, algae, and fungi. Fungi are themost rel-evant representatives of eukaryotic microorgan-isms in biotechnology and are used to produceantibiotics, secondary metabolites, and also or-ganic acids, vitamins, etc. For this reason, fungiwill be considered here in more detail.

Figure 5. Longitudinal section of a eukaryotic plant cellcw) Cell wall; chl) Chloroplast; cm) Cytoplasmic mem-brane; cp) Cytoplasm; di) Dictyosome; er) Endoplasmicreticulum; ex) Secretion vesicle for exocytosis; li) Lipiddroplet; mi) Mitochondrion; mt) Microtubule; n) Nucleusor karyon; rb) Ribosomes; v) Vacuole

2.2.5.2. Fungi

Fungi are aerobic eukaryotic organisms; theyform a separate kingdom within the domainof the eukaryota [25, 26]. There are unicellu-lar fungi, but the majority form filaments (hy-phae), (usually 5–10 µm in diameter) and growasmasses of hyphae (mycelia).Many fungi form

12 Biotechnology

fruiting bodies. The shapes of fungi are diverse,and fungi are usually identified on a morpholog-ical basis. They are much less versatile metabol-ically than bacteria, andmetabolic properties arealmost useless for identification. Fungi have cellwalls that often consist of chitin or cellulose. Thegroup is rich in species, their number exceeding100 000. Some estimates assume that 95% offungi have not yet been described.

Growth and Reproduction. Fungal hyphaegrow at their tip (Fig. 6). Each part of themycelium is potentially able to grow, and a smallpiece of a mycelium can serve as an inoculumfor growth. There are two kinds of reproduction,asexual and sexual.

A sexual reproduction occurs by spore for-mation, bud formation, or fragmentation. Conid-iospores are formed on the tips of hyphae (coni-diophores), e.g., in the genera Aspergillus andPenicillium. If the spores are formed within spe-cial vessels, the sporangia, they are called spo-rangiospores, e.g., in the genera Mucor and Rhi-zopus. Bud formation is the mechanism of asex-ual reproduction among the yeasts.

Sexual reproduction involves mating of twonuclei (karyogamy), zygote formation, andmeiosis. It usually results in the formation ofspores. In the lower fungi the zygote is usuallytransformed to a durant organ which after meio-sis forms a sporangium. In the higher fungi, thezygote nucleus divides meiotically, and sporesare formed either within a sac (ascus) or by bud-ding on top of the basidia; the spores formedare called ascospores and basidiospores, respec-tively.

The taxonomic classification of fungi is cur-rently under debate and different suggestionscan be found. The subdivision presented hereinclosely follows a traditional classification undcontains the following classes: basidiomycetes,ascomycetes, zygomycetes, deuteromycetes,myxomycetes.

Basidiomycetes. In basidiomycetes, the zy-gote enlarges to form a club-shaped cell, thebasidium. The basidiospores are formed fromprojections of the basidium usually resulting infour spores on the tip. Most species are terres-trial fungi and live as saprophytes or parasites;many formmycorrhizas on trees. Nutrientmediaand methods have been developed to grow sev-

eral hundred species and strains of these higherfungi, which include many edible mushrooms,in submerged culture. Some could be of interestin biotechnology.

Figure 6.Mycelium, conidiophores, and fruiting bodies offungiA) Mycelium of fungal hyphae; B) Yeast dividing by bud-ding; C) Sporangium of Mucor mucedo containing sporan-giospores; D) Sporophore of Penicillium forming conid-iospores on the tips of branched hyphae; E) Sporophore ofAspergillus forming conidiospores on the tips of hyphae(sterigmata) located on the spherical end of the sporophorecell; F) Fruiting body (perithecium) of a typical ascomycetecontaining asci and ascospores; G) Basidium with four ba-sidiospores; H) fruiting body of a typical basidiomycete

Biotechnology 13

Ascomycetes. The ascomycetes are namedfor their formation of spores within a sac-likezygote, the ascus. With the exception of a fewunicellular forms they grow as branchedmyceliawith open crosswalls; they are coenocytic.Manyascomycetes grow on excrements of animals,i.e., they are coprophilic, whereas others are par-asites on plants or insects. Asexual reproductionoccurs by various kinds of conidiospores.

The orderSaccharomycetaleswithin theAs-comycetes comprises all yeasts. Typical yeasts,such as Saccharomyces cerevisiae, are unicellu-lar. They grow either as diplonts (with diploidnucleus) or as haplonts (with haploid nucleus),such as Schizosaccharomyces pombe. All yeastsare able to grow on sugar substrates, and a fewcan even grow on alkanes (Saccharomycopsis,Candida lipolytica) or on methanol (Candidaboidinii,Hansenula anomala). Yeasts are organ-isms often used in industrial microbiology.

Zygomycetes. The zygomycetes are lowerfungi and comprise unicellular and mycelialfungi; they have diverse nutritional habits.They grow in water, soil, or moist habitats.There are saprophytic and parasitic species.The most important group biotechnologicallyis that of the Zygomycetales, which includeMucor mucedo, Rhizopus nigricans, and Phy-comyces blakesleeanus. Several species are usedfor the production of vitamins, carotenoids, or-ganic acids, and enzymes.

Myxomycetes. The myxomycetes or slimemoulds grow on decaying plant tissue, formmultinuclear plasmodia, and produce fruitingbodies of yellow, red, or brown color. There isnot yet a consensus on the exact evolutionaryaffinities of myxomycetes, but these organismsconstitute a well-defined, homogeneous groupof approximately 900 species.

Deuteromycetes. If a fungus is incapableof sexual reproduction, it cannot be classi-fied with either the ascomycetes or basid-iomycetes, hence a third group of higher fungiwas created. There are many deuteromycetes ofbiotechnological importance: Aspergillus, Peni-cillium, and Cephalosporium. Investigations ofthe 18S rRNA revealed that the majority ofdeuteromycetes could be assigned to the basid-iomycetes and ascomycetes so that the group of

deuteromycetes will possibly be abandoned inthe future [27, 28].

3. Metabolism

The decisive factor for the development of a suc-cessful biotechnological production is an effi-cient biocatalyst. If this biocatalyst is amicrobialproduction strain, knowledgeof itsmetabolic ca-pabilities is very important. Such knowledge canlead to an increase in product concentration andproduct variety, because one catalyst can oftenbe used to produce different products [29].

Metabolism is generally concerned with allthe events occurring within a cell that not onlykeep the cell alive but are also manifested ingrowth [30]. All synthetic reactions leading togrowth are energy-consuming (endergonic) andare commonly referred to as anabolic reactions.In order to carry out these reactions, each indi-vidual synthesis must be coupled with energy-producing reactions of the catabolic sequence.Metabolism therefore consists of an intricatecoupling between anabolism and catabolism(Fig. 7), whereby energy transactions and trans-fer mechanisms are based upon the basic lawsof thermodynamics [31, 32].

Furthermore, in order to obtain a functionalbiosynthesis, the microbial cell must also pro-duce a reducing agent, because biosynthesis in-volves the reduction of small molecules of highoxidation state to larger molecules of lower ox-idation states. Both the energy and the reduc-ing agent can be obtained from any of a numberof diverse reactions depending upon the growthconditions and the genetic system available [33,34] within the metabolism. Energy is gener-ally dependent on adenosine triphosphate (ATP)and the reductant on nicotinamide dinucleotideNADH or NADPH.

The cell has a very sophisticated regulatorycontrol system that avoids oversynthesis of anychemical compound, thus securing its integrity.These regulatory control systems start at thecell membrane (substrate uptake) [35] and arepresent throughout the pathways of catabolismand anabolism.

14 Biotechnology

Figure 7. Anabolic and catabolic reactions in microbial metabolism

3.1. Microbial Systems Biology

In the future, systems biology will enable themetabolism of somemicroorganisms to bemod-eled and mapped in silico to such an ex-tent that physiological performance and effectscaused by environmental changes will be pre-dictable. The essential prerequisites include suf-ficient knowledge of metabolic material flowand network analysis, reaction kinetic analy-sis of biochemical networks (enzyme kinetics,global metabolism regulation), global analy-sis of gene regulation networks (signal trans-duction), analysis of populationwide systems(quorum-sensing networks), and the abstractionof fundamental regulation patterns. The firstsuch model that integrates signal transduction,gene expression and metabolism has alreadybeen introduced for the response of yeast to os-motic shock [36].

There is no doubt that such models signifygreat progress for microbiology and biotechnol-ogy: quantitative simulations and targeted inter-ventions in the metabolic network ideally openup the way to highly specialized, optimized pro-duction organisms. In order to optimize specifictypes of metabolic performance or to acceler-ate desired biosyntheses, industrial biotechnol-ogy is already relying to a great extent on themetabolic engineering ofmicroorganisms.Here,mention should be made of the synthesis of ami-no acids [37, 38], optimized riboflavin produc-

tion in B. subtilis [39], and, in the case of ter-penoids, yield increases by engineering meval-onate biosynthesis in E. coli [40]. It is likely thatknowledge from systems biology will triggertremendous progress in this application-orientedfield. Moreover, strategies based on systems bi-ologywill also be important for biotechnologicalprocess development where an understanding ofthe complex regulatory networks of productionorganisms will be indispensable for process op-timization [41].

3.2. Energy Production

Redox reactions are among the most importantchemical reactions in living organisms. Theseoxidation – reduction reactions are of special im-portance for energy production by living organ-isms:

∆G0=n F ∆E0

where ∆G0 is the standard free energy of thereaction, n is the number of electrons (or hydro-gen atoms) involved, F is the Faraday constant,and ∆E0 is the potential difference between thetwo redox systems. The simplest way to thinkabout these oxidation – reduction reactions is interms of electron donors and acceptors. Thus,the substrate oxidized has a specific redox po-tential that is at the lower end of the electrode

Biotechnology 15

potential scale, whereas the final electron accep-tor has a potential towards the more positive endof the scale. The difference between the two po-tentials corresponds to the energy obtainable ina chemical reaction between them. If the poten-tials of the substrate (electron donor) and of thefinal electron acceptor are known, one can easilydetermine the energy production in a microbialsystem (Fig. 8) [42, 43].

Figure 8.Redox potentials of various energy-producingmi-crobial reactions

Because the potential difference betweenelectron donor and acceptor can be significant,the energy released could produce so much heatthat the cell would be damaged. In order to avoidsuch cell damage, the cell has an energy-release-controlling cascade system, which makes cer-tain that energy is released only stepwise. Thiscascade system is referred to as the respiratorychain or the electron transport chain. It is locatedin the cellular membrane and is responsible forthe production of energy by electron transfer in-volving an enzyme called ATPase (which formsor hydrolyzes ATP) [35, 44]. The ATPase alsoplays an important role for the transportation ofsubstances across the membrane into the cell.

According to the chemiosmotic hypothesisfor ATP production, the electron transfer chainsare coupled to ATP synthesis by a proton elec-

trochemical gradient (∆µ+H) across the energy-

transducing membrane. Proton electrochemicalpotential is a thermodynamic measure of theextent to which the proton gradient across themembrane deviates from equilibrium. This hy-pothesis is based on two separate proton pumps,whereby the∆µ+

H is generated by electron trans-fer and is used to drive the second pump, in-volving the enzyme ATPase, backward in thedirection of ATP synthesis. The final result ofsubstrate oxidation is therefore the generationacross the cell membrane of gradients of bothpH (∆pH) and electrical potential (∆E). Bothgradients exert a force on the protons extrudedby the respiratory chain, tending to pull themback across the membrane. This proton motiveforce, ∆µ+

H, is the key element:

∆µ+H=∆E−2.303RT/F ·∆pH

The numerical value of 2.303 RT/F is 59 mV at25 ◦C.

3.3. Substrate Transport

Substrates undergoing catabolic reactions mustenter the cell before catalysis can occur, becausemost catalytic enzymes are inside the cell. Thecell membrane is a barrier for most ions andother molecules. For an ion to be transportedacross amembrane both a pathway and a drivingforce are required.Driving forces can be concen-tration gradients, electrical potentials, metabolicenergy, or a combination of these. Four pro-cesses are known [45, 46]:

1) In simple diffusion, solutes move passivelyacross the cell membrane, depending on theconcentration gradient and thermal motion ofthe molecules (e.g., water, gases, low molec-ular hydrophilic compounds, organic acids inthe protonated form).

2) In facilitated diffusion, solutes require a car-rier for their transport in addition to the con-centration gradient and thermal motion of themolecules. In both simple and facilitated dif-fusion, no metabolic energy or proton motiveforce is required. The energy for the transportstems from existing concentration gradients.

3) The term active transport is applied to a tightcoupling of transport to metabolism in the ionpumps, which are central to chemiosmoticenergy transduction. There are at least two

16 Biotechnology

distinct classes of active transport systems:the membrane-bound and the binding proteintransport systems.

4) The group-translocation transport systemcomprises processes in which the passageof the substrate across the membrane occurssimultaneously with, and as a consequenceof, chemical transformation of the sub-strate (e.g., phosphoenolpyruvate–glucose–phosphotransferase systemwith most anaero-bic and facultatively anaerobic bacteria). Ac-tive transport and group-translocation trans-port are often referred to as “primary trans-port”, simple and facilitated diffusion as “sec-ondary transport”. The carrier systems in-volved can be of the “uniport”, “symport”, or“antiport” type.Many bacteria pump natrium ions out of thecell by taking in twoprotons per natrium ion inan antiport system. Sulfate ions, on the otherhand, are only taken in by some transportsystems if protons are simultaneously “sym-ported”.

3.4. Catabolism

3.4.1. Photosynthesis

Photosynthesis creates living matter out of inor-ganic material, replenishes the reservoir of oxy-gen in the atmosphere in the cases of algae andplants, stores the energy of sunlight to supportthe life activities of organisms, and removes car-bon dioxide:

CO2+H2A+light→ (CH2O)+2 A+chemical energy

If A is taken to be oxygen the process is plant(or algae or cyanobacteria) photosynthesis; if itrepresents a sulfur atom the process is bacterialphotosynthesis [43].

The process of photosynthetic energy conver-sion is initiated when photons are absorbed byspecificmolecules, such as chlorophyll. This ab-sorption leads to an ejection of electrons, whichare accepted by the compound having the low-est redox potential in animate nature, namelyferredoxin. The electrons then move through theelectron transport system, and the energy is fi-nally converted to ATP. Because the electronsreturn to the reaction center (Fig. 9), this sys-tem is called cyclic photophosphorylation. The

reducing agent is produced separately. In plantsand algae, a secondphotosystemexists that splitswater into H+ and oxygen, whereby the hydro-gen ions reduce NADP+ and oxygen is set free.In bacteria, however, such a second photosystemdoes not exist, and inorganic compounds such asthiosulfate are oxidized, with the electrons run-ning through the electron transport system to thereaction center and the proton via ferredoxin toNAD+ (Fig. 9). This is the reasonwhyplants andalgae possess mainly cyclic and bacteria non-cyclic photophosphorylation.

3.4.2. Chemosynthesis

In chemosynthesis, all energy and reducingagents are obtained by catalytic reactions of or-ganic or inorganic compounds. In the formercase, one refers to chemoorganotrophy and inthe latter to chemolithotrophy. In addition, threedifferent energy modes, which depend upon theelectrondonors and acceptors, are distinguished:aerobic respiration, anaerobic respiration, andfermentation [47].

Organic substances are used as an energysource by the vast majority of microorganismsthat live in natural environments (Fig. 10). Na-ture provides them with an abundance of largeorganic polymers. A polymeric substance con-sists of series of monomeric units linked to-gether. These monomeric units can be of thesame kind (e.g., cellulose, starch) or of differentkinds (e.g., pectin, lignin, sucrose). Within thepolymeric structure, the monomeric units canbe linked in a straight chain with identical link-ages or different linkages or in branched chains;moreover, they can be linked in a specific se-quence or at random. The structure of each of thepolymer substrates for microorganisms is veryimportant, because the enzymes produced by themicroorganisms are specific not only to the typesof monomers, but also in most cases to the wayin which the monomers are linked.

Since microorganisms can only take upmonomers, all enzymes involved in the break-down of polymeric substances must be extra-cellular. These enzymes are either membrane-bound or released into the medium. In gen-eral terms, Gram-negative bacteria accommo-date these enzymes in the periplasmic space bet-ween the cell membrane and the cell wall and

Biotechnology 17

Figure 9. Schematic representation of cyclic and noncyclic photophosphorylation in nature

Figure 10. Schematic representation of aerobic catabolism in microorganisms

the enzymes are thusmembrane-bound,whereasGram-positive bacteria and fungi have a ten-dency to release these enzymes into themedium.This is one of the reasons why extracellular en-zyme studies are carried out predominantly withGram-positivemicroorganisms, aswell as yeastsand other fungi.

3.4.3. Carbohydrate Metabolism

Most renewable resources are carbohydrates.The principal carbohydrate that serves as a car-bon and energy source for microorganisms is

glucose. However, not all microorganisms fol-low the same route of glucose utilization. Thereare at least four major pathways of glucosemetabolism, ofwhich three lead directly to pyru-vate:

1) Embden-Meyerhoff (EM) pathway, often re-ferred to as the glycolytic or fructose bispho-sphate pathway

2) Hexosemonophosphate (HMP) pathway, of-ten referred to as the pentose shunt or pentosephosphate pathway

3) Entner-Doudoroff (ED) pathway

18 Biotechnology

4) Phosphoketolase (PK) pathway, almost en-tirely specific for heterofermentative lacticacid bacteria

All four pathways of glucose metabolismhave a great number of intermediates and en-zymes in common.The details of these pathwayscan be found in many textbooks [32, 34, 48].

The EM pathway, for example, provides thegreatest amount of ATP, but it does not produceribose-5-phosphate, the important precursor forRNAandDNAbiosynthesis; nor does it produceerythrose-4-phosphate, which is important foramino acid biosynthesis. Microorganisms thatare capable of using only the EM pathway forglucose utilization are therefore not able to growon simple media with glucose as the sole carbonsource. They require growth factors or organiccompounds (e.g., yeast extract) for growth andare referred to as fastidious microorganisms.

In contrast, the HMP pathway produces allthe precursors necessary for bothRNAandDNAas well as for aromatic amino acid biosynthe-sis, but it produces only half the amount of ATPenergy. This pathway does not produce pyru-vate directly. The microorganisms must there-fore possess part of the enzymes of the EMpath-way. It is therefore not surprising that both path-ways, EM andHMP, are common combinations,particularly in facultative anaerobicmicroorgan-isms.

The ED pathway is unique, however, andhas so far only been found in bacteria. Al-though this pathway is linked partly to the HMPpathway in the reverse direction for precur-sor formation, pyruvate is formed directly be-cause of the aldolase cleavage of 3-ketodeoxy-6-phosphogluconate. The ED pathway can existon its own and is used by the majority of strictlyaerobic microorganisms. The net result is simi-lar to the HMP pathway, although one mole ofATP can be formed only if the carbon atoms gointo pyruvate instead of precursor.

3.4.4. Aerobic Processes

All aerobic microorganisms use the tricarboxyl-ic acid (TCA) cycle as a main metabolic path-way. In this cycle, appropriate groups of en-zymes catalyze a series of consecutive transfor-mations, including oxidations, which finally re-sult in the complete oxidation of pyruvate to car-

bon dioxide. The electrons and hydrogen atomsremoved from the individual organic compoundsare accepted by oxygen and form the other twoend products, ATP and water. The tricarboxyl-ic acid cycle, however, is not only an energy-producing cycle, but it is also responsible forthe production of precursors for amino acid andantibiotic biosyntheses [49, 50]: 2-ketoglutarateand oxalacetate ions. The tricarboxylic acid cy-cle serves, therefore, as the central pathway forthe production of energy as well as for anabolicprecursors.

Under ideal growth conditions, the interme-diates of the tricarboxylic acid cycle would becontinuously withdrawn for anabolic reactions.Then, the formation of oxalacetate at the end ofthe cycle became vulnerable. Because oxalac-etate is required not only as a precursor for an-abolic amino acid biosynthesis, but also for thecondensationwith acetyl-CoA to keep the tricar-boxylic acid cycle alive, the organismmust havesome safety device to ensure that oxalacetateis always available; otherwise the tricarboxyl-ic acid cycle would be interrupted. The organ-ism possesses a very fine control system for en-ergy production and biosynthetic requirementsin formof replenishment sequence reactions.Al-together, the microbial world has five enzymesat its disposal for such sequences, of whichphosphoenolpyruvate carboxylase is probablythe dominant: it catalyzes the formation of ox-alacetate from phosphoenolpyruvate and carbondioxide. Phosphoenolpyruvate, in turn, is the in-termediate fromwhich pyruvate is formed in theEM pathway.

This double function of the tricarboxylic acidcycle as an energy-generating cycle as well as abiosynthetic precursor-supplying cycle is veryoften referred to as amphibolic. This dual func-tion, of course, must be controlled. There ex-ists one enzyme in the tricarboxylic acid cycle,isocitrate dehydrogenase (it catalyzes the reac-tion: isocitrate → oxalosuccinate), which per-forms this regulatory control. In the case of anoverproduction of ATP this enzyme is inhibitedby ATP, preventing any further oxidation andATP synthesis, until the biosynthetic steps lead-ing to RNA, DNA, aromatic amino acids, andfatty acids have caught up and utilized the ex-cess ATP [33].

The general scheme in Figure 11 outlines theintricate interconnections between catabolism

Biotechnology 19

Figure 11. General scheme of interconnections between catabolism and anabolism (cell biosynthesis)©P = Phosphate

20 Biotechnology

and anabolismamongall those precursors neces-sary for cellular biosynthesis. Aerobic processesin biotechnology are used to produce variousproducts, for example, antibiotics, amino acids,and organic acids [29, 51, 52].

3.4.5. Fats and Fatty Acid Metabolism

Fats are another major group of naturally occur-ring compounds (see Fig. 10). The majority offats are triglycerides, that is, fatty acids (R1, R2,R3) esterified with glycerol:

These fats have to be converted into themonomeric components, the fatty acids andglycerol. This hydrolysis is carried out by en-zymes called lipases. The glycerol componentis converted to dihydroxyacetone phosphate,which participates in the EM pathway. The fattyacids, which vary in their carbon chain length,have to be activated by the formation of theirrespective coenzyme A (CoA) esters; this reac-tion step requires energy. These fatty-acid-CoAesters subsequently undergo cyclic oxidation,eliminating an acetyl unit at each turn. This typeof oxidation is referred to as β-oxidation. Theacetyl-CoA formed after each cycle can then en-ter the tricarboxylic acid cycle for the formationof energy and biosynthetic precursors.

However, this type of metabolism leadingto acetyl-CoA does not produce the importantribose-5-phosphate and erythrose-4-phosphate.In order to obtain these precursors, all organismswhose metabolism leads directly into the tricar-boxylic acid cycle via acetyl-CoA and not viapyruvate have to build up the carbon chain men-tioned above and have introduced two pathwaysfor this biosynthetic purpose:

1) The glyoxylate cycle. As mentioned earlier,overproduction of ATP inhibits the enzymeisocitrate dehydrogenase. The organism isthen capable of inducing two new enzymes,isocitrate lyase and malate synthase, to cir-cumvent the carbon dioxide-producing stepsof the tricarboxylic acid cycle. This new short-cut TCA cycle leads to succinate and via gly-oxylate to malate, and oxalacetate is formed

despite the inhibition of isocitrate dehydro-genase (see Fig. 10).

2) The gluconeogenic pathways. From the ox-alacetate formed, the organism is using one ofthe earliermentioned replenishment sequencereactions to produce phosphoenol-pyruvateand reverses the EM pathway to glucose-6-phosphate; from there ribose-5-phosphate anderythrose-4-phosphate can be formed via theforward HMP pathway.

A great number of enzymes are com-mon to both carbohydrate and fatty acidmetabolism,which shows the economyof bacte-rial metabolism. The principal differences are inthe control systems, which are outlined in detailin the literature, for example, [32, 34].

3.4.6. Hydrocarbon Metabolism

The metabolism of hydrocarbons has been ex-amined in detail in the framework of environ-mental biotechnology in connection with clean-up of contaminated sites. Aliphatic hydrocar-bons are good substrates for a large numberof microorganisms. The straight-chain alkanesare more readily attacked than substituted orbranched-chain alkanes. The attack occurs at ei-ther one end (monoterminal) or both ends (diter-minal). The alkane is converted via the primaryalcohol into the corresponding fatty acid andthen via β-oxidation into acetyl-CoA as out-lined previously. Methyl group oxidation incor-porates one atom of oxygen; this reaction is cat-alyzed by a mixed oxidase system consisting ofthree proteins: rubredoxin, NADH-rubredoxinreductase, and -hydroxylase. For the oxidationof methylene groups, rubredoxin is replaced bycytochrome P450 and -hydroxylase by methy-lene hydroxylase [53, 54].

Aromatic hydrocarbon utilization follows auniform biochemical concept. The great major-ity of aromatic hydrocarbons, irrespective of thenumber of benzene rings, converge in their ox-idativemetabolism to threemajor intermediates:catechol, 3,4-dihydroxybenzoic acid (protocat-echuic acid), and 2,5-dihydroxybenzoic acid(gentisic acid):

Biotechnology 21

The benzene nucleus in these intermediatesis cleaved either between adjacent hydroxylgroups (ortho cleavage) or between a hydroxyland a carboxyl group (meta cleavage). In bothcases, the active incorporation of an oxygenmolecule is required. Ortho cleavage, via the 2-ketoadipate, andmeta cleavage, via the keto acidpathway, lead into the tricarboxylic acid cycle atthe level of acetyl-CoA, succinate, or malate andfumarate.As in fatty acidmetabolism, the organ-isms use the tricarboxylic acid cycle for energyproduction and require the glyoxylate cycle to-gether with gluconeogenesis to obtain pentosefor RNA and DNA precursors.

3.4.7. Amino Acid Metabolism

The aerobic utilization of amino acids followsa pattern that is very similar to the metabolismof other carbon sources [29, 49]. The only dif-ference is the -amino group. In most cases, theamino acid is first converted into the correspond-ing keto acid by either cytochrome-linked oxi-dases, transaminases, or NAD(P)-linked dehy-drogenases. The final product of the metabolismalways leads to the intermediates of the tricar-boxylic acid cycle. This is one of the reasonswhy -amino acids are able to function as thesole source of carbon, nitrogen, and energy formany microorganisms. As in fatty acid and hy-drocarbon metabolism, microorganisms grow-ing on amino acids must possess the glyoxylatecycle and the gluconeogenesis pathway.

3.4.8. Anaerobic Metabolic Processes

The breakdown, or catabolism, of organic com-pounds in the absence of oxygen is generally re-ferred to as fermentation. The microorganismsthat carry out fermentations are either faculta-tive or obligate anaerobes. The most character-istic difference between respiratory and fermen-tative metabolism is in ATP energy production.Because no electron transport occurs, redox re-actions of organic compounds play a major role.

The number of cells obtained per mole of sub-strate in simple defined media is much smallerthan under aerobic conditions. In addition to thecell material, large amounts of organic end prod-ucts are formed, mainly primary alcohols, e.g.,ethanol or butanol.

Fermentations are normally classified ac-cording to their principal fermentation products.Figure 12 summarizes the end products fromcarbohydrates.

The distinction between aerobic and anaer-obic carbohydrate metabolism is made at thepyruvate level, because in anaerobic fermenta-tion the organism has to substitute for the tricar-boxylic acid cycle. In general the organisms arenot able to produce the intermediates that serveas precursors for amino acid and protein biosyn-thesis. Therefore, fermentation processes cannotbe carried out on simple defined media, but al-ways require complex media if the organism isto grow and maintain its metabolism. Most ofthese fermentation processes are rather complexand details may be obtained from the relevantliterature [32, 34, 55].

3.4.9. Single-Carbon-CompoundMetabolism

A few isolated genera of microorganisms arecapable of oxidizing single-carbon compounds.Microorganisms are calledmethylotrophs if theyhave the ability to derive both carbon and en-ergy from the metabolism of methanol, methan-otrophs if they can do the same with methane[53, 56 – 58]. Methane and methanol were themost common substrates used in the productionof single-cell protein. The oxidation of methaneleads tomethanol and from there to carbon diox-ide.

The oxidation of methane or methanol to car-bon dioxide, however, does not provide the or-ganism with any precursors for biosynthesis.These organisms therefore possess an unusualcarbon assimilatory pathway. Depending uponthe enzymic assemblage, formaldehyde is incor-porated into ribulose-5-phosphate or into the ser-ine pathway in order to build up the carbon chainfor the biosynthesis of RNA, DNA, and all theamino acids required for protein biosynthesis.The absence of a tricarboxylic acid cycle, thepresence of anaplerotic sequence reactions, and

22 Biotechnology

Figure 12. Fermentation end products of carbohydrate metabolism

the glyoxylate cycle demonstrates the biosyn-thetic capability of these pathways.

3.4.10. Inorganic Metabolism

Some microorganisms are capable of using in-organic compounds as an energy source in aer-obic metabolism or as a final electron accep-tor in anaerobic metabolism (anaerobic respira-tion). Both of these are combined in nature inthe sulfur cycle and the nitrogen cycle, [32, 34].

Sulfur Cycle. Under anaerobic conditions,sulfate or thiosulfate can be reduced to sulfide bysulfate-reducing bacteria. Such processes occurmainly inwater-logged soils or stationary estuar-ies rich in organic matter, and can cause seriouscorrosion of iron and steel pipes in the soil. Hy-drogen sulfide is an extremely toxic product. Ifthe soil or estuaries are supplied with oxygen,hydrogen sulfide can be reoxidized to sulfateby aerobic sulfur-oxidizing microorganisms, forexample, thiobacilli. The reoxidation of sulfur orsulfide to sulfate is amicrobial process of consid-erable biotechnological significance. It not onlyacidifies the soils and thus makes plant growthpossible, but the sulfuric acid formed can alsobe used for ore leaching processes. Sulfuric acidcan form soluble sulfates with a wide range ofmetals and by leaching old mining dumps ithelps concentrate these for further utilization of

the ore. Such ore leaching processes have led toa vastly improvedmining industry and increasedthe economy of ore mining.

Nitrogen Cycle. Nitrogen is the most abun-dant gas in the atmosphere. Nitrogen-fixingorganisms are capable of converting nitro-gen into nitrogen compounds that are accept-able to plants. Nitrogen-fixing microorganismscan be divided into free-living (cyanobacteria,Clostridium, Azotobacter) and symbiotic (Rhi-zobium). The last group in particular may be-come of great biotechnological importance, be-cause they live in association with legume plantsand provide these with the necessary nitrogensource even in nitrogen-starved soils, thus elim-inating the need for large amounts of nitrogenfertilizers. The nitrogen-fixing organisms con-vert nitrogen to ammonia, which then can be ox-idized by chemolithotrophs (Nitrosomonas andNitrobacter) via nitrite to nitrate. This conver-sion of nitrogen to nitrate is referred to as ni-trification. The reverse reaction, called denitri-fication, is an anaerobic process in which thenitrate or nitrite serves as electron acceptor. Theproduct of denitrification is nitrogen gas. Nitrifi-cation and denitrification are important processsteps in biological wastewater cleaning.

The chemolithotrophs, however, face an en-ergy problem. The redox potential of the inor-ganic compounds used as electron donors is well

Biotechnology 23

above that of the NAD/NADH couple that is re-quired as reducing agent for biosynthesis. Sincethe electrons can only enter the redox systemat the cytochrome level, electron transport mustbe reversed to obtain NADH. In photosynthesis,this reversal can be carried out by the reactioncenter of the photosynthetic apparatus, but inchemosynthesis the reversal has to occur withenergy input from the oxidation of the inorganicsource. It is therefore not surprising that suchorganisms grow much better in the presence oforganic material, which they are able to assimi-late directly, thus obviating the reductive biosyn-thesis and consequently the need for reductantformation.

3.5. Biosynthesis

The microbial cell consists of five major typesof macromolecules: proteins, polysaccharides,lipids, RNA, and DNA [32, 34]. These macro-molecules are built up from relatively fewmonomers, which the cell has to provide orto synthesize: amino acids, sugar phosphates,fatty acids, ribonucleotides, and deoxyribonu-cleotides.

Microorganisms are divided into two groups:autotrophs, which are able to produce alltheir cellular requirements from carbon diox-ide as the sole carbon source, and heterotrophs– the majority of microorganisms –which pro-duce the cellular material from organic com-pounds. Photo- and chemolithotrophs use ATPand the reducing power produced by photo-synthesis and/or oxidation of inorganic sub-strates to reduce carbon dioxide to cellular ma-terial. The pathway for carbon dioxide reduc-tion is referred to as the Benson-Calvin cy-cle. The actual carbon dioxide fixation reac-tion is catalyzed by ribulose-1,5-bisphosphatecarboxylase; the primary product of this re-action is 3-phosphoglycerate, which leads toglyceraldehyde-3-phosphate, a member of theEM pathway:

3 CO2+9ATP+6NADH2

→glyceraldehyde−3−phosphate

→+9ADP+8 Pi+6NAD+

From glyceraldehyde-3-phosphate, these organ-isms are capable of producing the important

pentoses, trioses, and the dicarboxylic acidsrequired for macromolecular biosynthesis [46,47].

3.5.1. Amino Acids

About twenty -amino acids are required for thebiosynthesis of the numerous types of proteinsthat provide the catalytic capability of the mi-croorganisms. These amino acids are producedfrom the following precursors:Erythrose-4-phosphate → tyrosine, tryptophanPhosphoenolpyruvate → phenylalanineRibose-5-phosphate → histidine3-Phosphoglycerate → serine, glycine, cysteinePyruvate → alanine, valine, leucine2-Oxoglutarate → glutamic acid, glutamine,

arginine, prolineOxalacetate → aspartic acid, asparagine,

methionine, lysine, threonine,isoleucine

Themost important amino acids are glutamicacid and glutamine, which figure in transami-nation reactions during biosynthesis. Therefore,the availability of these two amino acids is vitalfor protein biosynthesis. The detailed pathwayscan be found in the literature [32, 34, 46].

Theoretically, each of these -amino acids canbe produced commercially by microorganisms,some are already produced, but some practicalproblems remain to be solved for some of them[49].

3.5.2. Lipids

Most of the fatty acids that occur in lipids contain16 or 18 carbon atoms and are saturated or unsat-urated with one or more double bonds. Acetyl-CoA is the only precursor for the biosynthesis ofall fatty acids. In order to differentiate betweenfatty acid catabolism and fatty acid biosynthe-sis, the organisms employ CoA activation in theformer and an acyl carrier protein (ACP) in thelatter. The C2 units are added to the acetyl-ACPin the form of malonyl-ACP. Once the requiredchain length is reached, ACP is hydrolyzed andthe appropriate fatty acid is formed. Unsaturatedfatty acids are generally formed by dehydrationof a hydroxy fatty acid.

The fatty acids are then esterified with glyc-erol, which is readily available from dihydroxy-acetone phosphate, a member of the EM path-

24 Biotechnology

way. Finally, triglycerides are formed. The intro-duction of a phosphate group gives a phospha-tidic acid and a whole range of phospholipids.If the phosphate group in the phosphatidic acidis replaced by a carbohydrate, glycolipids areproduced [32, 34, 46].

3.5.3. RNA and DNA

Ribonucleotides consist of a purine or pyrim-idine derivative, ribose, and phosphate groups.A purine or pyrimidine base attached to a ri-bose is referred to as a ribonucleoside; if a phos-phate group is attached to this ribonucleosideit is called a ribonucleotide. The most impor-tant pyrimidine derivatives are uracil, cytosine,and thymine. The most important purine deriva-tives are adenine and guanine. The biosynthesisof pyrimidines and purines starts at the ribose-5-phosphate level, which also represents the ribosein the nucleotide. The RNA consists of ribonu-cleotides. Reduction of the ribonucleotides leadsto deoxyribonucleotides, the building blocks forDNA biosynthesis [44, 46].

3.6. Regulation

The microbial cell possesses a complex ofcatabolic and anabolic capabilities with manyinterconnected pathways. The cell is required tomaintain balance among the various parts of thisextremely complex network of reactions [32, 34,44, 46, 48]. The cell therefore has developedvery intricate and refined devices to streamlineits economy.

Regulation can either be manifested in theenzyme synthesis or in the enzyme activity. In-duction and repression of enzyme synthesis oc-curs at the genetic code, whereas feedback in-hibition simply regulates the activity of the en-zyme.The importance for biotechnology is to re-alize that the first type of regulation is concernedwith substrate, catabolite, and end product in-hibition, whereas the second mainly involves atransitional end product inhibition.

More than one enzyme usually is re-quired to channel a substrate into intermediarymetabolism. The substrate can therefore be re-sponsible for a coordinate or sequential induc-tion of enzymes. This activity of the cell nor-mally occurs during the lag phase of growth and

can be reduced to almost zero by preculture con-ditions.

A much more complicated regulation iscatabolite repression, which is mainly mani-fested in the so-called diauxie phenomenon. Thelatter is biphasic growth in the presence of morethan one carbon source in the medium. In thisreaction the organism takes up only one sub-strate at a time and the presence of this partic-ular substrate represses the enzyme system thatis responsible for the metabolism of the othersubstrate. This repression occurs at the operonlevel and occurs as soon as the concentration ofthe first substrate becomes very small.

Enzyme synthesis can also be inhibited byend products within a short period of time. Forexample, if the medium contains any α-aminoacid, the organism would use this amino aciddirectly instead of producing it. The added ami-no acid therefore represses the synthesis of eachenzyme in its synthesis pathway.

In addition to the regulation of enzyme syn-thesis, the cell must also adjust the activityof enzymes to its metabolic requirements. If amonomer is synthesized in larger amounts thanneeded for polymer synthesis, it is not necessaryto stop the synthesis of the enzyme completely,but it suffices to reduce the activity of that partic-ular enzyme. This fast control action is referredto as feedback inhibition. The target enzymesfor feedback inhibition are called allosteric en-zymes.

4. Metabolic Engineering

The term metabolic engineering refers to thegenetic optimization of prokaryotic or eukary-otic cells that are used in industrial fermentationprocesses. In these processes, they are utilizedfor the economically production of proteins orother fine chemicals. Bailey [59] firstly intro-duced the definition of metabolic engineeringin 1991 as “the improvement of cellular activi-ties bymanipulationof enzymatic, transport, andregulatory functions of the cell with the use ofrecombinant DNA technology.” However, oneof the early examples of successful genetic en-gineering of a living organism for productionpurposes, namely the production of human in-sulin in E. coli cells, had already been described

Biotechnology 25

quite some time before a name was given to thisnew direction of science.

According to Cameron and Tong [60], thereare five categories of metabolic engineering thatare grouped with respect to the objective of thegenetic modification:

1) Improved production of chemicals alreadyproduced by the host organism.

2) Extended substrate range for growth andproduct formation.

3) Introduction of new catabolic activities for thedegradation of toxic materials.

4) Production of chemicals that are new to thehost organism.

5) Modification of cell properties, for instance,to make themmore resistant to the productionconditions.

The process of metabolic engineering is mostoften described by a circle of elementary stepsthat have to be performed once or several times.This circular process is demonstrated in Figure13.

Figure 13. Schematic illustration of the elementary pro-cesses of metabolic engineering

The elementary processes are

– Analysis of the organisms with respect to theProteome and/or metabolome

– Design of a genetic modification that im-proves/allows the synthesis of the compoundof interest

– Production of a modified strain– Fermentation and production

Depending on the task at hand, one entersthe engineering cycle at different entry points.

When, for instance, an organism should bemod-ified to express a recombinant protein, the firststep is to design and create the DNA sequencethat codes for the protein in an appropriate vec-tor system. Thus, one will enter the cycle at thedesign level. The same is true when an existingmetabolic pathway should be extended in theway that the original end product of that path-way is metabolized to a compound of interest.However, when an existing metabolic pathwayshall be used to produce a given compound inhigh yields, one will have to start by analyz-ing the metabolic situation and the metabolicfluxes in order to identify targets for genetic en-gineering. In this case, one will enter the cycleat the analysis level. In particular in the lattercase, it is often necessary to go through the cy-cle of metabolic engineering more than once,since complex metabolic networks within livingcells often require several genetic modificationsto provide the compound of interest in improvedquantities.

Metabolic engineering is to a high degree de-pendent on novel developments and improve-ments in chemical analytics as well as molecu-lar biology. In both disciplines, the last decadehas brought about powerful new technologies– such as protein and DNA chip technologyto mention only two – that provided consider-able progress in metabolic engineering. For fur-ther information, the interested reader is re-ferred to the reviews by Nielsen [61] and Oster-gaard [62] and thewebsite of a scientific journalthat is exclusively devoted to this research area(www.apnet.com/mbe).

The following paragraphs will discuss inmore detail the three elementary processes ofmetabolic engineering, analysis, design, andproduction of a modified strain. Fermentationwill be addressed in other chapters of this series.(→ Ethanol, Chap. 5, → Antibiotics, Chap. 5)

4.1. Analysis of the Transcriptome,Proteome, and Metabolome

The bottleneck of all of the traditional methodsof transcriptome and proteome characterizationis the limitation to an analysis of just a few sam-ples at a time. While conventional methods areconfining themselves to the examination of sin-gle genes, a DNA or protein chip experiment

26 Biotechnology

delivers a complete gene or protein expressionpatternof the cell. Thehighdegreeof paralleliza-tion realized in biochips is the great advantageover classic molecular biological approaches.

4.1.1. Gene Expression Analysis using DNAMicroarrays

In the past few years, the complete DNA se-quences of a number of different microorgan-isms have been determined and can be ex-ploited to optimize strains as well as recom-binant protein production. Strain optimizationinvolves measurement of genomewide mRNAlevels in wild-type and mutant strains usingDNA microarrays (Fig. 14) Furthermore, mi-croarray analysis canhelp identifyingpreviouslyunknown genes required for recombinant pro-tein production.

Bioprocess optimization using microarraysentails metabolic control analysis, modeling,and molecular biology to create new mutantsand strains with an, for example, optimized pro-tein production rate. Recombinant protein ex-pression exerts a metabolic burden on the hostcell, whereby stress response mechanisms aretriggered on different levels. Microarrays allowthe investigation on a genome scale, which en-ables the qualitative and quantitative character-ization of the burden on host cell metabolism.Thereby, a better understanding of the impact ofrecombinant protein production can be achievedby using the generated “snapshot” of the actualcellular composition and activity. Additionally,the knowledge of the interaction of host cellmetabolism with recombinant protein produc-tion is improved and contributes to process op-timization.

Chip Technology. Alongside metabolomeanalysis, analysis of various components of theproteome, the transcriptome or the genome willbecome increasingly valuable. New biosensorsknown as biochips will be required. DNA chiptechnology has already opened up new waysof studying disease in more depth and identi-fying far more possible targets. A DNA chipis an array of synthetic DNA sequences repre-senting different genes. Up to now, membraneand immune biosensors are used to analyze sub-stances of the metabolome. One important goalin chemical and biological sensing is the sen-

sitive detection and selective identification ofbiochemical discrete compounds (carcinogens,metabolites, proteins, etc.) or living systems(bacteria, viruses, or related components) at lowlevels in complex biological matrices, tissues,and blood. In the future, it will be indispensableto analyze compounds of the genome and theproteome. Therefore, new formats of biosen-sors, so called biochips, have to be developed.Such biochips or microarrays can be used foridentifying genes as well as changes in theiractivity. Furthermore, these biochips allow thesimultaneous detection of several disease end-points using different bioreceptors such asDNA,antibodies, enzymes, or cellular probes.

The high parallelization degree of biochipsis a great advantage over classic molecular bi-ological methods. While conventional methodsare confining themselves to the examination ofsingle genes, a DNA chip experiment deliversa complete gene expressions pattern of the cell.DNA microarrays can be used for the purposeof monitoring expression levels of thousandsof genes simultaneously. Furthermore, biochipscan solidly simplify and accelerate a number oflong and expensive diagnostic methods. Addi-tionally, gene expression analysis across variousbiological conditions, cell cycle states, tissues,and subjects may help identify differentially ex-pressed genes. This type of information is a valu-able pinpoint in the investigation of biologicalprocesses and functional disorders. Recapitulat-ing, DNA chips provide a format to profile com-plex diseases and discover novel disease-relatedgenes. Applications for this technology includegene expressionmonitoring,mutation detection,metabolic engineering, drug development, tai-lor made therapeutics, SNP research, GMO de-tection, and high-throughput screening, amongothers. They have a profound impact on biologi-cal research, industrial production,medicine andpharmacology andwill be used as the biosensorsof the future [63 – 67]. For the understanding ofbiological systems with up to 30 000 genes, themeasurement of RNA levels for a complete setof transcripts of an organism will be necessary.The use of glass slides as medium enables highspot densities onmicroarrays (up to 10 000 spotsper slide). ADNA chip experiment works by hy-bridizing all of the gene probes on the chip withthe nucleic acid target to be tested. cDNA la-beled with fluorescent tags is used, produced by

Biotechnology 27

Figure 14. Interaction of labelled target molecules with probe molecules on a glass array

reverse transcription of RNA from the sampleto be analyzed. Hybridizing a sample of nucleicacid with many complementary gene probes inparallel on a DNA chip produces a hybridiza-tion pattern with corresponding hybridizationintensity. DNA chip technology therefore en-ables large numbers of genes to be screened si-multaneously, giving a comprehensive, detailedpicture of changes in gene expression, sheddinglight on complex regulatory interactions.

4.1.2. Fabrication of DNA microarrays

There are three primary technologies usedpresently in automated microarray fabricationincluding photolithography, ink-jetting, contactprinting, and derivatives thereof. Each of thesetechnologies has specific advantages and dis-advantages in microarray manufacturing. Thephotolithographic approach relies on the in situsynthesis of 25mer oligonucleotides using pho-tomasks. This means that each probe is indi-vidually synthesized on the chip surface. Pho-tolithographywas developed by Fodor et.al. [68]and commercialized by Affymetrix (Fig. 15).In contrast, the ink-jetting and contact printingmethods attach presynthesized DNA probes tothe chip surface. While the in situ probe syn-thesis necessitate expensive and sophisticatedequipment the contact and noncontact spotting

methods made DNA chips affordable for aca-demic research laboratories. Since 1996, manyDNA arrayers have become available and theself spotted glass slide DNA arrays are today themost popular format for gene expression profil-ing experiments.

Figure 15.Microarray fabrication using an Affymetrix 427arrayer

4.1.3. Proteome Analysis using ProteinMicroarrays

The utilization of protein biochips for the Pro-teome analysis offers a number of alternativesand advantages in metabolic engineering. Pro-tein microarrays make use of technological in-novations and enable the proteome analysis in

28 Biotechnology

miniaturized test formats, thereby promisingsignificant advantages such as an improved an-alytical speed, a better separation efficiency, re-duced sample/reagent consumption, contamina-tion reduction and reduced costs. Protein mi-croarrays consist of boundantigens or antibodiesas capture molecules for detecting proteins in acomplex mixture. They provide a huge potentialfor monitoring protein expression and proteinprofiling.

Although the basic construction of such pro-tein microarrays is similar to DNAmicroarrays,themore delicate nature of protein structures hashindered the development of such devices for theanalysis of proteins for a long time. This is dueto their more complex coupling chemistry, theinstability of the immobilized protein and thefar weaker detection signals. In principal, mi-croarray technology enables both, the probingof the genome and the proteome. Today, pro-tein chip technology focuses basically on theunderstanding of molecular pathways which al-lows conclusions concerning the molecular di-agnostic, the drug discovery, and metabolic en-gineering applications. These interferences canbe done because of the capability of protein mi-croarrays to analyze the protein function of awhole genome level [69 – 73]. The results ofsuch high- throughput screening approaches canchange our fundamental understanding of thecellular processes of life on the molecular level.However, gene expression analysis does not suf-ficient enable a reliable prediction of the func-tion of a protein.Monitoring protein interactionsis an extremely complex matter since the pro-teome is the quantitative representation of thecomplete protein expression pattern of a cell un-der accurately defined conditions. The proteomerepresents the protein equivalent of the genome.In contrast to the genome which is determinedby the sequence of its nucleotides and thereforestatic, the proteome represents an extreme dy-namic object which is influenced by many pa-rameters. Not all genes will be switched on atthe same time in a cell and the sensitive balancebetween protein synthesis and protein degrada-tion can vary widely under different metabolicconditions. Consequently, a repeated analysis ofthe proteome will be successful only under ex-actly defined conditions, for example, cell cul-ture conditions. In practice, this proves to bevery difficult since conditions to which the cell

severely reacts are often unknown. However, thesensitive dependence of most different parame-ters offers the possibility of applying specificsmall changes of the protein expression patternin order to create sensitive biosensors.

Ideally, the analysis of the proteome deliversthe currently available set of all proteins, if thereis a way to maintain the quantitative relations ofall proteins during the analysis. Such data can-not be obtainedwith classical molecular biologysince no strict connection between the amount ofmRNA and the amount of protein exists. Param-eters such as mRNA stability, posttranslationalmodifications, protein degradation, and othersconsequently prevent a statement over the cur-rently available amount of protein.However, thisinformation is of utmost importance making ahigh throughput analysis necessary. One attrac-tive method is the use of protein microarrays.Genomewide screens for protein function are ofbiological importance for many applications:

– Analyzing protein expression profiles– Monitoring protein-protein interactions– Identifying protein posttranslational modifi-cations

– Screening the substrates of protein kinases– Examining the protein targets of smallmolecules

– Proteomic analysis as a function of bioprocesscultivation conditions

4.1.4. Metabolome Analysis and Metaboliteflux analysis

In order to improve a given strain ofmicroorgan-isms or even more complicated a eukaryotic cellline that is used in a biotechnological produc-tion process, it is not only necessary to analyzegene expression patterns or the proteome. Anal-ysis of metabolic turnover is central for recog-nizing possible targets for genetic engineering.Metabolic turnover comprises a large numberof biochemical reactions. For instance, in thewell-examined yeast Saccharomyces cerevisiae,about 1500 biochemical reactions have been es-timated involvingmore than850metabolites andcofactors [74]. Since in most of these reactionsthere is more than two educts and more than oneproduct, not to speak of the necessary cofactors,themetabolic turnover represents a complex net-work of biochemical reactions, also referred to

Biotechnology 29

as the metabolic network. The concept of iso-latedmetabolic pathways such as glycolysis, cit-ric acid cycle, or urea cycle as presented in mostbiochemical textbooks is therefore somewhatmisleading since it indicates that these pathwaysproceed independently. However, they are all in-terconnected and represent a huge interdepen-dent network that can activate or slow down in-dividual branches. Control over the activity ofindividual metabolic branches can be exerted onthe levels of transcription, translation, protein-protein interaction, or enzyme activity regula-tion, which adds another level of complexity.According to Nielsen [77] it is therefore neces-sary to analyze themetabolic network in three in-stances (metabolic pathway analysis) in order tofully oversee the metabolic activities in a livingcells, which is considered as a prerequisite for arational optimization of the production strain:

– Network structure (pathway topology).– Quantification of fluxes through the branchesof the network.

– Identification of control structures.

Much work has been done in the past inbiochemical laboratories around the globe toidentify the metabolic network structure at leastfor the more well-known microorganisms – inparticular when they are of industrial impor-tance. Thus, very often the presence of a cer-tain metabolic pathway can be deduced fromthe available literature. The most powerful ap-proach to identify a certain pathway when no in-formation is available is called metabolic label-ing. Here, the organism is fed with 13C-labeledglucose (or any other appropriate fundamentalmetabolite) and after a given time that is al-lowed for metabolization the labeling patternof other intracellular components is identifiedand reports on the metabolic pathways involved.From the analytical perspective, analysis of themetabolite pattern ismost often done byGC-MS(gas chromatography coupled tomass spectrom-etry) but also by NMR (nuclear magnetic reso-nance). Later on, biochemical assays addressingthe activity of certain key enzymes can be usedto confirm the presence of a certain pathway anddig out the cofactor requirements for the individ-ual stepswithin the pathway. Pathway identifica-tion just by enzyme assays is possible, however,very tedious and time consuming.

Flux Analysis. Once the network topologyhas been identified, it is necessary to learnabout the fluxes through each of the networkbranches. This kind of analysis requires exper-iments as well as mathematical modeling. Themost important concept behind flux analysis isreferred to as metabolite balancing. Accordingto this, the concentration of each metabolite isstationary inside the cell or in other words: thegeneration rate of a given metabolite in onemetabolic pathway (v+) equals the rate of itsonward reaction through the sum of all down-stream metabolic pathways (v−). Thus, for agivenmetabolite the correspondingmaterial bal-ance can be described as

v+1+v+2+v+3+. . .+v+i=v−1+v−2+v−3+. . .v−i

Such balances are established for all relevantmetabolites, such that a set of algebraic equa-tions results that describe the flux of moleculesthrough the different branches of the metabolicnetwork. When some of the fluxes are experi-mentally determined, the remaining fluxes canbe deduced mathematically from this set ofequations. The beauty of the metabolic balanc-ing approach is its simplicity. But reliable pre-dictions can only be obtained when the cofactor(NADH, NADPH, . . .) balances are also knownwhich means that all the pathways that gener-ate or consume any of these cofactors have tobe considered in the formalism. Feeding the or-ganisms with 13C-glucose in combination witha molecular analysis of the resulting metabolitelabeling downstream of glucose provides, how-ever, an easier solution. From the labeling pat-tern of all metabolites it is possible to set upequations for the balances of individual carbonatoms. Thus, the number of equations describ-ing the overall system increases the number ofconstraints for the solution and thus, makes bal-ances of cofactors unimportant.

The final step in analyzing the metabolicnetwork is to identify the regulatory mecha-nisms that control the fluxes through each of thebranches [75, 76]. It is very obvious that detailedknowledge about flux control might immedi-ately identify possible targets of metabolic en-gineering, for instance, to redirect the metabolicflux away from a pathway that does not leadto the desired product or consumes precursors.In order to understand flux control, it is nec-essary to study the regulation of the enzymes

30 Biotechnology

that are active around the branching points ofthe metabolic network [77]. Here the regulatoryinstances can be found at several hierarchicallevels as mentioned earlier in this chapter. Theexpression level of a given enzyme or any ofits regulatory proteins may be controlled on thelevel of transcription, RNA processing, or trans-lation. DNA and protein chip technology, as de-scribed above, provide powerful approaches tounravel whether any control occurs on the levelenzyme expression. Finally, allosteric controlover enzyme activity introduces another levelof metabolic fine-tuning. The kinetic propertiesof an enzyme like, for instance, its affinity forthe substrate molecules, the maximum substrateturnover rate and its sensitivity for product inhi-bition have to be characterized by biochemicalassays. When these information and the currentconcentrations of the relevant metabolites areavailable, it should be possible to predict howthe fluxes through individual branches of themetabolic networkwill changewhen the activityof one enzyme is altered by genetic engineeringor when the feeding situation is changed.

However, it should be emphasized at thispoint, that due to the enormous complexity ofthe metabolic network and all its regulatory in-stances, it is very difficult to predict allmetabolicconsequences induced by just one genetic mod-ification. Thus, it may be necessary to com-pletely analyze the metabolome of the modifiedstrain and – if the results are not satisfactory – gothrough one or several more rounds ofmetabolicengineering as sketched in Figure 13. This is par-ticularly relevant when the organisms respond toalteration of their genetic make-up by openingup metabolic side pathways that are silent in thewild-type strain.

4.2. Design and Production ofGenetically Optimized Strains forProduction – In Vitro Mutagenesis

In order to generate genetically modified organ-ism that might be superior to wild-type strainsfor production purposes, there are basically threemajor options:

A) Introduction and expression of a gene that isnot originally expressed by the organism oronly in small copy numbers

B) Silencing of a gene that codes for an en-zyme or regulatory protein which should beswitched off or reduced in expression level

C) Modifying an existing gene – and hence thecoded protein – that is expressed by the or-ganism of interest, for instance, to alter thesubstrate specificity of an enzyme or engineerits catalytic performance

In all three cases, the most suitable target forgenetic manipulations is cloned DNA that canbe handled and tailored in the well-defined en-vironment of a test tube without the complexityof an entire organism around, in particular thecellular membranes that may hinder the acces-sibility of the various reagents. Cloned DNA isusually handled in formof plasmidDNA.Aplas-mid (cytoplasm + chromatid = plasmid) is a cir-cular piece of double-stranded DNA that is au-tonomously replicated in bacteria and may evenbe exchanged between them. Plasmid DNA oc-curs also in wild-type strains since they are reg-ular extrachromosomal genes that can be easilyshared between certain bacteria. Many molec-ular tools have been developed that are specif-ically designed to work with plasmid DNA invitro. A particular family of plasmids, the so-called Expression plasmids or expression vec-tors as the more general term, additionally con-tain the necessary nucleic sequences to initi-ate transcription (promotor) and translation ofa gene that has been correctly inserted into theplasmid – instead ofmultiplication only. Expres-sion plasmids for prokaryotic cells differ fromthose for eukaryotic cells [78]. The three differ-ent strategies to alter gene expression in moredetail:

A) When a new gene should be introduced intoan organism or the expression level of an ex-isting one should be increased, this particulargene has to be cloned in a suitable expressionvector controlled by an appropriate promotorthat ensures a sufficiently high copy numberof mRNAs and hence protein. The produc-tion of human insulin in E. coli is a famousexample for the production of a recombinanteukaryotic protein in a prokaryotic organism.

B) When an existing gene should be silenced orreduced in activity, there are several exper-imental strategies. Today the most promis-ing technique is RNA interference (RNAi),a process by which double-stranded RNA si-

Biotechnology 31

lences gene expression. By transfecting cellswith small interfering RNAs (siRNAs) with100% homology to a target mRNA sequence,a specific knock-down of the correspondingprotein can be achieved. Bound by an RNA-induced silencing complex (RISC), the siR-NAs induce the degradation of their homol-ogous mRNA molecules in the cell, thus theprotein expression level decreases. RNAi isa reverse-genetics approach to identify genefunction by altering the phenotype of a cell[79 – 82].

C) Until the middle of the last century, tradi-tional biotechnology concentrated on eluci-dating new metabolic pathways and improv-ing the strains used for the production of anti-biotics and other useful natural products, usu-ally by exploiting random mutagenesis andselection. Later in the 1980ies, biologists be-gan to improve strains through metabolic en-gineering, that is, through the modification ofsingle metabolic pathways by means of tar-geted or site-directed genetic changes. Thetwo different approaches – random or site-directed mutagenesis – to improve the geneticmake-up of an organism are still applied to-day and shall be summarized here. However,throughout the last years progress in molecu-lar biology has provided a pool of experimen-tal approaches for targeted genetic changesthat are way too many and multifaceted to bereviewed comprehensively. Thus, only a fewprinciples can be discussed here, and are con-fined to the best-known models of genetic re-search, the bacteria. The interested reader isreferred to other chapters of this series or theliterature on molecular biology for more de-tailed and organism specific information [83,84] (→ Genetic Engineering).

4.3. Random Mutagenesis, Isolation andSelection of Mutants

Spontaneous Mutants. In bacterial popula-tions, mutational events occur at certain rateswithout experimental treatment. These sponta-neous mutations, which result in mutants, aredue to errors that occur during DNA replica-tion. The frequency of spontaneous mutations ina bacterial population varies from gene to geneand species to species and may involve 10−2

to 10−11 of the total cell population. The spon-taneous mutation rate (probability of a mutationper cell and generation) for a gene is about 10−5,and for a pair of nucleotides it is 10−8. The num-ber of reversions (or functional back-mutations)is of the same order of magnitude.

Induction of Mutations. The mutant fre-quency can be considerably increased by treat-ment of the cells with chemical, physical, orbiologic mutagenic agents. Three classes of mu-tants can be distinguished with respect to theirgenetic structure: 1) replacement of one basepair by another, e.g., AT by GC; 2) a base paircan be inserted or eliminated; 3) several basepairs, DNA fragments, or even genes can belost (deletion), change their position within thechromosome (transposition), or be interruptedby insertion of foreign DNA (insertion). Class-1mutants, called point mutants, revert readily totheir original structure.

Various chemical agents are in use to inducemutations [85, 86]:

1) Incorporation of Base Analogues.Base analogues are antimetabolites. Some aresufficiently similar to the natural purine andpyrimidine bases that they are taken up bythe cells and incorporated into DNA duringreplication. They are able to fulfill their func-tions, but tend to bind a wrong counterpartduring replication, thus introducing a wrongbase pair and causing a point mutation.

2) Chemical Change of Bases.Several mutagenic agents effect a chemicalchange in a base and thus cause an error inreplication. For example, treatment of cellswith nitrite results in the de-amination ofadenine, guanine, and cytosine, and conse-quently inmispairing.Alkylating agents, suchas ethyl or methyl methanesulfonate, ethyl-eneimine, nitrogen mustard, and N-methyl-N-nitro-N-nitrosoguanidine (MNG), belongto the most effective mutagenic agents. Acri-dine dyes (proflavin) function by intercalationand result in insertions or deletions of singlebase pairs.

3) Irradiation.UV irradiation mainly affects pyrimidinebases and results in replication errors.

4) Transposon Mutagenesis.By conjugation, transposon-containing plas-mids (Tn elements) can be transferred from

32 Biotechnology

an appropriate donor bacterium to the bac-terium to be mutagenized. Transposons areDNA fragments that tend to “jump” to variouspositions in the DNA sequence of the chro-mosome or plasmid. Their insertion interruptsgenes and results in insertion mutants.

The changes in the DNA described above in-volve an alteration of its base sequence, called apremutation. Subsequently, themutant characterhas to be expressed in the phenotype of the cell.About 10–20 growth cycles may be required fornuclear segregation, dilution of enzymes, start-ing or stopping the synthesis of enzymes, andother functions.

4.4. Types of Mutants and SelectionPrinciples

A mutation is a rare event, and the number ofmutants in a population is small. Many thou-sands of Petri dishes would have to be inspectedand many millions of colonies (clones of singlecells) would have to be examined to recognizeand isolate a mutant if there were not a selection(enrichment) procedure for increasing the num-ber of the desiredmutants within the population.Enrichment is achieved either by killing part ofthe parent cells or by growth under conditionsthat confer growth advantage to themutant cells.There are many means for enriching mutants. Infact, designing strategies to select mutants is oneof the most difficult and demanding arts of themicrobiologist, and each type ofmutant requiresa special trick.

The recognition of mutants poses anotherproblem. Mutants that have lost or gained pig-ment, colony size or characteristics, or that havebecome resistant to toxic compounds or bacte-riophages can be easily detected in a lawn ofparent cell colonies. Some mutants show up af-ter addition of indicators. The detection of aux-otrophic mutants requires comparison of growthon two different nutrient media. One tries to rec-ognize mutants on an agar plate and to avoid la-borious test tube assays of thousands of isolatedcell clones. There are, however, many mutanttypes that require these efforts.

4.4.1. Auxotrophic Mutants

If the mutation has affected the ability to syn-thesize, for example, the amino acid leucine,the mutant will require leucine in the nutri-ent medium. A mutant that is auxotrophic forleucine (leu−) can be recognized by compari-son of its growth on two agar plates, one withand the other without leucine. While the pho-totrophic parent type (leu+) will grow on bothplates, the auxotrophic leu− mutant will growonly on the supplemented agar.

The enrichment of auxotrophic mutants canbe achieved by application of agents such aspenicillin that kill only growing cells and leavenon-growing cells unimpaired. If the populationof parent cells and rare leu− mutants is grownon a minimal medium (lacking leucine) only theparent cells will grow and after the addition ofpenicillin will be killed within a few hours. Themutants and some parent cells will survive andthe mutant fraction is increased by a factor of104 to 106. Another method involves the prin-ciple of “lethal synthesis.” Growing cells will,for example incorporate radioactive phosphateor antimetabolites and will be killed while thenon-growing mutants will survive.

4.4.2. Regulatory Mutants

Some mutants, which in contrast to the parenttype form a biosynthetic enzyme constitutively,can be selected directly on the agar plate. Forexample, if an antimetabolite is added to theagar, the parent cells will incorporate it into theirprotein and stop growing. Mutants, however,that overproduce the normal metabolite becauseof overproduction of the biosynthetic enzymewill not incorporate the antimetabolite; they willgrow, and sometimes even show their regulatorydefect by excreting the metabolite, thus initiat-ing secondary growth of the parent cells in thesurroundings of the mutant colonies. Some pro-cedures for the selection and recognition of mu-tants are listed in Table 2.

4.4.3. Other Selection Methods

Various other methods can be used to separatemutants from their parent cells. Because some

Biotechnology 33

Table 2. Procedures for selecting and recognizing various mutants

Kinds of mutants Selection or enrichment procedure Recognition of mutants

Mutants resistant toinhibitors, antibiotics,toxic compounds, orbacteriophages

about 108 cells spread on a nutrient agar mediumcontaining the inhibitory or killing agent.

only the desired resistant mutants able to grow.

Auxotrophic mutantsthat require accessorynutrients (vitamins,amino acids, or othermetabolites) forgrowth

penicillin technique or analogous procedures: cellsgrown in a medium lacking the accessory nutrients butcontaining penicillin or another agent that kills onlygrowing cells. Auxotrophic mutants not able to grow inminimal medium survive.

if the cell suspension is spread on a completemedium containing the accessory nutrients and if thecolony pattern is then replicated on a minimalmedium, colonies that do not grow on the minimalmedium are auxotrophs.

Mutants that lack theability to utilize aspecial substrate

penicillin technique and/or direct isolation of pinpointcolonies: the cell suspension is spread on a nutrient agarthat contains the substrate utilizable by the wild-typecells at normal concentrations (0.5 vol%), and thesubstrate accessible by the desired mutants at a verylow concentration (0.005 vol%). Pinpoint colonies aretransferred and screened.

comparison of colony patterns on agar platescontaining different substrates; mutants unable togrow on a substrate will need a different nutrient forgrowth and are therefore recognized. Excretorymutants can be recognized by staining reactions (pHindicators) or by dyes added to the agar or aftercolony growth.

Temperaturesensitive(conditional lethal)mutants

penicillin technique to kill the wild-type cells growingat their upper temperature limit.

comparison of colony pattern on plates that havebeen incubated at different growth temperatures.Only those colonies that grow, e.g., at 25 ◦C but notat 37 ◦C are isolated.

Mutants derepressedor constitutive forcatabolic enzymes

1) continuous culture with the substrate asgrowth-limiting factor; 2) alternating growth on twodifferent substrates; 3) growth in the presence of anagent suppressing induction (antiinducer).

If the cell suspension is spread on a nutrient agarcontaining a noninducing substrate and afterincubation the colonies are sprayed with a solutioncontaining the constitutively utilizable substrate +indicator + inhibitor of enzyme protein synthesis,only constitutive mutants immediately degrade thesubstrate and change the indicator.

Mutants derepressedor constitutive foranabolic enzymes

growth in the presence of an antimetabolite that inhibitsthe growth of the wild-type cells. Among the resistantcells are some mutants that are not subject to endproduct repression.

only the resistant mutants grow, colonies of a cloneforming the enzymes of a biosynthetic pathwayconstitutively will be surrounded by a halo ofsatellite colonies due to the excretion of a metabolite.

cell constituents have lower or higher densi-ties than the average cell mass, the cells can beincubated under conditions that promote syn-thesis only in the parent or the mutant cells,followed by centrifugation in a sucrose gradi-ent. Cells rich in lipids (poly(β-hydroxybutyr-ic acid), triglycerides), glycogen, calcium dipi-colinate, magnetosomes, etc. can thus be sepa-rated. In other cases tactic responses, such asmigration in light–dark fields (phototaxis), ingradients of nutrient (chemotaxis), or oxygenconcentrations (aerotaxis), can be used to se-lect mutants that are defective in transport sys-tems or in perception mechanisms. Adsorptionon particle surfaces (glycogen or starch gran-ules, lipid droplets, lignocellulose particles) fol-lowed by fractional centrifugation can be used toselect formutants that have altered surface struc-tures. The separation efficacy can be increasedby the use of column chromatography or bysuicide techniques. Suicide techniques employ

compounds that are converted by the wild typesto toxic compounds; this lethal synthesis will fi-nally kill the parent cells. Examples aremonoflu-oroacetate, bromopyruvate, or chlorate, whichare converted into fluorocitrate, bromolactate, orchlorine, respectively. The mutants are unableto catalyze such conversions and will thereforesurvive. With this methods it is possible to se-lect for cells that are deficient in nitrate reduc-tase (chlorate, perchlorate), or for fermentativemutants that have lost the ability to form acids(bromide, bromate). Successful biotechnologyin many cases depends on the background in-formation and the ingenuity of the researcher inselecting, recognizing, and manipulating the de-sired organism.

In most cases, a single random mutagene-sis decreases the characteristic of an organismthat is to be changed, such as, for instance, thecatalytic performance of an enzyme. By apply-ing high-throughput screening technologies, it

34 Biotechnology

is, however, possible to screen large numbers ofstrains that are produced by randommutagenesisand find the valuable ones with beneficial muta-tions. Thus, by going through several rounds ofrandommutagenesis on the one hand and screen-ing for the desired phenotype on the other, thisprocess will identify those strains with consid-erably improved properties for production [87].

4.4.4. Targeted or Site-Directed Mutagenesis

Site-directed mutagenesis allows modifying aDNA sequence and thus the protein that is codedby this sequence in a specific andwell-controlledway. Thus, it is possible to exchange one ormoreamino acid within the primary structure of theprotein and thereby alter its functionality, for in-stance the catalytic performance of an enzyme.Compared to random mutagenesis, this is themore straightforward and direct approach notrelying on chances that a genetic change mayoccur at a site that is useful for strain improve-ment. However, it requires detailed informationon the three-dimensional shape of the proteinand its primary structure. Many different ap-proaches for targeted genetic alterations havebeen described that cannot be fully reviewedhere. We will summarize the most widely ap-plied and flexible concept of oligonucleotide-based mutagenesis.

In oligonucleotide-based mutagenesis, thegene of interest is cloned in a plasmid. Af-ter denaturation of the double-stranded circularDNA, oligonulceotides are added to the systemthat share sufficient sequence homologywith thecoding strand of the gene to allow for hybridiza-tion. However, at the site where the mutationsshould be introduced the sequence of the olignu-cleotide is altered. Since this is only a minorportion of the entire olignucleotide, hybridiza-tion still occurs but the base exchange has beenintroduced. Figure 16 sketches the underlyingprinciple.

Chain extension byDNApolymerase and lig-ation results in a double-stranded DNAwith onemutant strand and one wild-type strand. Whenthese plasmids are then introduced in bacteriaby transformation, semiconservative replicationproduces homoduplexes with both strands be-ing either of the wild-type or the mutant. Whencolonies are grown, one can now screen for those

strains in which the mutant plasmid has pre-vailed.

Many more experimental options exist to in-troduce site-specific mutations. Many of theseare based on polymerase chain reaction (PCR)and the application of primers that already holdthe mutation, or insertion or deletion. The inter-ested reader is referred to Bowen [88].

The aim of modern metabolic engineering isto study the cell as an integrated system of ge-netic, protein, metabolite, and pathway eventsthat are continually changing. This approachshould help to optimize the production of sub-stances or to produce substances with improvedproperties. Historically, metabolic engineeringis the targeted recombination of the DNA forproteins involved in the metabolism or in reg-ulation. Today, the objective of metabolic engi-neering is to quantify the pathway alterations inresponse to environmental mediators taking intoaccount the entire metabolism. Knowledge of invivo flux distributions in cells at different physi-ological states is of increasing importance for theevaluation of biotechnological processes. Geneexpression data provide information on path-ways relevant to the metabolic models. Further-more, the so-called combinatorial biosynthesisuses techniques from molecular biology to alteror combine genes frombiosynthesis pathways ofthe secondarymetabolism to generate new prod-ucts with improved pharmacological profil. Theimportance of studying the entire metabolismrather than one gene or protein at a time has be-come increasingly relevant with the advent ofhigh-throughput genomic and proteomic tech-nologies, especially microarrays. The DNA mi-croarray technology will gain great importancein the field of future biological research devel-oping into a key technology of the 21st cen-tury hence revolutionizing modern biotechnol-ogy. DNA chips are used as biosensors in indus-trial analysis, biomedical diagnosis, and foren-sic science. Although this technology providesa powerful tool that is widely utilized for geneexpression, it is just beginning to find appli-cations outside of genomics. Further develop-ment of protein, cell, and tissue chips will makeit possible in the future to carry out miniatur-ized, highly parallel analysis of metabolic path-ways. The challenge is to transfer these princi-ples into technically applicable and precise ana-

Biotechnology 35

Figure 16. Site-directed mutagenesis of cloned DNA by oligonucleotide mutagenesis

lytical systems that can be used for many appli-cations repeatedly.

5. Cultivation and Bioprocesses

In this chapter, only the basic requirements forbioprocesses are described very shortly. Forany further details concerning different typesof bioprocesses, bioreactors, kinetics, sterility,and cleaning, the reader is referred to (→ Bio-chemical Engineering). Very detailed informa-tion about the monitoring and control of bio-processes can be found in this contribution inChapter 8.

Bioprocesses [89, 90] are used for the trans-formation of organic matter with the help of bio-catalysts, such as living cells, dead cells, or theircomponents, e.g., enzymes [91, 92]. Biopro-cesses are utilized for chemical syntheses and forthe conversion of waste material to either usefulproducts (recycling) or effluents harmless to theenvironment (waste treatment). The outstandingfeature of bioprocesses is their high syntheticpotential to carry out a series of very compli-cated chemical reactions in a one-step process.

Classic biocatalysts are yeasts, fungi, and bacte-ria; animal and plant cell cultures have becomeimportant only in more recent times (see Chap-ter 9). Process development [93] [94] [95] in-volves the areas of biology (strain developmentand bioregulation), reactor design, process con-trol [96], and medium design [97, 98] as well asdownstream processing (product recovery, seeChapter 7).

5.1. Isolation of Microorganisms

Microorganisms can be purchased from cul-ture collections. But “microbes are everywhere,the environment selects,” and a desired typeof microorganism can often be isolated fromits most probable natural habitat. For example,methanogenic bacteria are present in the anoxicmud sediments of ponds and lakes, hemoglobin-degrading bacteria in abattoirs, and hydrocar-bon-oxidizing bacteria around oil fields or leakyengines of motor cars. From samples of thesematerials, bacteria can be isolated by enrich-ment culture. The technique of enrichment cul-ture is simple and straightforward [99, 100].

36 Biotechnology

By establishing defined environmental condi-tions with respect to the energy, carbon, and ni-trogen sources; hydrogen acceptor; gas atmo-sphere; temperature; pH value; light; etc., andby inoculatingwith amixed population that con-tains the desiredmetabolic type, the best adaptedorganism will dominate and overgrow all ac-companying organisms.

Liquid enrichment culture is recommendedif the fastest growing organism is desired. In thiscase, the organism is repeatedly transferred fromthe original liquid culture through several sub-cultures. If, however, a great number of strainswith only slightly differing traits are to be sepa-rated, the direct plating method is the preferabletechnique: the inoculum is diluted and spreadon a solidified enrichment medium (agar dish).During incubation the cells form colonies andcan be isolated separately.

Whereas formerly these kinds of naturallypure or well-balanced mixed cultures were usedin industrial practice (e.g., yeast fermentations,vinegar and cheese production) now only purecultures are used. Pure cultures are obtained bydiluting the enrichment culture and spreadingthe suspended cells on an agar surface or dis-tributing the cells in an agarmedium.After incu-bation, single colonies are isolated and streakedon various media to test for commensalic bac-teria. Finally, a clone of the desired organism isobtained as a pure culture.

Preliminary results indicate that previouslynoncultivable microorganisms could be the keyto the biosynthesis of active substances. Thegenes responsible for the expression of the natu-ral substance biosynthesis could be inserted intomicroorganisms which can be grown easily, aprocedure known as the metagenome approach.The metagenome is a set of all genetic mate-rial from organisms which cannot be cultured,e.g., from the soil or from communities of or-ganisms. The development of the metagenometechniques becomes possible with the develop-ment of high-throughput techniques. The de-velopment of high-throughput DNA sequenc-ing began in the early 1990s as simplificationsand automation made it possible to decode thesequence of entire genomes. In 1995 the firstfull genome sequence was published, that of theGram-negative bacterium Haemophilus influen-zae. Of particular interest for biotechnology isthe possibility of sequencing microbial produc-

tion strains developed by repeated randommuta-genesis and screening. A comparison with pre-cursor strains could identify the mutations re-sponsible for increased production. That way,production strains could be designed with just aminimal number of defined mutations [101].

Designer Bugs. The term used for microor-ganisms tailored by genetic engineering meth-ods so that they can carry out desired biochemi-cal conversions efficiently is designer bugs.Con-trary to the strategy used in the past to obtainmicroorganisms for the production of a desiredproduct by coincidental mutagenesis and se-lection, genetic engineering has opened up thepossibility of customized construction of pro-duction strains by metabolic engineering. Thegenome permits insight into the entire metabolicpotential of the organism. This insight is cur-rently limited because the function ofmany geneproducts is not yet known. Organisms, such asE. coli, Bacillus subtilis, Corynebacterium glu-tamicum, Pichia pastoris, or S. cerevisiae, arepreferentially applied in the development of de-signer bugs. Further important aspects govern-ing the choice of host organism for a designerbug are knowledge of metabolism, regulationmechanisms, growth properties, and hazard po-tential. Based on methods developed in the lastfew years for the analysis of the genome, tran-scriptome, proteome and metabolome, whichthus permitted a holistic consideration of the or-ganism (“systems biology”), it will be possiblein future to develop designer bugs for biotech-nological production of a desired product con-siderably more quickly and specifically than inthe past.

A list with mostly all culture collec-tion worldwide can be found in the inter-net [102] under the URL http://www.bacterio.cict.fr/collections.html and in ref. [103 – 105].

5.2. Requirements for Growth

The process solution must primarily supply car-bon and energy. In order to maintain growth,nutrients, trace elements, and such growth fac-tors as vitamins must be added to the processsolution. Appropriate selection of the mediumcomponents is essential for proper functioningand activity of the biocatalysts involved in thereaction.

Biotechnology 37

Table 3. Standard nutrient media

a) Minimal medium for bacteriaK2HPO4 0.5 g/LNH4Cl 1.0 g/LMgSO4 · 7 H2O 0.2 g/LCaCl2 · 2 H2O 0.1 g/LFeSO4 · 7 H2O 0.01 g/LGlucose 5.0 g/LTrace element solution 1.0 g/L

b) Complete medium for bacteriaPeptone 10 g/LYeast extract 1.0 g/LNaCl 2.0 g/LMgSO4 · 7 H2O 0.2 g/L

c) Complete medium for fungi (pH 6.0)Malt extract 10 g/LYeast extract 4 g/LGlucose 2 g/LKH2PO4 0.5 g/LNH4Cl 1.0 g/L

d) Vitamin solution for soil and water bacteriaBiotin 0.2 mgNicotinic acid 2.0 mgThiamin 1.0 mgSodium 4-aminobenzoate 1.0 mgSodium pantothenate 0.5 mgPyridoxamine 5.0 mgCyanocobalamine 2.0 mgDistilled water 100.0 mLTwo to three mL of this solution is added to 1 Lof nutrient solution.

e) Trace element solutionMnCl2 · 4 H2O 3 mg/LCoCl2 · 6 H2O 5 mg/LCuCl2 · 2 H2O 1 mg/LNiCl2 · 6 H2O 2 mg/LNa2MoO4 · 2 H2O 3 mg/LZnSO4 · 7 H2O 5 mg/LH3BO3 2 mg/L

The growth of microorganisms is dependenton the presence of water or moisture. Nutrientsto be used as sources of energy and for synthe-sis of cell constituents are dissolved in water.The growth requirements for various microor-ganisms are different, and many recipes for thecomposition of nutrient media are known. Ba-sically, all chemical elements that constitute thecell substance have to be present in utilizableforms. There are ten macroelements that areconstituents of all organisms: carbon, oxygen,hydrogen, nitrogen, sulfur, phosphorus, potas-sium, calcium, magnesium, and iron. In addi-tion, there are microelements (trace elements),such as manganese, nickel, cobalt, molybde-num, zinc, copper, vanadium, boron, chlorine,

sodium, selenium, silicon, tungsten, and a fewothers, that are not required by all organisms.The compositions of a few simple synthetic nu-trient media are presented in Table 3.

5.2.1. Chemical Composition of BacterialCells

Bacterial cells harvested by centrifugation froma culture growing in liquid medium containabout 70wt% water. The elemental assay of thedry mass of E. coli is: approximately 50% car-bon, 20% oxygen, 14% nitrogen, 8% hydro-gen, 3% phosphorus, 1% sulfur, 2% potassium,0.05% each of calcium, magnesium, and chlo-rine, 0.2% iron, and a total of 0.3% trace el-ements. The organic analysis of the dry massof cells is presented in Table 4. It should benoted that the values refer to a specific bacteriumgrown in a specific environment and harvestedin a specific phase of growth. The results maydiffer if, e.g., the cells encountered limitation ofthe nitrogen source and accumulated poly(β-hy-droxybutyric acid) (up to 90wt%) or glycogen(about 50%) within their cells.

Table 4. Overall macromolecular composition of Escherichia coli(mass percent, dry matter basis) [106]

Protein 55.0RNA 20.5DNA 3.1Lipid 9.1Lipopolysaccharide 3.4Peptidoglycan 2.5Glycogen 2.5

Total macromolecules 96.1Soluble pool of buildingblocks, vitamins 2.9

Inorganic ions 1.0

5.2.2. Carbon and Energy Sources

Only autotrophic organisms are able to synthe-size all organic cell constituents from carbondioxide as the main carbon source. All oth-ers derive the cell carbon from organic com-pounds, which usually serve as both carbon andenergy sources; they are partially assimilatedand partially oxidized (dissimilated). Glucose,the monomeric constituent of polysaccharidessuch as cellulose or starch, can be used by themajority of microorganisms.Many other natural

38 Biotechnology

compounds can be utilized and degraded by oneor another microorganism.

5.2.3. Accessory Nutrients

In addition to carbon and energy sources, manyorganisms require accessory nutrients (growthfactors), such as vitamins, amino acids, purines,or pyrimidines (see Table 3).

5.2.4. Sulfur and Nitrogen

These elements can be used in their oxidizedforms as sulfate and nitrate. Ammonium ion isthe most common nitrogen source for microor-ganisms.

5.2.5. Oxygen

Theoxygen atomsof the cellular constituents aremainly derived fromwater, substrates, or carbondioxide. Atmospheric oxygen serves mainly asa terminal hydrogen acceptor of aerobic respira-tion, being reduced to water.

5.2.6. Complex Media

Many microorganisms can grow in a simple nu-trient medium such as that listed in Table 3 a.For Leuconostoc mesenteroides such a syntheticmedium contains 40 components. If the nutri-ent requirements of an organism are not exactlyknown, complex nutrient media can be used,such as yeast extract, yeast autolysate, peptone(Table 3), meat extract, wort, carrot juice, co-conut milk, or horse manure extract. In othercases complex media are used for economicalreasons, for example, whey permeate, corn steepliquor, soybean extract, or molasses.

5.2.7. Solid Media

For solidification of media, agar is added at aconcentration of 1.5 to 2.0wt%. Agar melts at100 ◦C and solidifies on cooling to below 45 ◦C.

5.2.8. Hydrogen Ion Concentration

The majority of microorganisms prefer a pH ofabout 7.0. However, there are acidophilic andalkaliphilic microorganisms. Yeasts and otherfungi prefer pH 5.0. Buffers, in most cases phos-phates, are used to maintain the desired pH, al-though the CO2/HCO−

3 system or organic sub-stances may do as well. The pH value is very im-portant: the drop from pH 7.0 to pH 6.0 means atenfold increase in the concentration of H+ ions,which is a significant change.

5.2.9. Carbon Dioxide

Many microorganisms require a higher concen-tration of CO2 than exists in the atmosphere,such as 10 vol%. This requirement applies tothose bacteria that in their natural habitats (in-testinal tract, tissue, milk, blood, fermentingjuices) are exposed to high CO2 partial pres-sures.

5.2.10. Aeration

All obligately aerobic microorganisms requireoxygen as an electron acceptor for energy gen-eration. For growth in thin layers on liquid orsolid media atmospheric oxygen may suffice. Inliquid media aerobic bacteria grow only on thesurface if the medium is not agitated and aer-ated. Only dissolved oxygen is utilized; its sol-ubility in water is very low (6.2 mL/L at atmo-spheric pressure and 20 ◦C). Therefore oxygenhas to be supplied continuously to the cell sus-pension growing in submerged culture in flasksor fermenters. To meet the increasing oxygendemand in a growing culture, the speed of agita-tion, the oxygen concentration in the gas phase,and the total gas pressure can be increased. Var-ious kinds of fermenters have been designed toincrease the gas transfer from the gaseous to theliquid phase. The final cell density and yield ofmicroorganisms depends on the rate of oxygentransfer. For fermenter design and scale-up, boththe respiration rate (oxygen uptake) of the mi-croorganisms and the oxygen transfer rate fromgaseous to liquid phase have to be known. Be-cause the formation of desired products is highlydependent on oxygen supply – whether the cells

Biotechnology 39

are able to take up oxygen at their maximum res-piration rate or only much less – aeration posesone of themost important problems in fermentertechnology.

5.2.11. Anaerobic Techniques

For the growth of strictly anaerobic bacteriathe complete exclusion of oxygen is required.Anaerobic techniques involve deaerated nutri-ent solutions, tightly sealed flasks, incubation inanaerobic jars, the use of chemical oxygen ab-sorbers (pyrogallol, dithionite or the “gas pak,”which contains hydrogen- and carbon dioxide-evolving agents and a catalyst to promote theoxyhydrogen reaction), resazurin as a redox in-dicator, anaerobic hoods, and skilled hands topractice these anaerobic techniques.

5.2.12. Media Preparation

Substrates for industrial applications are usuallyincluded in complex media, which must be sup-plemented with special compounds, such as anitrogen source, various nutrient salts, or cer-tain trace elements. Organic precursors are alsoadded in some cases for efficient product forma-tion. Many of the feedstocks need appropriatepretreatment in order to provide for good assimi-lation by themicroorganisms. Starch-containingmaterials are prepared by milling or steam treat-ment for softening and swelling. For many pro-cesses, especially when yeasts are used, starchmust first be broken down to sugar by treatmentwith amylase derived from barley malt or withmicrobial amylases. Other feedstocks, such aswood, must be pretreated with acid or alkali.Sulfite liquor is stripped from sulfur dioxide byaeration or neutralization. Molasses is purifiedby acidification and centrifugation or filtration.In some cases heavy metals have to be removedfrom feedstocks prior to their use. For processesusing solid substrates, see [107].

In general, the raw materials are dissolved orsuspended in water and the resulting mediumis heated, filtered, and sterilized. The complexcomposition of themedia used in industry causesconsiderable problems. For downstream pro-cessing (harvest, concentration, and purificationof product) or for analytical assays during theprocess, additional pretreatment of the raw ma-terial is needed to avoid unfavorable side effects.

5.3. Sterilization

The basis of microbiological laboratory meth-ods and of the conservation of food and feedproducts is the killing of microorganisms. Ster-ilization means the removal of living microor-ganisms, and can be achieved by moist heat, dryheat, filtration, irradiation, or chemical means[108].

5.3.1. Moist Heat

Vegetative cells of bacteria and fungi suspendedin water are killed at temperatures around 60to 80 ◦C within 5 to 10 min; yeast and fungalspores are killed above 80 ◦C, and endosporesof bacteria at 120 ◦C within 15 min. Becauseendospores of bacteria are highly tolerant to var-ious environmental conditions, they are ubiqui-tous and are distributed through the air. Theirpresence requires that all nutrient media andcanned foods be sterilized in an autoclave atabout 121 ◦C for 20 min. If conditions do not al-low the germination of spores and the growth ofspore-forming bacteria, e.g., in acid fruit juices,jam, or desserts, heating to 80–100 ◦C for 10minwill suffice. This is called partial sterilization orpasteurization.

5.3.2. Dry Heat

For killing bacterial endospores by dry heat,longer exposure times and higher temperaturesare required than with moist heat. Glass andother heat-resistant equipment can be sterilizedfor 2 h at 160–180 ◦C in a dry oven. In any caseappropriate indicators or soil samples that con-tain bacterial spores are included in the oven totest whether adequate temperatures have beenreached and even the extremely heat-resistantspores have been inactivated.

5.3.3. Filtration

Solutions containing thermolabile compoundscan be sterilized by filtration through nitro-cellulose membranes, kieselguhr, porcelain, as-bestos, and others. Usually filters with a poresize <0,2 µm are used.

40 Biotechnology

5.3.4. Irradiation

UV irradiation is used to keep rooms partiallysterile. Bacteria and their spores are killed ratherquickly, but fungal spores are only moderatelysensitive to radiation. Ionizing radiation (X-ray,gamma radiation) is used to sterilize food andother compact materials.

5.3.5. Chemical Means

Ethylene oxide is used to sterilize food,plastics, glassware, and other equipment. β-Propionolactone and diethyl carbonate areadded to nutrient solutions. The presently usedsoaps and detergents applied at moderate tem-perature kill all vegetative cells and even manyspores. Cleaning by themethods practiced in thekitchen provides almost sterile glassware.

Remarkably, the killing of microbes is a pro-cess that follows first-order reaction kinetics.In a population there are allways some cells orspores that are more resistant than the majority.Accordingly, sterilization procedures are mostlikely to be effective on relatively clean sub-strates.

5.4. Types of Bioprocesses

In chemical technology, a choice is to be madebetween batch processes or continuously oper-ated processes. Many parameters usually favorthe second over the first choice. However, inbiotechnology, the batch process still predom-inates. This specific problem is treated in moredetail (also in regard to engineering aspects) in→ Biochemical Engineering.

5.4.1. Surface Culture

Surface cultures are mostly used when fungalmycelia act as catalysts. Mycelium is grown inshallow pans containing a small volume of themedium. As the mycelium grows, it eventuallyforms a compact mat. Thismethodwas typicallyused in the production of acids, such as citricacid. In large-scale production, surface cultureis being abandoned more and more and replacedby the submerged culture method [109 – 111].

Surface cultivation is usually applied in thelab scale in fungal research. Large Erlenmeyerflasks or specially designed vessels, such as theFernbach, Roux, or Sakaguchi flasks, containingsmall volumes of liquid are used. The flasksand liquid are sterilized and inoculated withspore suspensions or small pellets of mycelium.Growth and product formation are dependentupon the depth of the medium layer and theamount of surface provided by the flasks, whichin turn affects the necessary oxygen supply.

In large-scale production large pans are used.They are filled with substrate, inoculated, andthen kept at controlled temperature and air hu-midity. Pans are stacked on suitable racks, andconnected with a system of overflowing tubes.The liquidmedium is poured in at the top. In thisway, an entire stack can be loaded at once. If thestack is designed appropriately, it can be steril-ized, inoculated, aerated, and maintained undercontrolled conditions. The same principle canbe applied for solid substrates, which are firstinoculated and then loaded into the pans. Thematerial is spread at a thickness of several cen-timeters onto screens or perforated metal sheets.

Columns filled with carrier material ontowhich the microbes are immobilized are alsoconsidered a surface culture as long as the col-umn is not completely flooded. The medium istrickled through the column and air is injected atthe bottom. The product is then collected fromthe carrier, which is made of wood shavings,plastics, or ceramics. This type of surface cul-ture has been used for vinegar production andsewage treatment. However, it was abandonedand replaced with submerged culture. Remark-ably, the immobilization methods that were de-veloped for such biocatalysts as living cells, rest-ing cells, enzymes, or organelles are formallysimilar to these conventional processes.

5.4.2. Submerged Culture

In this process, themicrobes are suspended in theliquid medium. The simplest example of a sub-merged culture consists of a vessel without anyagitation device, in whichmicroorganisms settleat the bottom and form a compact layer. This isthe classic arrangement for all types of ethanol-producing processes. Some agitation will occur,

Biotechnology 41

however, from the movement of rising carbondioxide bubbles.

For submerged, anaerobic processes, simpleagitation devices for gentle mixing will suffice.This configuration allows for the mixing of liq-uid and gas. Carbon dioxide or other gases, suchas nitrogen, can be added during the process.

5.5. Process Layout

5.5.1. Reactors

Appropriate reactors in biotechnology have tomeet the requirements for agitation, aeration,corrosion resistance, and aseptic operation. Var-ious designs of stirred vessel are therefore usedin the laboratory and in production plants. Con-tainer construction is based on principles similarto reactors used in heterogeneous catalysis.

In former times, bioprocesses were carriedout under nonsterile conditions and took placein vats of wood. Beer today is brewed in largecubical containers coated with ceramic tiles. Forthe more conventional surface processes (citricacid, acetic acid), small pans were used.

A basic problem in aerobic processes is theefficient transport of oxygen into the liquid andthe microbial cells. Very efficient devices havebeen developed to provide air for aerobic re-actions. Some examples of more modern reac-tor configurations for agitation and aeration areshown in Figures 20 and 21.

5.5.2. Containments for Anaerobic Processes

Large units for anaerobic processes are madeof wood, metal, or coated concrete. They haveno covers and consequently cannot be operatedaseptically. However, submerged anaerobic pro-cesses can be performed under conditions thatfavor the formation of product and inhibit theaccumulation of unwanted organisms by properselection of reaction parameters, such as pH andtemperature. Lactic acid, for example, is pro-duced at 50 ◦C and low pH, and the developmentof competing microbes is negligible.

In other cases, closed vessels are used. Theyare usually made of stainless steel, have largecapacities of up to 1000 m3 or more, and areequipped with agitators and, if necessary, with

cooling andheating coils.Gases that formduringthe process (CO2, H2, or evaporated solvents)are collected at the top. These large tanks haveopenings for loading, inoculation, harvesting,and cleaning. Large units are mostly sterilizedwith steam.

Extremely large containments with capaci-ties of thousands of cubic meters are used insewage treatment. They are usually constructedof concrete and allow automatic feed and re-moval of material. Sludge treatment plants areoften equipped with heating elements to allowthermophilic processes for methane production.The gas is collected and stripped of hydrogensulfide if necessary.

5.5.3. Reactors for Aerobic Processes

Bioreactors used for aerobic processes are ba-sically similar to those used for anaerobic pro-cesses. In addition, they contain devices for aer-ation and agitation. The capacities used in in-dustry may vary considerably, from 10 to 200m3. Large tanks of 1000 m3 and more are alsoused for aerated, sterile processes (e. g., glutamicacid). For aseptic processes the tank is sterilizedwith steam before loading; a slight overpres-sure is often maintained during cultivation. Ag-itation principles vary considerably and includemechanical as well as pneumatic and hydrody-namic systems.

The most commonly used type of bioreac-tor is the stirred tank reactor (STR) using aflat blade turbine (FBT) for agitation (Fig. 17).This configuration provides excellent mixingand mass transfer in media of low viscosity.Drawbacks are high power demand, poor per-formance with highly viscous liquids, and ratherpoor interchange of material betweenmixing re-gions around the blades. Air is normally injectedby a sparger. Larger units are equipped for pneu-matic agitation, inwhichmovement is caused byinjected air. For proper hydrodynamic control,various kinds of baffles and draft tubes can beinserted.

5.5.4. Inoculation

After inoculation with the microorganisms, theprocess should start immediately and the reac-tion should proceed fast. The amount of active

42 Biotechnology

cell culture added is therefore critical and de-pends on the size of the batch. Scaling-up fromthe original starter culture to the inoculationbroth is done in several steps in large-scale in-dustrial processes (Fig. 19). The starter cultureis kept deep frozen (−80 to −90 ◦C) in sealedvials or in vials that were previously subjectedto lyophilization (a). They can be stored in thisway for several months or even years withoutany damage. For short-term storage at 4 ◦C, agarslants are prepared (g) and purity is tested onPetri dishes (h) before inocula are scaled up forproduction.

Figure 17. Stirred tank reactor (STR) equipped with flatblade turbine (FBT) and baffles for agitation and a spargerfor gas distributionThe ratio of H: D is usually 3 (max. 5).

For scaling-up, the liquid cell culture isplaced in liquid broth, first in test tubes (b), thenin small (c), and subsequently in large Erlen-meyer flasks (d) containing approximately 200mL ofmedium. Proliferation of the culture takesplace by shaking the flasks overnight at constanttemperature. Larger amounts of inocula are thenprepared in agitated and aerated fermenters ofvarious sizes depending on the volume of theproduction plant (e, f). In the early stages ofthis procedure (steps a through d) the inoculumis transferred under sterile conditions in gloveboxes.

In fungal inocula properwetting of the sporesis achieved by adding small amounts of surfac-tants to the broth. If inoculation by spare suspen-sions is not optimal, mycelial pellets can be usedfor start-up. Bacterial spores must be activatedby thermal treatment before they can be usedfor inoculation. During the exponential growthphase of the bioprocess cells can be harvestedfor following inoculations.

5.5.5. Operation Modes

Optimum production depends strongly on pro-cess layout. The standard process is still thebatch operation, which consists of loading, in-oculation, processing, harvest, and cleanup ofthe vessel. The advantages of this procedure areits adaptability to weekly work hours and smalllosses in case of contamination or decreases inproductivity.

Greater specific production canbe attained byusing semicontinuous or sequential-batch oper-ations. In this method only a partial stream ofthe broth is withdrawn after completion of thereaction (e.g., 90%) and new medium is addeddirectly into the remaining volume. This type ofoperation is also called cyclic continuous pro-duction. Modern vinegar production is operatedin the cyclic manner and maintained by fully au-tomated process control.

Fully continuous operation, that is, the con-stant flow of medium into a reaction vessel andthe simultaneous removal of an identical amountof the process solution, has shown the highestproduction rate. However, practical and biolog-ical constraints, especially the high contamina-tion risk, have so far prevented the general intro-duction of continuous processing into biotech-

Biotechnology 43

44 Biotechnology

←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−Figure 18. Various bioreactor configurations [112]a) Gas; b) Motor; c) Draft tube; d) Baffles; e) Mechanical foam breaker; f) CylinderA) Multistage flat blade turbine reactor (FBT) and baffles (d) making up the classical stirred tank reactor (STR)B) Forced agitation with draft tube (c) and ship propellerC) Combination of flat blade turbine and draft tube, operated with an overflow patternD) Combination of flat blade turbine and draft tube, floddedE) Simple vessel with hollow stirrer element for self-aeration and distributionF) Same as E, but with draft tube resulting in vortex flow patternsG) Torus reactor; annular form with ship propellerH) Tower with multistage flat blade turbinesI) Vibrating elements in towerJ) Pulsated towerK) Horizontal rotating cylinderL) Rotating disks on horizontal axisM) Rotating elements in a bathN) Compact loop reactor with gas – liquid separating element. Operated as completely filled vessel. Forced agitation withship propeller.

nology. Therefore, only processes that need notbe carried out under aseptic conditions are op-erated continuously such as sewage treatment,methane formation, and feed yeast or ethanolproduction from sulfite liquor.

The potential advantages of continuous cul-tivation are substantial because of the efficiencyof the process and the uniformity of the finalproduct. As a result, much effort is being madeto develop a sound basis of knowledge necessaryfor using this process strategy.

In contrast to large-scale industrial processescontinuous cultivation methods are widely usedin the laboratory [113 – 115]. The growth-controlling factor is either the concentration ofnutrients in the medium (chemostat system) orthe concentrationof insoluble biomass sensedbyoptical measurement (turbidostat methods). Thepurpose of these control systems is to maintaina constant concentration of nutrients, cells, andmetabolic products in the vessel (steady state).The kinetics of growth or product formation can

Figure 19. Preparation of inocula.The individual steps a through h are explained in the text

Biotechnology 45

be kept under precise control by selecting the ap-propriate dilution rate. All types of variations ofthe single-stage continuous cultivation are pos-sible, such as multistage arrangements, with orwithout recycling.

5.6. Process and Product Overview

The potential of biotechnology consists in itsability to replace classical chemical productionprocesses and to facilitate the production of newproducts. It is undisputed that, particularly in thearea of basic and fine chemicals production, theuse of biotechnological processes is meaningfulsince

– biotechnological processes are usually distin-guished by their high specificity (relating tothe conversion of substrates) and selectivity(relating to the product spectrum)

– biotechnological processes often use renew-able resources as raw materials, thus con-tributing to the much discussed sustainabilityof products and processes

– biotechnological processes can be carried outunder mild reaction conditions in terms ofpressure, temperature, and pH.

In view of these facts, in the past hundredyears a multitude of industrial biotechnologi-cal processes have been developed [116 – 120]whose efficiency exceeds that of chemical pro-cesses, and this has helped to establish them inthe long run (Table 5 and 6). One interestingarea is the production of fine chemicals. Theterm “fine chemicals products” refers to sub-stances that are highly functional and for whichworld demand is typically quantities of less than10 000 t/a. From a chemical point of view theseproducts are usually distinguished by havingseveral reaction centers and frequently by chi-rality. Classical syntheses of these substances in-clude several reaction steps using stoichiometricquantities of reagents and often deploy extrava-gant protective group chemistry, expensive no-ble metal/heavy metal catalysts, and drastic re-action conditions, e.g., aggressive solvents.Herebiocatalysis allows synthesis under considerablymilder reaction conditions in terms of pressure,temperature, and acidity. At present, due to theexcellent enantioselectivity of enzymes, whitebiotechnology methods are primarily used for

the production of chiral compounds. Straathofet al. [121] list 134 industrial biotransforma-tions. Just short of 90% of the products de-scribed are chiral fine chemicals. In future, thisfield will undoubtedly acquire greater impor-tance and new reaction sequences for the pro-duction of compounds with several stereo cen-tres will be established.

Also bulk chemicals are produced by fer-mentation or biotransformation processes. Bulkproducts mean products exceeding 10 000 t an-nually. It is expected that, by the year 2010, 6–12% of bulk products and polymers producedby chemical means will already be producedby biotechnological processes. High-volume,biotechnologically produced goods are to befound in the food, livestock feed, and the drinksand tobacco industries. A prime example of abulk product derived by an enzymatic processis acrylamide. Global biotechnological produc-tion capacities have continually expanded in thelast few years and are today probably in the re-gion of 100 000 t/a. Monomers and polymersproduced by biotechnological processes for theplastics and polymer industry are becoming in-creasingly interesting. Biotechnologically pro-duced polymers, such as polylactide (PLA), 1,3-propandiol (PDO) and poly-3-hydroxybutyrate-co-3-hydroxyhexanoate (PHBH) could providethe basis for innovations.

As for the energy industry, besides bioetha-nol two further products should be mentioned:biogas and hydrogen. Whereas the technologyfor biogas recovery is state-of-the-art, hydro-gen production is far more complex and requireslong-term research efforts. Biogas recovery is arecycling method for residues. The product canbe separated comparatively easily, which is cru-cial to the cost-effectiveness of the process.

6. Biocatalysis andBiotransformation

6.1. Introduction

Biocatalysis (also called biotransformation) isthe conversion of one substance (substrate) toanother (product) by a microorganism or an iso-lated enzyme. It is a chemical reaction catalyzedby a particular cellular enzyme or by an en-zyme originally produced within the cell. Most

46 Biotechnology

Table 5. Products obtained by fermentative processes of biotechnology [122 – 124]

Product/process Annual production t/a Price EUR/kg Market value 106 EUR Main applicationAmino acidsl-Glutamate 1500 000 1.20 1800 flavour enhancerl-Lysine 700 000 2 1400 animal feed additivel-Threonine 30 000 6 180 animal feed additivel-Phenylalanine 10 000 10 100 aspartame, medicinel-Tryptophan 1200 20 24 animal feed, nutritionl-Arginine 1000 20 20 medicine, cosmeticsl-Cysteine 500 20 30 (incl. extraction) food, pharmaOther amino acids andderivates

3000 pharmaceuticals, cosmetics,nutrition

AcidsLactic acid 150 000 1.80 270 food, leather, textilesGluconic acid 100 000 1.50 150 food, textiles, metal,

constructionCitric acid 1000 000 0.80 800 medicine, food, metal,

detergentsAcetic acid 190 000 0.50 95 foodItaconic acid 4000 co-monomerSolventsAcetone* 3000 000 – solvent1-Butanol* 1200 000 – solventBioethanol >18 500 000 0.4 solvent, basic chemical,

energy sourceBiomassStarter cultures 100 foodBaker’s yeast 1800 000 2300Mineral yeastEnzymesEnzymes (total) 1830Detergents 580 detergentsFood industry 500 starch degradation,

proteasesTextile, leather industry 250 tanningAnimal feed 170 phytases, proteasesFoodCheese 15 000 000 150 000Erythritol 30 000 2.25 67 sugar substituteDrinks and tobaccoBeer 138 000 000 2.5 drinks, tobaccoWine 27 766 000 drinks, tobaccoAntibioticsBacitracin A 4 3000 12 healing of woundsBacitracin A >200 <120 24 animal feed additiveVirginiamycin 70 250 17.5 hog feedingCyclosporin 3 5200 15.6 organ transplantationMonensin >3000 8 24 animal feed additivePenicillins 45 000 300* 13 500 medicine, animal feed

additiveCephalosporins 30 000 medicine, animal feed

additiveTetracyclins 5000 50 250Antibiotics (ca. 160 onmarket)

19 000

Other active substancesAcarbose 300 active substanceBiopolymersPolyhydroxyalkanoate packagingPolylactide 140 000 2.25 315 packagingXanthan 40 000 8.4 336 food, oil productionScleroglucan oil productionPullulan film former in foodstuff

applicationsDextran (derivatives) 2600 200** 520 blood substituteHyaluronic acid 500Polyglutamic acid

Biotechnology 47

Table 5. (continued)

Product/process Annual production t/a Price EUR/kg Market value 106 EUR Main applicationVitaminsRiboflavin (b2) 30 000 active substance, animal

feed additiveCyanocobalamin (b12) 20 25 000 500 active substance, animal

feed additiveVitamin C 80 000 8 640 food, animal feedl-Sorbose (vitamin Cprecursor)

50 000

Amino sorbiteLipidsPhytosphingosine cosmeticsAnimal feedGalactooligosaccharides 2500 3.50 9 prebioticsCosmeticsDihydroxyacetone suntan preparations

of these enzymes are necessary for the normalmetabolism and reproduction of the cell. In bio-catalysis, however, these enzymes are simplyused as catalysts for chemical reactions. In ad-dition to their natural substrates, many enzymescan utilize other structurally related compoundsas substrates and therefore may catalyze non-natural reactions upon addition of foreign sub-strates to the reaction medium. Thus, biocataly-sis or biotransformation is a specific category ofchemical synthesis.

6.2. Classification of Biocatalysts

Enzymes andwhole cells can be applied in a bio-transformation in many different forms. Usuallya whole cell biotransformation is a growth de-coupled process, so resting cells (harvested andwashed after fermentation) are employed. Thecells may be intact or permeabilized (e.g., forbetter substrate uptake). The different types ofbiocatalysts are:

– Whole or treated cells (e.g., lyophilized, per-meabilized)

– Organelles– Cell-free multienzyme systems– Combination of individual cell-free enzymes– Single cell-free enzyme

Isolated Enzymes may also be applied in dif-ferent forms. In most cases, it is not necessaryto purify an enzyme to homogeneity, a crudecell extract or partially purified solution mayalso be employed as biocatalyst. In some well-known technical enzyme preparations, severalisoenzymes are present (e.g., pig liver esterase).

In both whole cells and enzymatic biotransfor-mations the catalytically active species is theenzyme. Enzymes can be subdivided into sixclasses (classified by the Committee on EnzymeNomenclature of the International Union of Bio-chemistry) depending on the type of reactionthey catalyze (Table 7) and are listed numeri-cally in a catalogue published by the committee[125] (→ Enzymes).

6.3. History

Biocatalysis includes some of the oldest humanknown chemical transformations such as the useof yeast for baking or brewing or the use ofchymosin from the stomach of young cattle forcheese production. Records proving these an-cient technologies date back to about 7000 to6000 bc.

Another old andwell-known example is vine-gar production, which has been known since thedawnof recorded history. It is the oldest exampleof microbial oxidation (Fig. 20).

The process has evolved along traditionallines of fermentation without knowledge of theunderlying biochemistry. In 1862, Pasteur de-scribed the biochemical nature of vinegar pro-duction [126]. Subsequently Brown confirmedin 1886 that the oxidation of alcohol to aceticacid proceeded through acetaldehyde [127].Bacterium xylinum, which causes the reactionto proceed, was first described by Brown and isthe first microbial catalyst.

Today, the oxidation of ethanol to acetic acidby acetic acid bacteria is well understood; itproceeds via two successive dehydrogenations,

48 Biotechnology

Table 6. Biotechnology products obtained by biocatalysis and biotransformation [122 – 124]

Product/process Annual production t/a Price EUR/kg Market value 106 EUR Main applicationBasic chemicalsAcrylamide 100 000 1.40 28 –Amino acidsl-Aspartic acid 13 000 – aspartame productionl-Methionine 400 20 infusion solutionsl-Dopa 300 – active substancel-Alanine 500 – infusion solutionsd- and l-Valine 50 – –l-tert-Leucine 10 500 –l-Carnitine 200 – –β-Phenylalanine >1FoodGlucose 20 000 000 0.30 6000 liquid sugar,fermentation

mediumFructose sugar from HFCSIsoglucose, HFCS 8000 000 0.80 6400 liquid sugarl-Hydroxybutanedioic acid 100 20 acidifying agentPalatinite sugar substituteIsomalt 70 000 sugar substituteAspartame 10 000 – sweetening agentFructooligosaccharide(inulin)

10 500 2–3 prebiotics

Antibiotics (derivatives)6-APA 10 000 – –7-ACA 4000 – –d-4-Hydroxyphenylglycine 7000 – –Intermediate products(S)-2-Chloropropionic acid 2000 – herbicide synthesisd-Pantolactone 2000 – –(S)-Methoxyisopropylamineseveral thousands – herbicide synthesis

(Outlook after chiral switchby Frontier)

(R)-2-(4′-Hydroxyphen-oxy)propionic acid(R-HPOPS)

1000 – –

(S)-Phenethylamine etc.,optical active amines

500 – –

d-Mandelic acid >200 ca. 20 chiral auxiliaryEthyl (S)-4-chloro-3-hy-droxybutyrate

>150 – cholesterol-reducing drugs,e.g., Atorvastatin (Lipitor)

m-Phenoxybenzaldehydecyanohydrin

100 – –

(S)-3-Acetyl thioisobutyrate100 – –(-)-RAN 50 – –(R)-Glycidyl butyrate 50 –Active substance(precursors)Dilthiazem precursor 50 – –Nicotinamide 3000 – –Progesterone 200 – active substanceEphedrine 1500 60–90 pseudoephedrine, chiral

auxiliaryN-Acetylneuraminic acid sialidase inhibitor (e.g.,

Relenza)Special productsCyclodextrins 5000 10 50 domestic, food, stabilizersPantothenic acid 11 600 ca. 6 ca. 70

Biotechnology 49

which depend on the cytochrome system forelectron transfer to atmospheric oxygen, the ul-timate hydrogen acceptor:

Table 7. Classification of enzymes and their reactions

Class ReactionOxidoreductases: Enzymes catalyzing

oxidation–reduction reactionsinvolving oxygenation, such asC–H → C–OH, or overallremoval or addition of hydrogenatom equivalents, for exampleCH(OH) → C=O andCH–CH→C=C.Dehydrogenases, oxidases,peroxidases, hydroxylases, andoxygenases are involved in thisclass.

Transferases: Enzymes catalyzing the transferof various groups (e.g., aldehyde,ketone, acyl, sugar, phosphoryl)from one molecule to another.

Hydrolases: Enzymes catalyzing hydrolyticcleavage of C–O, C–N, C–C, etc.Esterases, amidases, peptidases,phosphatases, glycosidases, etc.are involved.

Lyases: Enzymes catalyzing cleavage ofC–C, C–O, C–N and other bondsby elimination, leaving doublebonds, or by adding groups todouble bonds.

Isomerases: Enzymes catalyzing a change inthe configuration of a molecule.Racemases and isomerases areinvolved.

Ligases: Enzymes catalyzing the joiningof two molecules with theaccompanying hydrolysis of ahigh-energy bond. Such enzymesare often termed synthetases.

Figure 20. Biotransformation of ethanol to acetic acid

C2H5OH+12O2→CH3CHO+H2O

CH3CHO+12O2→CH3COOH

Other historical examples of biocatalytic oxida-tion are the oxidation of glucose to gluconic acidby Acetobacter aceti [128] and the oxidationof sorbitol to sorbose by Acetobacter species[129]. A variety of such early reports on bio-catalytic conversions was collected by Plimmer[130]. Since then many other books have beenpublished on this topic.

In 1921, a microbial reaction for the stere-ospecific production of d-(−)ephedrine was de-scribed [131]. A yeast strain, cultivated to pro-duce acetaldehyde from glucose, was foundto condense benzaldehyde with acetaldehydeto form optically active l-phenyl-1-hydroxy-2-propanone. This product was in turn convertedchemically into d-(−)ephedrine (Fig. 21). Thisis an example of a successful combination ofchemical and biological reactions.

Figure 21. Chemoenzymatic synthesis of optically activel-phenyl-1-hydroxy-2-propanone and chemical conversioninto d-(-)ephedrine

Biocatalyis reached its present significancemuch later, when specific modifications ofsteroids (or sterols) by microorganisms werediscovered. Various steroid reactions, such ashydroxylation, epoxidation, dehydrogenation,isomerization, and hydrolysis, are performed bya wide variety of microbial enzymes. These in-clude the following important reactions: reduc-tion of androstenedione to testosterone by yeasts[132], hydroxylation of progesterone to 11-α-hydroxyprogesterone by Rhizopus arrhizus[133], 11-β-hydroxylation of C21-steroids byCurvelaria lunata [134], introduction of a ∆1-double bond into hydrocortisone by Corynebac-terium simplex [135], 16-α-hydroxylation of9α-fluorohydrocortisone by Streptomyces re-seochromogenes [136], and elimination of theside chain of cholesterol by Arthrobacter sim-plex [137]. Many novel intermediates for thechemical synthesis of new steroids becameavailable in this way.

In addition to the considerable practicalvalue, the amazingversatility ofmicroorganismsin the transformation of steroids provided an im-portant theoretical background for the applica-tion ofmicrobial enzymes to chemical synthesis.

50 Biotechnology

Many of these reactions cannot be achieved byconventional chemical synthesis.

Table 8. Advances in biocatalysis

Biocatalytic synthesis Example reactionsTransformation of steroids andsterols

hydroxylation (oxygenase),dehydrogenation(dehydrogenase), side-chaindegradation (oxygenase), etc.

Transformation of terpenoids hydroxylation of beta-iononewith monooxygenase fromBacillus negaterium [138].

Transformation of alkaloids oxidation of morphine to2,2′-bimorphin(pseudomorphine) byCylindrocarpon didymium [139];N-demethylation of codein tonorcodein by Mucus piriformis[140].

Synthesis of semisyntheticantibiotics

synthesis of semisyntheticpenicillins and cephalosporins(amidase).

Synthesis of organic acids hydration of fumaric acid(fumarase), diterminal oxidationof alkanes (oxygenase),asymmetric hydrolysis ofepoxysuccinic acid (hydrolase),etc.

Transformation of sugars isomerization of glucose (glucoseisomerase).

Protein synthesis synthesis of plastein (protease),semisynthesis of human insulin(protease), etc.

Synthesis of nucleic acid relatedcompounds

trans-N-ribosylation andtrans-N-arabinosylation(phosphorylase),phosphorylation of nucleosides(phosphotransferase),pyrophosphorylation ofnucleotides(pyrophosphotransferase),synthesis of sugar nucleotides(pyrophosphorylase), synthesisof coenzymes, etc.

Synthesis of amino acids asymmetric hydrolysis ofα-amino-ε-caprolactam, 2-ami-no-∆2-thiazoline-4-carboxylicacid, and 5-substitutedhydantoins (hydrolase),amination of fumaric acid(aspartase), synthesis ofl-tyrosine-related amino acids(β-tyrosinase),l-tryptophan-related amino acids(tryptophanase), etc.

Synthesis of amines decarboxylation of amino acids(decarboxylase).

Synthesis of industrial chemicals synthesis of alkene oxides(oxygenase), chiral alcohols andketones (dehydrogenase),pyrogallol (decarboxylase),amides (nitrile hydratase), etc.

Innumerable biocatalytic conversions involv-ing different types of reactions with organiccompounds and natural products were foundduring the following years; some of them arevery useful for chemical synthesis. Advances

in biocatalysis are summarized in Table 8. Al-though biocatalysis has become an importanttechnology for synthetic chemistry, it also hasa high potential for many fields of biotechnol-ogy.

6.4. Characteristics of EnzymeReactions Used in Biotransformations

Chemical reactions performed by biocatalysts(biotransformations) are essentially the same asthose carried out in inorganic or organic chem-istry.

Enzymes (or biocatalysts) used for microbialconversions increase the reaction rate by low-ering the activation energy as normal catalystsdo. The most striking difference between en-zymes and chemical catalysts, however, lies intheir substrate specificity. They catalyze specificreactions involving one or only a few structurallyrelated compounds and they distinguish almostabsolutely between stereoisomers or regioiso-mers. Therefore, only a very specific change ina functional group or bond of a compound is ac-celerated by the enzyme. As a result, one singleproduct is expected as long as only one enzymeis involved in a conversion.

In contrast to chemical reactions that often re-quire high activation energies (high temperature,pressure, etc.) biotransformations can be per-formed under considerable mild reaction con-ditions (Table 9). Due to the low energy in-put needed for biocatalytic processes and thehigh atom utilization (percentage of Mr of de-sired product to sum of Mr of all substrates),biotransformations are considered as an envi-ronmentally friendly alternative to classical or-ganic chemistry processes. To compare biocat-alytic and chemical processes on the basis of theamount of waste, Roger A. Sheldon [141] de-vised the “environmental quotient” (EQ), whichis the quotient of the amount of waste productper kilogram of product (E) and an “unfriendli-ness quotient” (Q): EQ = E × Q.

High catalytic efficacy is another character-istic property of enzymes. They increase theturnover rate without large energy requirementsand only a small amount of enzyme is neededto catalyze the conversion of a large amount ofsubstrate.

Biotechnology 51

Table 9. General characteristics of enzymatic and chemicalreactions

Parameter Enzymatic reaction Chemical reactionReaction conditions:Temperature physiological highPressure atmospheric highpH neutral differentReaction energy enzyme conformation

(van der Waals,hydrogen bonds, etc.)

thermal

Solvent water, (organicsolvents and ionicliquids under specialconditions possible,e.g., lowwateractivity)

water, organicsolvents

Specificities(substrate, stereo-,regiospecifity)

high low

Concentration ofsubstrate and/orproduct

low high

Table 10. Characteristics of biotransformation microbialconversion and fermentative production

Parameter Biotransformation Fermentativeproduction

Microorganism resting or treated cellsgrowing cellsReaction simple catalytic

reaction (one or fewsteps)

life process (multistepreaction)

Reaction time short longStarting materials expensive substrates cheap carbon and

nitrogen sourcesProduct natural and/or

unnaturalnatural

Product concentrationhigh lowProduct isolation easy tedious

As a result, enzymes exhibit their activ-ity under mild reaction conditions, such as at-mospheric pressure, temperatures around 20–40◦C, and pH values near neutrality, wherenormal chemical catalysts are inactive. On theother hand, enzymes cannot function under dras-tic reaction conditions because of their deli-cate protein structures. The unique propertiesof enzymes are extremely useful when unstablemolecules are to be converted without undesiredside reactions.

There are two ways for using biocatalysts inthe production of useful compounds: biotrans-formation and fermentation. Fermentation hasbeen used since ancient times (e.g., alcoholicfermentation) and is still of industrial impor-tance in the production of such compounds asorganic acids, solvents, or antibiotics. In contrastto biotransformation, fermentation is consideredto be a biologic method, because it results from

the actual life process of the microorganisms. Inother words, the reaction product results fromthe complex metabolism of the microorganismfrom cheap carbon and nitrogen sources. There-fore, living cells are required for fermentation,whereas this is not so important in biotransfor-mation. Fermentation products are always nat-ural products. Advantages and disadvantages ofthese two microbial processes are briefly sum-marized in Table 10. However, it is not alwayspossible to draw a clear line between the twocategories.

6.5. Types of Biocatalysts and ReactionSystems

Because biotransformations are essentially cat-alytic chemical reactions, a suitable catalyst forthe desired conversion must be prepared care-fully. Although conventional vegetative cell cul-tures are most commonly used, there are sev-eral alternative methods; some of these havealready been operated commercially. These al-ternatives have been evaluated extensively withregard to, for example, increased yield, controlof side reactions, simplified processes, and im-proved economy. They have great future poten-tial.

Various types of biocatalysts are useful formicrobial conversions (see Section 6.2. Anycombination of these systems may also be pos-sible. Several successful conversions are knownin which multistep reactions are catalyzed bydifferent catalysts (see Section 6.5.6).

6.5.1. Biotransformation with GrowingCultures

The substrate is added to the growth mediumduring inoculation or an appropriate phase ofgrowth. The preparation and inoculation of themedium, addition of substrate, and incubationare successively carried out in one flask untilthe reaction is completed. Because of its extremesimplicity, this procedure is frequently used notonly for screening (see Section 6.6), but also inlarge-scale production. Usually, the substrate isadded directly to the medium without steriliza-tion if the fully grown culture is the catalyst.Continuous chemostat methods may be used tomaintain a steady state of growth, enzyme levels,

52 Biotechnology

and substrate concentration. If high cell densi-ties are required by the conversion or if the na-ture of the growth medium causes problems forthe isolation of the product, the reaction shouldbe performed after separating the cells from themedium, see next section.

6.5.2. Biotransformation Conversion withPreviously Grown Cells

Themicroorganism is grown under suitable con-ditions and the cells are then collected by cen-trifugation or filtration. In this way, the cells re-tain most of their enzyme activity. For the con-version they are resuspended either in an in-complete medium (e.g., one without a nitrogensource; this prolongs cell viability and activityas well as possible cofactor regeneration) or in asimple reaction medium (e.g., a buffer solutionor water containing the substrate). The reactionis then carried out directly or after suitable treat-ment of the cells. It is theoretically not importantwhether the cells are dead or alive. Vegetative,washed, and dried cells fall into this category.The advantages of this method are listed below:

1) Growth and conversion steps are controlledindependently for optimization.

2) The grown cells can be stored for some periodof time until initiation of the reaction.

3) The cell density for the conversion is easilycontrolled.

4) The reaction can be carried out under non-sterile conditions.

5) Product isolation is usually easier becauseof the simple composition of the reactionmedium.

In some cases, however, the strict separationof microbial growth and conversion is consid-ered to be unfavorable for economic reasons.

Depending on the ability of substrate perme-ability through the cell membrane these biocat-alysts may have to be treated differently prior totheir use.

6.5.2.1. Vegetative or Washed Cells

In cases where the cell membrane is permeableto substrate and product, no special technique isneeded for the preparation of the cells and theycan be used as vegetative or washed cells.

6.5.2.2. Permeabilized Cells

In caseswhere substrate or productmolecules donot readily permeate the cell membrane, a mod-ification of the cell becomes necessary. Com-monly used techniques to change the permeabil-ity of the cell membrane to control the diffusionof substrate into the cell and of product out ofthe cell are: using surfactants, organic solvents,antibiotics or enzymes. Besides these chemicalmethods physical procedures, such as osmoticshock or freezing and thawing may also be use-ful [142]. The production of high-energy phos-phate esters, such as adenosine 5-triphosphate,nicotinamide adenine dinucleotide, flavin ade-nine dinucleotide, or coenzyme A, is a typicalexample [143].

6.5.2.3. Dried Cells

Dried cells are an alternative to permeabilizedcells.Drying causes structural changes in the cellwall or membrane. Contact between the cell en-zyme and the substrate is more readily achieved.In some cases, drying also eliminates undesiredside reactions, because some enzymes are dam-aged through drying. Dried cells are usually pre-pared by air-drying, lyophilization and acetonetreatment. The powdered dried cells can retaintheir enzyme activities in the frozen state foryears. They can be used as “instant catalysts”in the same way as normal chemical catalysts.

6.5.3. Biotransformation with Spores

Many fungal spores have as many useful en-zymes as vegetative or grown cells. The fungiare cultivated under conditions that induce highspore yields. The spores are then separated fromthe mycelium and used as catalysts in a similarway as mentioned in the previous section. Pro-cesses using spores have the same advantagesas those using grown cells. Spores are usuallymore stable than cells. Some types of spores canbe stored for several years without loss of con-version activity. They usually do not germinateduring the reaction, but in some cases germina-tion is required for maximum conversion activ-ity. The 11-α-hydroxylation of progesterone by

Biotechnology 53

Aspergillus ochraceus spores represents a suc-cessful spore process [144].Many other applica-tions have been reported, e.g., fatty acids conver-sions, triglycerides, carbohydrates, or penicillinV [145].

6.5.4. Biotransformation with ImmobilizedCells

Immobilized cells are cells that have either beenfixed or bound to a surface, entrapped in a gelmatrix, or retained in a membrane reactor. All ofthe above-mentioned cells can be immobilized.Numerous immobilization methods have beenreported; they can be divided into the followingfour groups:

1) Entrapment or encapsulation in a porous poly-mer network, e.g., polyacrylamide, alginate,κ-carrageenan, gelatine, agar, collagen, cellu-lose, polyurethane, poly(vinyl alcohol).

2) Covalent, ionic, or physical attachment toan appropriate water-insoluble solid support,e.g., ion-exchange resins, silica gels, metaloxides.

3) Aggregation of cells by physical or chemicalcross-linking with glutaraldehyde, polyethyl-eneimine, or other agents.

4) Retention of cells by membranes (microfilti-ration, cutoff 0.2–2µm), e.g., in formofmem-brane reactors or hollow fiber modules [153].

In successful cases, the immobilized cellsretain their activities over several months andshow a higher operational stability than unsup-ported cells. Additional advantages are: 1) easyremoval of the cells from the reaction mixture,2) repeated use of the cells, 3) continuous op-eration of the reaction, 4) cell regeneration byimmersion into an appropriate nutrient medium,and 5) easy product isolation. Some of these fea-tures are especially advantageous if the collec-tion of a sufficient cell mass for the conversionis costly.

However, disadvantages must also be consid-ered: 1) The catalytic activity of cells usuallydecreases during the immobilization procedurebecause the cells are partly damaged and perme-ability is further inhibited. This decreased activ-ity can be compensated by increasing the celldensity. 2) The immobilization itself sometimesrequires special equipment. The conversion step,especially when operated continuously, also re-quires a special engineering design compared toconventional conversion processes. Examples ofsuccessful large-scale applications are listed inTable 11.

An advance in this field was the introduc-tion of an immobilized system of growing cells

Table 11. A selection of commercial applications of immobilized biocatalysts: microbial cells or enzymes

Microorganism orimmobilized enzyme

Methods of immobilization Applications Year of introduction Ref.

l-Amino acylase fromAspergillus oryzae

adsorption toDEAE-Sephadex

optical resolution ofracemic amino acids

1969 [146]

Escherichia coli (aspartase) entrapment inχ-carrageenan gel

continuous production ofl-aspartic acid fromammonium fumarate

1973 [147]

Brevibacteriumammoniagenes (fumarase)

entrapment inχ-carrageenan gel

continuous production ofl-malic acid from fumaricacid

1974 [148]

Escherichia coli (aspartase)and Pseudomonas dacunhae(aspartate -decarboxylase)

entrapment inχ-carrageenan gel

continuous production ofl-alanine from ammoniumfumarate

1982 [149]

Penicillin acylase fromEscherichia coli

entrapment in a cellulosetriacetate matrix

production of6-amino-penicillanic acidfrom penicillin G

1973 [150]

Glucose isomerase fromBacillus coagulans orStreptomyces species

many methods available production of high-fructosesyrup from glucose

1973 [151]

β-Galactosidase fromLactobacillus bulgaricus,Aspergillus oryzae, etc.

many methods available production of low-lactosemilk and production ofsweetenings

1977 [152]

Whole cells Arthrobactersp. (oxygenase)

retention by microfiltrationin cross-flow membranereactor

production of muconic acid(raw material for newresins, pharmaceuticals andagrochemicals)

[153]

54 Biotechnology

into a conversion process. For example, cells en-trapped in -carrageenan are viable and reproducewithout loss of activity per unit cells [154]. Thecontinuous production of ethanol from glucoseby S. cerevisiae in κ-carrageenan [155] and the∆1-dehydrogenation of cortisol byArthrobactersimplex in calcium alginate [156] are examplesof such successful operations. Further improve-ment of this technique is now expanding into theapplication to complexmultistep reactions, suchas the production of antibiotics.

6.5.5. Biotransformation with Cell-freeEnzymes or Purified Enzymes

In general, the use of cell-free enzymes or puri-fied enzymes for conversions is expensive, be-cause purification of enzymes is often tediousand time-consuming. Therefore, they are notso suitable for large-scale industrial production.However, there are several cases in which theyare advantageous over systems using cells. Suchcases are:

1) Poor substrate or product permeabilitythrough the cell wall

2) Problems caused by undesired side reactions3) Commercial availability of the desired en-

zyme at an acceptable price

If an extracellular enzyme is used, cells arenot involved at all.

Cell-free enzymes can also be used as im-mobilized catalysts [157]. The same techniquesas those used for cell immobilization are avail-able. Immobilization of penicillin acylase [150],l-amino acid acylase [146], lactase [152], andglucose isomerase [151] have been applied tocommercial production. The entrapment of sol-uble enzymes in a membrane-type reactor withsimultaneous coenzyme regeneration is an alter-native to the use of cell-free enzymes. l-Aminoacid dehydrogenases in combination with for-mate dehydrogenase as a coenzyme regenera-tor and nicotinamide adenine dinucleotide cova-lently bound to polyethylene glycol in a mem-brane reactor have been successfully applied tothe continuous amination of keto acids yieldingoptically active amino acids [158]. l-Leucine isalready produced commercially by this process.

Whitesides [159] has reviewed the potentialapplications of cell-free enzymes in organic syn-thesis.

6.5.6. Multistep Reactions Using DifferentBiocatalysts

In order to performmore than two sequential re-actions, two or more different biocatalysts aresometimes required either simultaneously or se-quentially. Theoretically, any combination ofcatalysts can be used for such multistep reac-tions. Examples of successful applications are:continuous production of l-alanine from ammo-nium fumarate via L-aspartate as the interme-diate by immobilized Escherichia coli (aspar-tase) and Pseudomonas dacunhae (l-aspartateβ-decarboxylase) cells [149], complete conden-sation of racemic homocysteine by washed ordried Pseudomonas putida (racemase) and Al-caligenes faecalis (S-adenosylhomocysteine hy-drolase) cells [160], complete hydrolysis ofracemic α-amino-ε-caprolactam (see Section6.6.2.1), and continuous amination of α-ketoacids to l-amino acids by two types of cell-freeenzymes (see Section 6.5.5).

Another remarkable example is the synthe-sis of coenzyme A from pantothenic acid, l-cysteine, and ATP (or AMP), which proceedsthrough five sequential steps (see below) and iscommercially performed solely with Brevibac-terium ammoniagenes cells [161, 162].

Step 1: pantothenic acid + ATP →phosphopantothenic acid + ADP

Step 2: phosphopantothenic acid +l-cysteine + ATP →phosphopantothenoyl-l-cysteine+ ADP + inorganic phosphate

Step 3: phosphopantothenoyl-l-cysteine→ phosphopantetheine + CO2

Step 4: phosphopantetheine + ATP →dephosphocoenzyme A +inorganic pyrophosphate

Step 5: dephosphocoenzyme A + ATP→ coenzyme A + ADP

6.5.7. Multiphase Reaction Systems

Since their discovery, enzymes have beenthought to be inert catalysts in organic solvents.This is basically true, but enzymes have becomeavailable that are catalytically active in water-miscible organic solvents. Many reactions thatare impossible in water because of kinetic orthermodynamic reasons can be performed in or-ganic solvents [163, 164] (e.g., transesterifica-tion reactions [165]). Chemically modified per-oxidase exhibits 21% of its activity in benzene

Biotechnology 55

as compared to that of the unmodified enzymein aqueous solution [166]. Polyphenol oxidaseconverts nearly 100% of phenol to catecholwhen the reaction is carried out in chloroformcontaining only 1–2%water [167]. Such liquid–liquid two-phase systemswith lowwater contentcan also be used to shift the hydrolytic equi-librium toward water elimination. The synthesisof esters, amides, and peptides by this methodshows great practical potential.

Multiphasic conversion systems usingmicro-bial cells as catalysts have also been extensivelystudied, especially in the case of lipophilic com-pounds, which have a limited solubility in water.In some cases, increased reaction rates and/orproduct yields have been achieved.

6.6. Process Design

6.6.1. General Considerations

When designing a biocatalytic process manyimportant aspects require careful consideration.Besides the essential question of the type of en-zyme used for biocatalysis there is the selectionof a compound to be synthesized and a survey onavailable substrates and routes or reactions thatare of special importance in designing a conver-sion process (also see Chapter 5).

6.6.1.1. Evaluating Enzyme Potential

When a new conversion or a new enzyme use-ful to biocatalytic conversions is discovered,the question as to the potential of the new en-zyme for practical purposes must be answeredfirst. An example for a high-potential biocatal-ysis with practical application in the phar-maceutical industry is l-phenylalanine dehy-drogenase (PheDH, EC 1.4.1.20) from Ther-moactinomyces intermedius for the conversionof ketoacid acetals to acetal amino acids [168](Fig. 22). The amino acid acetal is a precur-sor for Omapatrilat r©, an antihypertensive drug,which inhibits the angiotensin-converting en-zyme (ACE) and neutral endopeptidase (NEP).The synthesis is carried out in 16,000 L batchreactions with heat dried E.coli cells containingthe cloned andoverexpressedPheDHfromTher-moactinomyces intermedius and the formate

dehydrogenase from C. boidinii for cofactor re-generation.Yield (97%), conversion (97%), andoptical purity of the product (ee> 99%) are veryhigh.

Figure 22. Synthesis of acetal amino acid (Bristol-MyersSquibb)

Another interesting example is the produc-tion of l-tyrosine and l-dopa (dihydroxyphe-nylalanine) by bacterialβ-tyrosinase [169]. Thisenzyme has long been known to catalyze the α,β-elimination of l-tyrosine to phenol, pyruvate,and ammonia (Eq. 1).

Because the enzyme also catalyzes the reversereaction (Eq. 2) and the β-replacement reactionof l-tyrosine with phenol derivatives (Eq. 3), itdid not take long for the application of these re-actions in the synthesis of l-tyrosine and l-dopa.

56 Biotechnology

Subsequently, similar reactions were foundto be catalyzed by several pyridoxal phos-phate dependent enzymes, such as trypto-phanase, cysteine desulfhydrase, and 3-chloro-d-alanine chloride lyase. These enzymes werethen successfully used to synthesize l-trypto-phan and 5-hydroxy-l-tryptophan, l-cysteine,and d-cysteine [169 – 171] (see Table 12). Thesereactions can be summarized as follows:

L (D) −RCH2CH (NH2)COOH+H2O

→RH+CH3COCOOH+NH3

L (D) −RCH2CH (NH2)COOH+R′H→L (D) −R′CH2CH (NH2)COOH+RH

R′H+CH3COCOOH+NH3

→L (D) −R′CH2CH (NH2)COOH+H2O

where for β-tyrosinase R = hydroxyphenyl, –OH, –SH, –Cl and R′ = hydroxyphenyl; for tryp-tophanase R = indolyl, –OH, –SH, –Cl and R′= indolyl; for cysteine desulfhydrase R = –SH,thiol radicals, –Cl and R′ = –SH, thiol radicals;and for 3-chloro-D-alanine chloride-lyase R =–Cl, – SH, thiol radicals and R′ = –SH.

These successful examples demonstrate theimportance of reassessing the capabilities ofwell-known enzymes or reactions.

Table 12. Synthesis of l-tyrosine, l-tryptophan, l-cysteine, d-cysteine, and related amino acids by pyridoxal phosphate-dependentenzymes

Product Yield, Substrates Reaction

Enzymeg/L mol% Microorganism

l-Tyrosine 58 88 a sodium pyruvate reverse of α,β-eliminationammonium acetate β-tyrosinasephenol Erwinia herbicola

l-Tyrosine 53.5 78 b d,l-serine β-replacementammonium acetate β-tyrosinasephenol Erwinia herbicola

l-Dopa 58.5 f sodium pyruvate reverse of α,β-eliminationammonium acetate β-tyrosinasepyrocatechol Erwinia herbicola

l-Dopa 53 71 c d,l-serine β-replacementammonium acetate β-tyrosinasepyrocatechol Erwinia herbicola

l-Tryptophan1 00 100 d sodium pyruvate reverse of α,β-eliminationammonium acetate tryptophanaseindole Proteus rettgeri

5-Hydroxy-l- 23.3 57 e sodium pyruvate reverse of α,β-eliminationtryptophan ammonium acetate tryptophanase

5-hydroxyindole Proteus rettgeri

l-Cysteine 50 88f 3-chloro-l-alanine β-replacementsodium sulfide cysteine desulfhydrase

Enterobacter cloacae

d-Cysteine 22 88g 3-chloro-d-alanine β-replacementsodium hydrogensulfide 3-chloro-d-alanine chloride

lyasePseudomonas putida

a Based on sodium pyruvate.b Based on d,l-serine.c Based on indole.d Based on 5-hydroxyindole.e Based on 3-chloro-l-alanine.f Based on 3-chloro-d-alanine.g Sodium pyruvate was added at 2-h intervals to maintain a concentration of 5 g/L for 48 h.

Biotechnology 57

6.6.1.2. Finding Suitable Enzymes

Occasionally a synthetic scheme has been de-signed but suitable enzymes or conversion pro-cesses are yet unknown. In such a case, it be-comes necessary to assay suitable microbialstrains or enzymes (as described in Section6.6.2) as to their suitability for catalyzing the de-sired reaction. The production of l-lysine fromd,l- α- amino- ε-caprolactam (ACL) is anothertypical example [172]. This process was de-signed to utilize cyclohexene, amajor byproductof nylon production. In this process, the biocat-alyst is required to hydrolyze only the l-formof α-amino- ε-caprolactam (l-ACL) to yield l-lysine (Fig. 23).

Figure 23. Hydrolysation of the l-form of -amino- -capro-lactam (l-ACL) to yield l-lysine

Two microorganisms were found during theassay: one of them hydrolyzes the l-form of thesubstrate and the other racemizes the remainingd-form. As a result, all the α-amino- ε-capro-lactam is converted into l-lysine without requir-ing any optical resolution.

6.6.1.3. Substrates

Any substrate is theoretically suitable forbiotransformations, provided the substratemolecules come into contact with the enzyme.Even gases, such as methane, are suitablesubstrates; they are bubbled into the reactionmedium. The substrate should be soluble in themedium and able to pass through the cell mem-brane unless the reaction is catalyzed by cell-free enzymes. The substrate is usually added tothe reaction medium, neat or as a concentrated

solution. Sterilization of the substrate is recom-mended prior to its use, if required.

Only dissolved substrate is converted. There-fore, slightly soluble substrates must first be dis-solved and the products may then crystallizefrom the solution after conversion:

Substrate (solid) � Substrate (solution)

� Product (solution) � Product (solid)

For example, powdered cortisol in concen-trations up to 500 g/L is converted to crystallineprednisolone with a good yield by Arthrobactersimplex [173]. Substrates may also be dissolvedin water-miscible organic solvents. The loweralcohols, acetone, dimethylformamide, and di-methylsulfoxide are suitable solvents. Surfac-tants may also be used to disperse the substrate.The combination of water-miscible solvents andsurfactants may offer some advantage. In somecases, chemical modification of the substratemay improve its solubility.

An example for a process using substratewith poor solubility is the regioselective oxida-tion of the tert-butyl group of terfenadine us-ing Cunninghamela blakesleana in a whole-cellbiotransformation [174]. Due to its low solu-bility in water, microcrystalline terfenadine issolved in the water-miscible organic solventdimethylfomamide prior to its addition to theaqueous reaction medium. This regioselectiveoxidation was studied as a biocatalytic alterna-tive for the chemical synthesis of the antihis-taminic drug fexofenadine.

In cases where the cell membrane is imper-meable to the substrate or a cell-free or puri-fied biocatalyst is required, the permeability ofthe cell must be improved, respectively, the cellwall has to be disrupted. This is accomplished byair drying, acetone drying, lyophilization, autol-ysis, lysozyme digestion, surfactant treatment,osmotic shock, freezing and thawing, or ultra-sonic treatment. For cell-free preparations, thecell debris can be removed by centrifugation ormicrofiltration.

If the substrate does not suit the desired con-version, its chemical modification should beconsidered. Addition, variation, or removal ofa protecting group or modification of a func-tional group in the substrate molecule some-times makes its interaction with the enzymemore efficient. In addition, these modificationsmay prevent undesirable side reactions or degra-

58 Biotechnology

dation. This method is frequently used in steroidconversions. A systematic assay for a suitablechloroacetoacetate ester for enantioselective re-duction yielding the l-form of 4-chloro-3-hy-droxybutanoate, a promising precursor for thechemical synthesis of l-carnitine, is an example[175].

6.6.1.4. Media

Besides performing biocatalysis in aqueous me-dia, there has developed the so-called “non-aqueous enzymology”which has become an im-portant area of research and development dur-ing the last decades [176]. Enzymes exhibit awide array of novel reactivities and selectivi-ties in nonaqueous solvents. For example, manyreactions that are impossible in water due tokinetic or thermodynamic reasons can be per-formed in organic solvents, [163, 164, 177] dueto the suppression of water-induced side reac-tions. Improved and altered substrate specifici-ties [178, 179] and selectivities can be observed.Examples of practical applications are enantios-elective synthesis [180], chiral resolution [181],and combinatorial biocatalysis [182]. The pos-sibility of the solubilization of hydrophobic sub-strates or products in organic solvents opensopportunities for the enzymatic production ofpoorly water-soluble fine chemicals and phar-maceuticals. The thermal and storage stability ofenzymes can be significantly enhanced in non-aqueous media [164, 176, 178].

6.6.2. Selection of Biocatalysts

More than 8000 enzymes are known [125]. Al-most all types of chemical reactions which arealso catalyzed by normal chemical catalysts areinvolved. According to the desired type of con-version and the classification of the enzymes onecan easily determine which enzyme would besuitable for the desired conversion by inspectingthe enzyme catalogue published by the commit-tee [125]. However, the properties of enzymesfrom different sources may vary widely, even ifthey catalyze the same reaction. Therefore, test-ing is indispensable. A wide variety of biocata-lysts are tested for the purpose of determiningtheir ability to carry out the desired reaction.

Such a test, also called screening, may be oneof the most important steps for a successful bio-catalytic conversion.

6.6.2.1. Screening

Kitahara and co-workers [183] screened bac-terial strains from cultures grown aerobically at30–37◦C in nutrient broth containing malt ex-tract. Sodium fumarate and ammonium chloridewere added 24 h after growth started. After in-cubation for another 24 h the l-aspartate in theculture broth was analyzed by chromatography.A strain of E. coli was selected by this screen-ing as the most promising producer of aspar-tase. Under suitable reaction conditions, 56 gof l-aspartate was produced per 100 mL with amolar yield of 99% by using 1 g of dried cells.Subsequently, this process was developed intolarge-scale technology bywhich l-aspartate wassynthesized continuously with E. coli cells im-mobilized on κ-carrageenan [184].

The screening procedure described abovemay be rather simple, but it reflects the essenceof screening. Normal screening procedures canbe more complicated and deal with a great vari-ety of microorganisms, including bacteria, acti-nomycetes, molds, yeasts, and basidiomycetes.

6.6.2.2. Enrichment

Anothermethod to find a suitable biocatalyst canbe enrichment. Sometimes it is desired or nec-essary to find biocatalysts capable of perform-ing a specific conversion from natural sources.Soil samples containing mixed microbial popu-lations are rich sources of test organisms. Be-cause the enzymes in these microorganismsmodify or degrade a great variety of complexorganic compounds, at least one of the mi-croorganisms is expected to offer one or severalenzymes that perform the desired conversion.Thesemicroorganisms are selected using the en-richment technique. A typical enrichment proce-dure is the isolation of bacterial strains that per-form the asymmetric hydrolysis of d,l-2-ami-no- ∆2-thiazoline-4-carboxylic acid (d,l-ATC),an intermediate of d,l-cysteine synthesis, to l-cysteine [185]. Soil samples were incubated in amedium containing 0.3% D,L-ATC as the sole

Biotechnology 59

nitrogen source. After spreading each cultureon a plate of the same medium, colonies thatappeared were isolated and grown on nutrientagar containing 0.2% d,l-ATCas the enzyme in-ducer. The resulting cells were then transferredto a solution containing 1% d,l-ATC and theformation of l-cysteine was demonstrated bychromatographic or microbiological methods.Among 1975 colonies from 388 soil samples,31 l-cysteine-producing bacteria were isolated.One of them, Pseudomonas thiazolinophilum,was mutagenized. Mutants with inhibited l-cysteine metabolism were screened from themutagenized cultures.One of thesemutants con-verted the added substrate almost stoichiomet-rically to l-cysteine with a yield of 31.4 mg/mL[186].

The enzyme system of Pseudomonas thia-zolinophilum hydrolyzes d,l-ATC in two stepsas shown in Figure 24.

Figure 24. Asymmetric hydrolysis of d,l-2-amino- 2-thiazoline-4-carboxylic acid (d,l-ATC) to l-cysteine

Although screening sometimes is regardedas old-fashioned, time-consuming, and even il-logical, some consider it to be one of the mostpromising methods for finding new genes in mi-croorganisms.

6.6.2.3. Molecular Engineering

Sometimes enzyme yields in wild-type organ-isms are low or the desired enzyme is not foundin the available microorganisms. In this case,molecular engineering techniques can lead to anenzyme catalyzing the desired reaction. Thereare two different strategies that represent the“state-of-the-art” technologies: rational designand directed evolution [153]. Both technologies

require different knowledge of the target enzymeas well as laboratory equipment (Table 13).

Table 13. Requirements and molecular methods for rational designand directed evolution technique

Rational design Directed -evolutionRequirements: 3D structure of the

target enzymehigh-throughput-screening/selectiontechnology to analyzeenzyme properties

knowledge of reactionmechanismmodeling ofenzyme-substrateinteraction

Molecular methods: site-directedmutagenesis

random mutagenesis

6.7. Improvement of ConversionProcesses

After an adequate process has been designed, itmust be optimized. This includes improvementof the strain and the search for optimal growth(highest enzyme yield and maximum activity)and optimal reaction conditions.

If the enzyme to be used is constitutive, theamount of biomass containing that enzyme willhave to be increased. This is usually sufficientbecause the constitutive enzyme is formed in-dependently of the medium composition. How-ever, if the enzyme is inducible, an effective in-ducer, which may be the substrate itself or an-other structurally related compound, will be re-quired. If the enzyme is repressive, the repres-sion factorwill either have to be eliminatedor therepressive compounds, which may be present inthe growthmediumor formedduring the growth,must be removed.

Catabolites of an easily metabolizable car-bon source, e.g., glucose or sucrose, and theend product of a metabolic pathway usuallycause catabolite or product repression, respec-tively. Therefore, the composition of the growthmedium, growthperiod, andvarious physical pa-rameters of growth, such as temperature, aera-tion, agitation, and pH, must be carefully con-trolled. In successful cases, the concentration ofthe required enzyme reaches more than 10% ofthe total soluble protein [187].

Optimal physical and chemical conditions forthe biotransformation itself must be determinedwith the same care as for the optimization ofgrowth conditions. This is especially important

60 Biotechnology

if side reactions are observed, because they de-crease product yield and make product isolationdifficult. Physical or chemical treatment of thebiocatalyst, such as heating, pH change, and theaddition of detergents, organic solvents, or spe-cific inhibitorsmay cause specific inactivation ofundesired enzymes. For example, the trans-N-arabinosylation of adenine from synthetic uracilarabinoside (Fig. 25) yields adenine arabinoside,an antiviral agent.

Figure 25. trans-N-arabinosylation of adenine from syn-thetic uracil arabinoside

This reaction is catalyzed by cells of En-terobacter aerogenes and takes place only attemperatures above 60◦C [188]. The reactiondoes not proceed below 50◦C because hypo-xanthine is formed by adenine deaminase be-fore the trans-N-arabinosylation by two kindsof nucleoside phosphorylases takes place. For-tunately, these phosphorylases are still active at60◦C, whereas the deaminase is completely in-activated at this temperature.Other examples aresummarized in Table 14.

In the case of immobilized biocatalysts, con-centration gradients of substrates and productsmay occur that could lead to decreased biotrans-formation rates. These gradients can be reducedby decreasing the Thiele modulus, e.g., by us-ing smaller carrier particles or by reducing thediffusion distance between the free solution andthe immobilized enzymes.

Microbial conversion can also be optimizedby improving the selected strain. Standardmeth-ods of strain improvement include the isolationof single colonies, optionally after mutagenesis,and screening of the best suited isolates. Suchconventional genetic manipulation techniqueshave been developed during the last decades forthe improvement of useful strains. RecombinantDNA technology is providing new prospects forstrain improvement. Plasmid or phage vectorscan transfer DNA fragments of microbial, plant,animal, or even synthetic origin into a suitablerecipient microorganism. These gene cloningtechniques are still restricted tomicrobial strainssuch as E. coli, B. subtilis, and S. cerevisiae, inwhich the genetic background and the appropri-ate host–vector systems arewell known, but theirpractical application is rare [191]. However, thistechnology will undoubtedly provide one of themost important keys for the future developmentof microbial conversions.

6.8. Conclusion and Outlook

Biocatalysts have become one of the most im-portant tools in nearly all fields of industrialbiotechnology. The main advantages of usingbiocatalysts are their unique properties such ashigh chemo-, regio-, and stereoselectivity andthe ability to catalyze reactions under mild con-ditions.

During the last couple of years, many newmethods and techniques have been developedto overcome common problems. Some exam-ples here might be molecular engineering tech-niques such as rational design or directed evo-lution to increase low biocatalytic activities[153], biocatalysis in nonaqueaous reaction me-dia to bypass solubility problems of substrateand/or product [179], or the applications of high-throughput screening methods for the searchof new and suitable biocatalysts. Another fieldof interest that will become of great impact is“metagenomic research” that will enable scien-tists to find new biocatalysts with desired quali-ties [192].

To make new biocatalysts attractive for in-dustrial applications they have to be availablein sufficient amounts. High expression rates ofenzymes can be achieved by genetical and phys-iological manipulations of the expressing mi-

Biotechnology 61

Table 14. Elimination of side reactions by physical or chemical treatment

Desired reaction Side reaction Treatment ReferenceAmmonium fumarate → l-aspartic acid(Escherichia coli/aspartase)

fumaric acid → l-malicacid

incubation of culture broth at pH 5and 45 ◦C for 1 h

[149]

Fumaric acid → l-malic acid (Brevibacteriumammoniagenes/fumarase)

fumaric acid → succinicacid

treatment of immobilized cells with0.6% bile extract

[148]

l-Aspartic acid → l-alanine (Pseudomonasdacunhae/aspartate β-decarboxylase)

l-alanine → d-alanine incubation of culture broth at pH 4.75and 30 ◦C for 1 h

[149]

Cholesterol → 1,4-androstadiene-3,17-dione(Arthrobacter simplex/multistep conversion)

further decomposition ofthe sterol molecule

addition of α,α′-dipyridyl (1 mM) tothe culture broth

[137, 189]

β-Sitosterol → 17-ketosteroids (Nocardiaspecies/multistep conversion)

further decomposition ofsteroid ring

addition of α,α′-dipyridyl (0.3 mM)to the conversion mixture

[190]

d,l-2-Amino-∆2-thiazoline-4-carboxylate →l-cysteine (Pseudomonasthiazolinophilum/multistep conversion)

further degradation ofl-cysteine

addition of hydroxylamine orsemicarbazide to the reaction mixture

[186]

croorganism such as optimizing the number ofplasmids per cell or inactivation of “negative”cell functions (e.g., deletion of protease codinggenes).Apart fromdeleting unwanted side activ-ities also novel metabolic pathways can be im-plemented. These microorganisms, customizedonly to fulfill special needs for biocatalytic ap-plications, are called “designer bugs” or “tailor-made microorganisms”.

7. Downstream Processing

Downstream processing is one of the most un-derestimated steps in bioprocesses and it is wellknown especially in the pharmaceutical industrythat downstreaming is the most expensive andunfortunately the most ineffective part of a bio-process. Thus, one can assume that new devel-opments are widely described in the literature.Unfortunately, this is not the case. Only a fewworking groups focus on new andmore effectiveprocedures to separate products from fermenta-tion broths or biotransformations. A chief char-acteristic of biotechnology is the wide varietyof products. Due to this variety, a broad spec-trum of separation techniques must be applied.However, for nearly all products one starts witha dilute suspension and tries to produce a highlypurified dry product. In the case of extracellularproducts, the solids in this suspension may in-clude intact organisms, other insoluble fractionsof themediumor natural sample, and perhaps in-soluble products. Concerning intracellular prod-ucts the solids include in addition fragmented

mycelia caused by cell disruption, which is nec-essary to gain the products. According to thisstarting point, nearly each downstream processconsists of the four following steps [193]:

– Removal of insoluble particles– Isolation of the product– Purification– Polishing

With respect to downstreaming in laboratoryscale, one is normally only limited by the avail-able equipment.However,with regard to upscaleprocesses one should have in mind that the de-veloped process should also be applicable in in-dustry. Thus, Belter et al. [193] have formulatedthe following questions, which are also crucialto biotechnology downstream processes:

– What is the value of the product?– What is the acceptable product quality?– Where is the product in each process stream?– Where are the impurities in each processstream?

– What are the unusual physicochemical prop-erties of the product and the principal impu-rities?

– What are the economics of various alternativeseparations?

In addition, it is important to use materialswhich are available for all upscale processes,since the downstream behavior of the productmay change while changing, for example, thetype of chromatographic resin or membranema-terial. Normally the recovery costs exceed thebioprocess costs; however, some upstream pa-rameters influence the downstream processing:

62 Biotechnology

– The characteristic properties of the producingmicroorganism or cell line

– The location of the product– The stability of the product– Byproducts and impurities– Concentration of the product in the mediumfrom which it is to be recoveredAgeneral downstream schemebased on these

parameters is given in Figure 26.

Figure 26. General downstream scheme in biotechnology

The main aims of the primary separation stepare to achieve a volume reduction and to make a

first stage purification of the product by remov-ing dissimilar components from the broth. Themost important techniques in use are:

– Membrane processing– Ion exchange chromatography

In the membrane process, ultrafiltration andreverse osmosis are often used for separation,concentration, and desalting. In addition, po-lar membranes are used for ion exchange anddesalting. Ion exchange chromatography is ap-plied in order to remove either major contami-nants from the broth or the desired product fromthe broth. Chromatographic procedures – basedon columns or membranes – are also often usedin the purification step. Other techniques in thispart of the downstream process are precipitationand liquid–liquid extraction.

7.1. Sample Disruption

The influence of the sample disruption step inupstream and downstream processes cannot beignored. The disruption is dependent on theproperties of the sample. The main focus ofthe disruption is always to release as much aspossible of the product. However, depending onthe mechanism, parts of the product may be de-stroyedduring the disruption process. Thus, veryeffective and short procedures are required. Adistinction can be made between mechanical,chemical, and enzymatic procedures for productrelease. Mechanical methods are often preferredbecause of short residence time, lower operatingcosts, and contained operation [194]. The mostcommon mechanical means of disruption are:

– Homogenizers [195, 196]– Bead mills [196].

A homogenizer consists of a positive-displacement pump, which supplies the liquidsample at high pressure through a small nozzleor an orifice valve. The disruption results fromthe combination of shear force and impingementon the valve. Bead mills use horizontal grindingchambers filled with glass beads or other resis-tant materials such as zirconium oxide, zirco-nium silicate, titanium carbide, etc [197]. Thedispensed sample is introduced into the grind-ing chamber on a continuous basis. The level of

Biotechnology 63

disruption depends on the variable turning speedof the bead mill.

By using bead mills, cell disruption can beachieved in a single run with better temperaturedistribution and temperature control. Anotherpossibility for cell disruption or at least productliberation is the use of microwaves or ultrasoundwhich can be combined with sample extractionby organic solvents. Microwave techniques arewidely used in acid digestion of solid samples.Their use in the extraction of organic analytesfrom environmental samples is less widespread,despite the availability of commercial devicesfor this purpose and their potential for reducinganalysis time and solvent consumption.

A possibility for cell lysis under very mildconditions is the use of hydrolyzing enzymes.In addition, enzymes offer selectivity duringproduct release. Enzymes hydrolyse the wallsof cells, and when sufficient wall has been re-moved, the internal osmotic pressure bursts theperiplasmic membrane allowing the intracellu-lar components to be released [195]. The ef-fect of lytic enzymes is specific to particulargroups of cell types, which is attributed to thedifferences in cell wall composition. For exam-ple, the most efficient lytic enzyme for bacte-ria is lysozyme from hens’ egg. This enzyme isalso used in large-scale processes for enzymeproduction [198]. Even more highly specializedprocedures for sample disruption can be appliedin biotechnology. For example, high yields of in-tracellular enzymes from yeast can be obtainedby applying a series of electric field pulses [199].By using this technique, up to 90% of the totalactivity can be released without any further orprevious treatment of the cells. The method isbased on electroinduced changes in the cell en-velope leading to a leakage of part of the intra-cellular proteins without the formation of debrisand permits the treatment of large volumes.

7.2. Solid–Liquid Separations

Solid–liquid separation is a very importantprocedure during downstream processing inbiotechnology for cell separation, cell debris re-moval, and also for product recovery. The mostimportant solid–liquid separation techniques inuse for are filtration and centrifugation. Filtra-tion separates solids from a liquid by forcing the

liquid through a solid support or filter medium.Dead-end filtration and cross-flow filtration aretwo different designs of filtration which can beused. In dead-end filtration mode, the total pro-cess fluid stream flows through the membrane.The retained solids accumulate on themembraneand build up a filter cake. The membrane hasto be changed when the membrane pores areclogged by the solids. When the feed flow isdirected parallel to the membrane surface, theterm cross-flow filtration is used. The tangentialflowof liquid removes any retainedmolecules orparticles from the membrane surface, which re-sults in a stable flux for a longer time period. Byusing cross-flow filtration, relatively low shearstress is possible and filter aids are not required.In addition, scale-up is simple and cell washingis possible in a single process step.

Over the last 30 years, a number of fur-ther membrane processes have been developedfor molecular separation. These filtration tech-niques can be divided into four major groups:reverse osmosis (hyperfiltration, RO), nanofil-tration (NF), ultrafiltration (UF), and microfil-tration (MF) [200]. Membrane processes areeasy to scale up and the possibility of using thesame materials and configurations in differentsizes from laboratory to process scale reducesthe validation effort enormously. However, fil-tration processes are limited with regard to se-lectivity. The fractionation of proteins can onlybe achieved with large differences in the molec-ular weight of the proteins and it is important tokeep in mind that a certain difference in the mo-lecular weight of two proteins does not mean thesame degree of difference inmolecular size. Pro-teins that differ in molecular weight by 10 timesmay differ in size by only 3 times when in glob-ular or folded form. Another problem encoun-tered when using membrane processes for prod-uct recovery canbe the slow retentate flux,whichcan result in the formation of a thick secondarymembrane. Another possibility is the strong in-teraction of the sample with the membrane ma-terial. This often depends on unspecific proteinadsorption which is affected by several factors[201]. Thus, different membrane types have tobe screened to minimize the unspecific bindingof the sample when a new filtration procedurehas to be developed.

64 Biotechnology

7.3. Product Recovery

One of themost important procedures in productrecovery are chromatographic processes. Dur-ing chromatographic steps the sample is sepa-rated into several fractions according to inter-actions between the different molecules in theliquid phase and the stationary phase (chromato-graphic resin). The molecules can be separatedaccording to their size and/or charge and basedon hydrophobic/hydrophilic or affinity interac-tion with the stationary phase. Column chro-matography utilizes a vertical column filledwiththe solid support with the sample to be separatedplaced on top of this support. The rest of thecolumn is filled with a solvent (eluent) which,under the influence of gravity, moves the sam-ple through the column. Differences in rates ofmovement through the solid medium are trans-lated to different exit times from the bottom ofthe column for the various elements of the orig-inal sample. Based on the specific interactionsbetween the sample in the mobile phase andthe stationary phase the column chromatogra-phy can be performed as:

– Ion-exchange chromatography,– Size-exclusion chromatography (gel-filtration chromatography),

– Reversed-phase chromatography, and– Affinity chromatography.

Immobilized-metal-ion affinity chromatogra-phy (IMAC) as a special form of affinity chro-matography is a popular and powerful way topurify proteins. It is based on the specific co-ordinate covalent binding between histidine orother unique amino acids and various immobi-lized metal ions (e.g., nickel).

Most of the chromatographic systems arerun in a batch or quasicontinuous mode. Nowa-days, new developments such as simulatedmov-ing bed chromatography [202] and anular chro-matography [203] offers the possibility of con-tinuous processes. One problem in column chro-matography are the difficulties in upscaling. Ifthe column radius is increased, unless specialpacking techniques are employed, the packingprocedure becomes inefficient and the pack-ing itself unstable. In addition, to maintain theoptimum mobile phase velocity, the flow ratewill need to be substantially increased and theconsumption of mobile phase will eventually

become economically impractical. Conversely,if the column length is increased, then theimpedance to flow will become greater lead-ing to high column pressures. If large columnradii are employed, then themechanical strengthof the column system will limit the maximumpermissible pressure. Consequently, lengthen-ing the column will eventually require the parti-cle diameter to be increased to provide adequatepermeability. Increased particle diameterwill, inturn, reduce the column efficiency, which mayimpair the resolution of the compounds of inter-est [204].

Membrane. Until today, the required pro-cess steps for the product recovery are carriedout separately, which leads to many energy-intensive process steps such as filtration or chro-matography and production of vast wastewa-ter amounts, respectively. Membrane adsorp-tion allows the integration of several processsteps into one-unit operation, which means thedownstreaming of biotechnologically producedproteins can be carried out by saving processtime and resources [205, 206]. In addition tothis, conventional chromatographic techniqueswhich are based on packed columns often re-quire lengthy procedures. This can lead to thedegradation of sensitive proteins [207]. Further-more, packed columns exhibit a high pressuredrop across the column and a slow diffusion-controlled binding process of solutes within thematrix [208]. Whereas membrane systems showseveral advantages to packed columns, most im-portant, mass transfer takes place through con-vection rather than through diffusion. Due tothis fact, membrane adsorbers enable a time-effective performance with high flow rates with-out high back pressure [209]. Membranes canbe converted into efficient adsorbers by attach-ing functional groups to the inner surface ofsynthetic microporous membranes. Affinity ad-sorption, ion exchange, or immobilized-metalaffinity chromatography can be carried out bythese membranes. Membrane ion exchangersof strong acidic (sulfonic acid), strongly ba-sic (quarternary ammonium), weakly acid (car-boxylic acid), and weakly basic (diethylamine)types are commercially available. A chelatingmembrane based on the iminodiacetate (IDA)group is applicable for IMAC. Membrane ad-sorber technology has several major advantagescompared to classical separation methods. Due

Biotechnology 65

to the membrane structure, the binding of pro-teins is not limited by diffusional processes,therefore loading and elution can be performedat very high fluxes resulting in very short cycletimes. Compressibility of the membrane undernormal operation conditions can be neglected,channeling cannot occur, and the pressure dis-tribution inside the modules is designed to haveplug flow through the module, all of which leadto sharp breakthrough curves. Scale-up is veryeasy, materials and systems allow cleaning inplace (CIP), and the validation of the processis made easier due to of standard products andvalidation service of suppliers.

7.4. Solvent Extraction

Solvent extraction is the most common methodfor the recovery of hydrophilic substances and,therefore, a method for separating well-solublemetabolites from cultivation media. Classicalextraction processes use organic solvents, whichare often rarely suitable for effective recovery ofthe solute. Recently, new extractions have beendeveloped which form specific adducts with themetabolite in question and allow its recoverywith high efficiency and selectivity [210]. Sol-vent extraction in biotechnology focuses on therecovery both of primary metabolites (e.g., eth-anol, acetic acid, citric acid, and amino acids)and of secondary metabolites (e.g., antibioticsor vitamins). The concentration of secondarymetabolites is usually much lower than thatof primary metabolites. Since most secondarymetabolites are for use as therapeutics, the qual-ity requirements of the products are high. Sol-vent extraction can help to fulfill these require-ments. In addition, the set-up of integrated bio-processes (production and downstreaming) canbe performed by solvent extraction [211]. Super-critical fluid extraction (SFE) becomes also animportant tool in biotechnological downstreamprocessing since it offers important advantagescompared with other solvent extraction meth-ods. It is possible to work in an oxygen-freesystem, which prevents oxidation. The low tem-peratures applied minimize thermal degradationand microbes or their spores are not soluble.In addition, supercritical fluids for extractionsare inexpensive. The successful implementationof this technique can lead to improved sample

throughput, more efficient recovery of analytes,cleaner extracts, economic replacement of halo-genated solvents, and a high level of automa-tion, compared to conventional sample prepa-ration procedures [212]. Supercritical fluid pro-cesses are being commercialized in the polymer,pharmaceutical, specialty lubricants, and fine-chemicals industries. Supercritical fluids are ad-vantageously applied to increase product perfor-mance to levels that cannot be achieved by tra-ditional processing techniques.

8. Monitoring and Modeling ofBioprocesses

The requirement to operate biotechnologicalprocesses in a cost-effective manner by simulta-neously maintaining a high product quality andsafety is becoming increasingly relevant. Histor-ically, themost importantmeans of achieving in-creased productivity from bioprocess plant hasbeen through time- and resource-consuming ex-perimental developments. More recently, how-ever, significant advances have been made in thearea of computer applications for bioprocess su-pervision, modeling and control, and their intro-duction in process industry.

Typically, little use is made of the largeamounts of bioprocess online and laboratory as-say data after an experiment is completed. Re-cently, attempts have beenmade using enhancedautomation strategies to improve the qualityof information presented to operators and in-crease the level of automatic process supervi-sion, although few industrial applications haveyet been reported. On a plantwide perspective,the scheduling of process operations is also tra-ditionally manually undertaken by experiencedpersonnel by using a trial and error approachwith a planning board. This also appears to bea potential area for modern bioprocess manage-ment.

Knowledge-based systems, such as fuzzylogic systems, can be designed to cope with un-certainty and allow the coupling of quantitativeinformation with the qualitative or symbolic ex-pressions (in the form of heuristics), so as toreproduce the actions of an experienced processoperator. They might therefore be used as an in-telligent, online virtual assistant to the process

66 Biotechnology

operator, or, with extra knowledge as a supervi-sor in running and maintaining bioprocess oper-ations within optimal operating conditions. Themain objective in this approach is to develop anexpert supervisory system which uses biopro-cess and control knowledge in the form of rulesin conjunction with bioreactor state estimation(soft sensing), parametric identification and pro-cess control algorithms running in real time. Inaddition, provision needs to be made for pro-cess models and known bioprocess behavioralcharacteristics to be incorporated in order to en-hance the overall supervisory control strategy.The complete system aims to perform processmonitoring, process control, sensor validation,fault detection, and diagnostic tasks and, in ad-dition, provide recovery advice so as to achievecontinuous online optimization.

Collateral with the above approach is the on-line determination of the critical key variableswhich govern process production. The measure-ment and estimation problems are well knownand represent the main drawback concerning aneffective biosystem optimization. Three ways ofreducing the difficulties encountered can be pro-posed:

– Better sensors– Better sampling and automatic analysis sys-tems

– Online estimation of difficult to measure, orimmeasurable, variables.

Although all of these are attracting researcheffort, the techniques of online state and parame-ter estimation are receiving the greatest interest.Whilst developments in biosensors are in somecases helping to contribute to the online determi-nation of bioprocess parameters, they are by nomeans at the stage where general applicabilityhas been achieved. Until this is the case, the de-velopment of online estimation techniques willprovide the major means by which online closedloop feedback control of critical bioprocess vari-ables can be achieved.

Several different approaches can be adoptedin the development of bioprocess estimation al-gorithms. The easiest but potentially least accu-rate way of obtaining an estimate of a “difficulttomeasure” primary process variable is to estab-lish a correlation with a measurable secondaryprocess variable, whilst ignoring measurementerrors, noise, etc. Amore robust approach might

be to adopt a numerical estimation technique,either for estimating the parameters of a prede-fined model structure, usually of a generalizedlinear time series form, or directly to obtain anestimate of a difficult to measure primary pro-cess variable (state estimation or software sen-sor). An alternative approach is to exploit thenonlinear mechanistic structure of the biopro-cess, if known, in the form of a state observer.

Neural Computing. A relatively new devel-opment in artificial intelligence is the area ofneural computing. Neural networks are dynamicsystems composed of highly interconnected lay-ers of “simple” neuron-like processing elements.In the field of process engineering, it is theirability to capture process nonlinearities whichoffers potential benefits. Other useful charac-teristics are their ability to adjust dynamicallyto environmental and time-variant changes, toinfer general rules from specific examples andto recognize invariances from complex, high-dimensional data. These properties provide neu-ral networks with the potential to outperformother “learning” techniques. Given a series ofexperimental data the network is able to estab-lish the governing relationships in these train-ing data. This ability can be exploited to aid thenonlinear modeling, control, and optimizationof complex processes, as well as being used inexisting predictive control methods.

8.1. Characteristics of Bioprocesses

8.1.1. System Definition

A biotechnological process consists of a systemof chemical, biochemical, and microbiologicalreactions, incorporating process-engineering as-pects such as mass and energy conservation,thermodynamics, or heat and mass transfer cor-relations. (Fig. 27). The biological part of thesystem represents the peculiarity of the systemcharacteristics and causes the great difficulties inconsidering it from the system theoretical pointof view.

Common for all system models is their map-ping of inputs to outputs, of stimuli to responsesin some with the additional introduction of statevariables. Therfore, the task is to define properinputs and outputs, dependent on the problem

Biotechnology 67

Figure 27. General model structure for biotechnological processesx = external flow and transport rates; c = concentration of the component (·)I inlet, (·)O outlet; TR = transfer rates; Q =volumetric reaction rate; q = specific reaction rate; Y= yield coefficient; r = intrinsic reaction rate; (·)i = index for quantity i;(·)j = index for quantity j

available, to use appropriate signals (trajecto-ries) to describe the system behavior in their im-portant aspects and to build up the model.

A biotechnological system can be defined asa set of i biochemical reactions involving m in-put variables −→x (t), o output variables −→y (t), m

state variables−→z (t) and r parameters⇀p notated

in operator transformation T(·):⇀z (t) =T

(⇀x (t) ,

⇀p

)(1)

⇀y (t) =T ∗

(⇀x (t) ,

⇀z (t) ,

⇀p

)(2)

with

⇀z (t) =

[z1 (t) z2 (t) . . . zn (t)

]T

⇀x (t) =

[x1 (t) x2 (t) . . . xm (t)

]T

⇀y (t) =

[y1 (t) y2 (t) . . . yo (t)

]T

⇀p =

[p1 p2 . . . pr

]T

(3)

The model does not determine the input vari-ables −→x (t). They represent the systems stimuliand can be divided up into control variables and

68 Biotechnology

random forcing functions. Control variables arethose which are chosen by the operator to ma-nipulate the system in such away the benefits aremaximized. Typical examples in bioprocess en-gineering are: temperature trajectories, agitationrate, or feed rate of key components. Randomforcing functions can be seen as outer distur-bances of the system whose origin comes fromthe system environment and not from internaluncertainties, e.g., impurities of the inoculumsor the feed medium. To evaluate the system dy-namics, it is important to define meaningful tra-jectories of the control variables. Keeping thevalues constant will not result in a representativeinformation retrieval of the biosystem. Only ifcharacteristic curves – or specific combinations– are chosen to which the system reacts sensitivea representative process description is possible.Therefore, it is evident that the experimental aswell as the theoretical designmust be performedfrom deep problem awareness.

The state variables−→z (t) represent storage el-ements for mass quantities (e.g., substrate, prod-ucts, biomass, inhibitors, interferences), infor-mation, or energy of the system. The state vector−→z (t) is composed of any set of quantities suffi-cient to completely describe the behavior of thatsystem. Given a state vector at a particular pointin time and a description of the system forcingfunctions, in an appropriate uncertainty formu-lation, and control functions from that point intime forward, the state at any other time could becomputed. In biotechnological processes statevariables are frequently defined, if the key quan-tity of interest can not measured directly andmust therefore determined by state estimatorsfrom other process variables [213, 214]. The in-troduction of state variables is not strictly nec-essary. But in their absence inner relation canonly be hardly understood and described. Alsoa direct mapping of an input vector to an outputvector without consideration of state quantitiesin form of a signal model, as it is typical in con-ventional signal analysis in electrical engineer-ing, can not be accomplished. Their applicationdepends on the point of view. A signal modelprovides mainly information about the behav-ior of changing influencing parameters and howthey affect the outputs based upon given inputs.No information can be provided about the intrin-sic reactions or the basic mechanism, the focusis outside of any interpretation purposes [215,

216]. Mostly, they are used for software sensors[217, 218] or optimization purposes [219, 220].Both represent black-box applications (see be-low), where only the outputs are of special inter-ests, the inner relations or interpretation aspectsare secondary.

As output variables −→y (t), very often quanti-ties are specified which are accessible by onlinemeasuring systems or which can be estimatedby software algorithms. They can be seen asthe result of a measurement model which relatesthe state variables −→z (t) to the output variables−→y (t). These quantities represent the valueswithwhose the system can be evaluated. The outputvector −→y (t) is strongly related to the aspect ofobservability (see below).

The division into parameters, state variables,or output variables is not an immanent propertyof a system, but a specific view by the analyzerof the system. It can be changed in relation to themode of process operation, to the process targetsor the simplifying assumptions of the model. Astate variable zi can be exchanged to an outputvariable yi and vice versa. A mathematical sys-tem should provide a coherent description of theentire bioprocess including all relevant systemaspects. The degree of complexity of the biopro-cess or the possibility for a simplified modelingof some parts of the system is determined bythe intended application and by the skills of thesystem analyzer.

Parameters⇀p can be seen as fixed quantities

of the system. In the ideal case, they should betime invariant, as it is indicated by their originaldefinition. However, sometimes a time depen-dency of parameters is introduced to gain a bet-ter systemdescription.Normally, the parametersare used to interpret the biosystem characteris-tics. However, the meaning of the parameterscan be quite different depending on the point ofview from which the biosystem is described. Interms of a mechanistic approach using funda-mental physico–chemical equations, the deter-mined parameters, after they are fitted by ex-perimental data, have a direct physical meaningand can be used for interpretation purposes andare comparable between different experiments.If the behavior is characterized phenomenolog-ical, by means of descriptive equations, the pa-rameters determined are only valid under thespecific boundary conditions of the experiments.

Biotechnology 69

They can be quite different in different exper-iments and possess nearly no general validity.One must be very careful when comparing dif-ferent approaches upon the quantities of theseparameters.

8.1.2. System Description

A biotechnological system consists in particularof a set of chemical, biochemical, and microbi-ological reactions whose components, reactionsrates, and other characteristics as temperature,pH-value, or internal energy, are mostly not ex-actly known. The metabolism of the biologicalunit is a very complicated process, not only be-cause the intracellular reaction network may bequite complex, but also due to the large num-ber of inner relations, control loops on reactionlevel, regulatory mechanism, and genetic levelthat are overlaid to coordinate the elementary re-actions. So even when the microkinetics couldbe measured exactly, it would be impossible toestablish a correct system map in every detailwithout the introduction of rigorous simplifica-tions. Another reason for uncertainty is the inho-mogenity of the units (e.g., cells) in thewhole en-semble, which is normally not considered whencharacterizing biosystems. The relations are notconstant and vary with time. Furthermore, theremight be a morphological differentiation bio-logical unit which is accompanied generally bychanges in the dynamic behavior.

Therefore, the interesting question is whythese complex systems can often be describedfor specific process targets by a few mathemat-ical equations only. One reason is that the func-tional blocks of the biosystem operate togetherin a network of regulation, and exchange ofmass, charge, and energy and a few bottleneckprocesses determine the behavior of the wholesystem. Another reason is the tremendous num-ber of units in the ensemble of a biosystemwhichhides individual variations in their growth andleads to a smoothed average behavior. In mostcases, the problem shifts. It is not the difficulty tobuild up the equations, but it is hardly to deter-mine the characterizing parameters, which aregenerally dependent on time and other systemstate quantities. Furthermore, a few reactionscan already determine the rates of many others.When the main inputs and the final overall out-

puts are known, a prediction is often even possi-ble with incomplete information on the involvedintracellular reaction network [221 – 223].

For analysis and design of those systems, twoaspects have to be considered – the biological re-actions catalyzed by the bioactive compound,and the numerous chemical and physical pro-cesses which precede, accompany, and followthem. Within the biosystem, the most importantphysical processes, which are intimately boundto the biological reactions, are associated withtransport of material, energy, and information toand from the location of the bioactive unit. Thedependency of the biological reactions on themicroenvironment around the unit is called mi-crokinetics. Due to the transport phenomena, themicroenvironment will vary along different lo-cations in the systems. The integral descriptionof microkinetics in connection with the trans-port processes that may be included in a sys-tem model is called the macrokinetics. Ideally,onewould always attempt tomodel the transportphenomena and the microkinetics of the model,because this model could be combined withproper reactor models to predict the macroki-netics of different biosystems. Unfortunately, atpresent, the effort for simulation of detailed re-actor models is rather high and methods for an-alyzing the microenvironment of the cells arestill rare. Therefore, what is usually observedis always a kind of macrokinetics. To find acompromise between these facts and our limitedknowledge about the process, the descriptionof the biological system is often characterizedbymeans of so-called formalkinetic approaches.This means a formal application of system equa-tions that represent actually microkinetics to aprocess, where only averagedmacrokinetics canbemeasured. As an immediate consequence, themodel parameters will change when the oper-ating conditions of the reactor are changed, oreven more, if a different reactor with changingboundary conditions is used. This necessity lim-its the predictive power of formalkinetic modelsfor biotechnological processes significantly.

In principle, independent of the available sys-tem volume, the mass balance of a component ifrom the general point of view can be describedusing a finite volume approach solving the gov-erning equations for fluid flowand heat andmasstransfer:

70 Biotechnology

∂ci

∂t+div

(ci

⇀u

)−div (Γ gradci) −dci

dt=0 (4)

where the dependent variable is denoted by theconcentration ci and

⇀u represents the velocity.

Both Γ , the diffusion coefficient, and dci/dt, thesource term, are specific to a particular mean-ing of a component i. From this general descrip-tion, a one-dimensional model can be derived.In the presented approach a dynamic process de-scription is the goal rather than a rigorous three-dimensional simulation of a compartmented sys-tem. For describing biological systems a cou-pling with the involved chemical, biochemical,and microbiological reactions is necessary.

In the considered (finite) volume, homoge-neous (bio-)chemical reactions system can beanalyzed according to Gavalas [224]. He as-sumes a detailed knowledge of the chemical be-havior of all the system components. The dy-namic of those reaction systems is described bya set of ordinary differential equations, by us-ing the idea that every isolated chemical kineticsystem should be consistent with the conserva-tion of atomic species and of internal energy. Inprinciple, this approach can also consider regu-latory and closed-loop effects at the componentlevel. A general stoichiometric exact model asa closed homogeneous reaction system of con-stant (finite) volume can be described as follows.

dci (t)dt

=M∑

j=1

kijϕj (c1 (t) ,. . .,cN (t) ,T (t)) (5)

where(·)i Component i with i = 1, . . . , N(·)j Reaction j with j = 1, . . . , Mci(·) Concentrations of the different

(bio-)chemical components ijj(·) Reaction rate of the j-th reactionT (·) Temperaturekij Exact stoichiometric coefficient

from component i in reaction j

If we consider a reaction in a volume withpossible in- and outflow, the above approachmust be extended with the balances of the in-ternal energies

∂T

∂t+div

(T

⇀u

)−div (Γ gradT ) −dT

dt=0 (6)

dT (t)dt

=1cp

M∑j=1

Hjϕj (c1 (t) ,. . .,cN (t) ,T (t)) (7)

where

Hj Reaction enthalpy of j-th reactioncp Averaged specific heat capacity of mass in the

considered volume

In this model, all the components involvedin the process reactions take part in the mathe-matical consideration. Furthermore,Gavalas as-sumes that the j-reactions are independent ofeach other. Themass conservation andmore pre-cisely the conservation of atomic species imposecertain relations between the stoichiometric co-efficients kij .

In this case, a so-called forward problem isconsidered. A forward problem is one in whichthe parameters and starting conditions of the sys-tem, and the kinetic or other equations whichgovern its behavior, are known and the equationscan be solved by typical numerical methods.From amathematical and physical point of view,it can be seen as the calculation of the “effect ofa cause” and not to estimate the “cause of aneffect”, as it is accomplished in an inverse prob-lem. In other words, we usually know how to usemathematics and physical reasoning to describewhat would be measured if conditions were wellknown [225, 226]. Most mathematical modelsin fluid dynamics are of the “forward” type; therelevant properties of the aquifer or reservoirsare assumed to be known, as well as the initialand boundary conditions. A model then predictsthe resultant flow. This is typically the approachused to map a scenario caused by lot of complexinteracting partial processes or taken in sensitiv-ity studies, which are quite useful, and can showwhat the most important features or processesare likely to be for a site [227 – 229]. Follow-ing the ideas of forward problems, structured orcybernetic approaches for the description of bi-ological systems can also be mentioned. Theystructure the cell mass into several intracellu-lar compounds and functional groups which areconnected and regulated via an optimal criterionto each other and to the environment by fluxes ofmaterial and information. The functional groupsmay, in one extreme, consist of detailed reactionnetwork considering asmanyas known reactionsand regulatory loops [230, 231].

Mostly, however, in bioprocess engineeringwhen microbiological and biochemical reac-tions are involved, the exact stoichiometry is un-known. In the engineering literature of the last20 years a less precise mathematical model for

Biotechnology 71

biotechnological processes is encountered [232,233]. The so-called “general reactor model” –firstly introduced by Bastin and Dochain [232]– tries to unify chemical, biochemical and mi-crobiological process using the same approach.The bioprocess, an n-dimensional system of or-dinary nonlinear differential equations, is basedon a simple representation of the biotechno-logical processes, where superfluous details areomitted. Only the dynamics of those compo-nents that play an important role in the processare considered, irrelevant byproducts or nonlim-ited substrates are neglected. The stoichiometryof the chemical reactions is only qualitativelytaken into account by means of yield coefficientYζ(·)/c(·) or stoichiometric coefficients k∗

ij (Eqs.8 and 9).

dςi (t)dt

=M∗∑j=1

k∗ijϕj (ς1( t),. . .,ςN (t) ,T (t)) (8)

Yςj/c∗i=

M∗∑J=1

dςj

dt

dci∗dt

(9)

where ci (·) and ζi (·) are concentrations of thedifferent (bio-)chemical components, ci (·) arethose substances (i*=1, . . . ,N**) whose re-actions are calculated by the yield coefficientsYζ(·)/c(·), ζi are those components (j=1, . . .,N*)whose turnover are determined by the dominat-ing reaction rates ψj .

M∗ Number of the dominating reactions in theconsidered volume

ψJ (·) Reaction rate of the j-th reactionT (·) Temperature

In general, these coefficients do not have anymechanistic meaning in contrast to the above-mentioned approach, instead they represent for-malkinetic quantities, which must be estimated,determined experimentally, or established bypractical experiences. This allows the inclusionof chemical, biochemical, and microbiologicalprocesses in a unified approach. However, thereaction scheme may be inconsistent with thelaw of conservation of mass. Also cell-to-cellheterogeneity, e.g., due to different proliferationphases, are not considered and simplifies a segre-gated viewpoint to an unsegregated perspective.

The reactions rates can be often a very com-plex function of the operating conditions and ofthe state of the biosystem. In the case were ψ

is proportional to the specific growth rate of theorganism µ, there exist several possible models[234]. This approach is very common in liter-ature (overviews in [235, 236]) and can be as-signed – in contrast to the above-mentioned ap-proach – to the class of inverse problems.

It is typical for inverse problems that in manysituations the quantities that we wish to deter-mine are different from the ones which we areable to measure. If the measured data depend,in some way, on the quantities we want, thenthe data at least contain some information aboutthose quantities. Starting with the data that wehave measured, the problem of trying to recon-struct the quantities that we really want to knowis called an inverse problem; we measure an ef-fect and want to determine the cause. Typicalapplications of inverse problems aremodel iden-tification and estimation, image analysis, numer-ical analysis, or navigation [237, 238].

8.1.3. Dynamics of Biosystems andReal-Time Considerations

Real-time considerations play an important ruledealing with process control strategies. A noto-rious underestimation of the dynamic propertiesof microbial and cellular populations exists andresults mainly frommatching the duration of therespective batch cultivations to the relevant timeconstant of the biosystem under investigation.However, metabolic regulation of enzyme activ-ities and fluxes often takes place on a time scaleof seconds rather than days although the lattermay also be true for some processes. It is there-fore in the scope of promoting biotechnologicalresearch to adopt and develop appropriate exper-imental concepts,methodologies and equipmentin order to consider all relevant time constants[239].

What is fast?What is slow?What is relevant?The last question is the most important whendealing with modeling. The relaxation time con-cept of Harder and Roels [240] (Fig. 28) mapstypical time constants of microbial and cellularcontrol on the level of modification of enzymes(activation, inhibition, dis-/association of sub-units, covalent modification or digestion) to therange of milliseconds to seconds, on the level ofregulation of gene expression (induction, repres-sion, or derepression of transcription) to min-

72 Biotechnology

Figure 28.Concept of relevant relaxation times and time constants (according to [240]). Note that the time scale is logarithmic

utes, on the level of population selection andevolution to days and larger units. Consideringmainly the growth of microorganisms, typicaltime scales of 0.3 to 20 h are relevant. The exam-ples discussed belowwill illustrate how bioengi-neering is facing the individual time constants.

A typical bioprocess, if operated in batchmode, extends over several hours or a few days.If operated in continuous mode, it is not rea-sonable to accept operating times of less than amonth (see, e.g., Heijnen et al. [241]). If an or-ganism has special physiological features suchas baker’s yeast, a transition fromone to the otherdomain (e.g., from low to high dilution rates or,in otherwords, frompurely oxidative to oxidore-ductive growth) may also result in considerablechanges in the time required to approach a newsteady state. Axelsson et al. [242] reported arough estimate for this case: the time constantfor experiments at low dilution rates (D<DR,whereDR=D atwhich the regulatory switch bet-ween oxidative and oxidoreductive metabolismoccurs) is, as expected, in the order of the meanresidence time (τ=D−1) however, above DR,the time constant was predicted to be at leastone order of magnitude greater. In experimentsspecifically designed to verify this, either evengreater time constants or no unique stable steadystates at all were found [243].

If excess carbon and energy source is pulsedto a carbon- and energy-limited culture, theintracellular ATP concentration initially dropsbecause of the phosphorylation sink (glucok-

inase and/or hexokinases). Only later, duringcatabolism, can the energy provided by this ex-tra substrate be liberated in terms of new ATP.The duration of the ATP sinkwas predicted to bein the order of a few tens of seconds by Nielsenet al. [244]. Obviously, such rapid reactions areunder kinetic control and the necessary enzymeactivities are present in sufficient quantities. Itis not clear, at present, whether the fluxes attaintheir organism typical maximal values immedi-ately or some fine tuning of enzymatic controlprecedes this event. It is likely that in the lattercase a regulation of (intracellular) enzyme ac-tivity, if at all necessary, takes place on the levelof enzyme modification (e.g., phosphorylation)rather than on the level of de novo production(i.e., control on the transcriptional level) becausethe latter would require much more time [245].

The dynamics of microbial cultures have animportant impact on the characteristics of mea-surement and process control. The “typical timeconstant” in a bioprocess is often erroneouslyanticipated to be equivalent to the entire durationof cultivation. However, the quantitative inves-tigation of substrate uptake requires a time reso-lution of a few 100 ms, otherwise artifacts mustresult [246, 247]. This statement was evidencedonly after a suitable technique had been estab-lished: glucose metabolism was stopped within100 ms by spraying the cell suspension from theoverpressurized bioreactor into 60% methanolwhich was pre-chilled to −40◦C.

Biotechnology 73

The relevant relaxation times of a culture sys-tem are determined by the actual cell density andthe specific conversion rate (capacity) of the cul-ture, that is, by one or more operational and statevariables (for instance feed rate, the concentra-tions or activities of cell mass and of effectors,if relevant) and inherent characteristic proper-ties of the biosystem which are represented byparameters. There are metabolites with a longlifetime and other (key) metabolites with veryshort lifetimes, e.g., molecules representing theenergy currency of cells such as ATP and othernucleotides. Rizzi et al. [248] and Theobald etal. [249] have shown that the energy charge re-sponse to a pulse challenge (ATP) of a yeastculture is a matter of a few seconds only. How-ever, the responses can differ considerably whenpulses or shifts are applied to cultures of differ-ent recent history [245, 250]. Neubauer et al.[251] found that substrate oscillations greatly af-fected the growth performance of E. coli. Shortterm, that is, less than 2min, glucose excess, andstarvation were investigated in a looped systemof a stirred tank and a plug flow reactor. Themetabolism changed within some few minutestotally from anaerobic to aerobic and vice versa.Similar observations have also been made foryeast [252].

Two other paradigms demonstrate that theband width of relaxation times is extremelybroad: 1) the time required to achieve a newsteady state in a chemostat culture is approxi-mately determined by (µmax−D)−1. 2) The timerequired by a culture to consume a considerablefraction of small amounts of residual substrateduring sampling – and thus systematically fal-sify the analytical results if no appropriate inac-tivation takes place depends, among others, onthe cell density and can also be in the order of afew seconds only.

Even simple static models are very valuablefor compensation of systematic errors built intoautomated analytical procedures. One importantexample is the case when sampling requires awell-known but non-negligible time. A bypassbehaves as a plug flow type reactor fractionwhere flow dependent spatial gradients developandwhere no inactivation can take place becausethe bulk of the bypassed aliquots are returnedto the reactor. The cells continue to consumesubstrate while they are being transported fromthe exit of the reactor to the filter. The permeate

recovered is representative for the filter site butnot for the reactor. Knowing the transport timeand somebasic kinetic parameters one can easilycompensate online for such errors provided thata useful estimate of the actual biomass concen-tration is available. Even though a bypass canbe tuned to operate at a mean residence timeof 5 s or less, this can be enough for a signifi-cant decrease in substrate concentration in high-density cultures. Sample buses need a minimal(dead) tune for transportation of the sample andin situ filters tend to fail in high density culturesbecause of rapid fouling. Hence, the problem isreal and can not be ignored.

8.2. Biotechnological MeasurementSystems

All measurements have the ultimate goal ofcreating representative information from thebiosystem involved. Cellular or biochemicalquantities are the primary variables in biopro-cess engineering. They mainly determine theperformance of the bioprocess and are thereforeof special interest [253, 254]. Measurement andacquisition of information are building the fun-dament for all process observations aswell as forthe development of new bioprocesses. They re-present the presupposition for process optimiza-tion and control. However, they must be avail-able online to exploit them in a control strategy.Measurements are not only key issues inmodernprocess development, they also open the door toa detailed process supervision and understand-ing, and avoid restricted views when analyzingprocesses just through a keyhole [255, 256].

In comparison to other disciplines such asphysics, mechanical or electrical engineering,sensors useful for online monitoring of biotech-nological processes are comparatively few; theyare mostly available for physical and chemicalquantities rather than for biological ones. Thereasons are manifold, but generally biologicallyrelevant information is much more difficult andcomplex to access and interpret than those fortypical physical or chemical quantities [257].Other important reason derives from restrictingrequirements for the sensor equipment, namely

– Sterilization procedures,– Stability and reliability over extendedperiods,

74 Biotechnology

– Application over an extended dynamic range,– No interferences with the sterile barrier,– Insensitivity to protein adsorption, surfacegrowth,

– Resistance to degradation or enzymatic breakdown, and

– No disturbances from matrix compounds (in-terferences, matrix effect).

These aspects are in an industrial environ-ment covered by the fact that the operation ofthe analyzer and its service must be as simpleas possible. The aim of applying such a mea-surement system is to get more information butnot to increase the chances of malfunctions ofthe whole process. Due to the complex natureof many process analyzers this requirement canonly be met in some exceptional cases. Finally,material problems can arise from the constraintsdictated by sterile process conditions or by bio-compatibility, which often make the construc-tion of the sensor hardware rather difficult.

8.2.1. Process Requirements ConcerningMeasuring Quantities

In modern bioprocess engineering there are un-doubtedly only a few variables that are generallyregarded as essential. Among these are severalphysical (i.e., temperature), less chemical (pH-value) and even less biological quantities (cellconcentration). Figure 29 gives a summary ofwhat is nowadays believed to be a minimum setof required measurements in a bioprocess. Suchequipment is typical for standard production ofbiomaterial [258]. However, the conclusion thatthese variables are sufficient to characterize themicroenvironment and activity of the biocompo-nents, of course, is more than questionable. Forthe consideration and compliance of the processtargets, as described above, besides some envi-ronmental and operational quantities, in partic-ular the important biochemical state variablesof the biosystems must be known, namely theamounts and composition of the active biomass,of the starting material, of the products (andbyproducts) or other metabolites. Modern bio-process observation systems try to overcomethe lack in the instrumentations, by building upfunctional relationships between these quanti-ties. The approach is to substitute the directmea-surement by a function based value, calculated

by other measured values. But this implies thor-ough problem awareness and is restricted to spe-cific boundary conditions (see Section 8.2.2).

Figure 29. Common measurement instruments and controlunits of bioreactors as generally accepted as routine equip-ment (→ measurement only, � measurement and open- orclosed-loop control)

Biomaterial. The active biomaterial is ofparamount importance to scientists as well asto engineers. It is an indicator for the avail-able quantity of biocatalysts. It is definitely animportant key variable, because it determines –simplifying- the rates of growth and that for bio-transformation. Almost all process descriptionsand optimization tools contain the quantity ofbiomass as the most important state variable.

The biomaterial of interest depends on thesystem considered: microorganism, enzymes,nucleotides, or simple protein amount and con-stitution.Many control strategies involve the ob-jective of optimizing the biomaterial concen-tration. For automation purposes, the biomassis mostly seen as a homogeneous entity, beingaware that this represents an uncertainty becausesegregation into different individuals is more re-alistic. An ideal measurement of the bioactivecompound would include their activity, physi-ological state, morphology, or other classifica-tions rather than just their mass. But these quan-tities are normally ill-defined and, in general,inaccessible to online measuring devices.

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A series of sensors and methods, that canbe automated and have appeared in the re-cent decades for the measurement of the to-tal biomass without discrimination of any seg-regation (e.g., active or inactive cells). Manyof them rely on optical measuring principles[259, 260], others exploit filtration character-istics [261, 262], electrical properties of sus-pended cells [263, 264], or thermodynamic laws[265]. One has to decide according to the spe-cific application, which method seems to be ap-propriate concerning the boundary conditions,because all methods reply on indirect measur-ing principles, and the access of the informa-tion carrying signal differs in nongeneral ranges.To get online information about the actual stateof the biomaterial techniques based on fluores-cence principle, especially the 2-D fluorescencespectroscopy, should bementioned. It representsa noninvasive method to analyze intracellularcompounds. Technically, either intra- or extra-cellular fluorophores (e.g., NAD+, Riboflavin,Tyrosine, ATP) are excited by visible or ultra-violet light, and the fluorescent light emitted bythe fluorophores at a longer characteristic wave-length is collected and gives information abouttheir concentration [266 – 268].

Substrate. The presence of sufficient andappropriate starting material (substrate) is thecause of any biotransformation and representsthe supposition of the product formation. Onecan solve the inverse problem, namely concludethat biological activities cease whenever an es-sential substrate is exhausted, and thus omitthe measurement of the substrate, provided theprogress of the bioprocess and/or product for-mation is known [269]. But, this is not alwaysa proper solution because there are many moreplausible, and also probable, reasons for a de-crease in bioactivities than just their limitationby depletion of a substrate.Onemust, then, solvethe direct problem, namely analyze the relevantquantities of the biological unit. From an engi-neering point of view this measurement shouldbe available instantaneously in order to be able tocontrol the desired process characteristics. In en-vironmental biotechnology, in particular, the ob-jective of a bioprocess canbe to removea startingmaterial as completely as possible rather thanmaking a valuable product. So, the starting ma-terial is identical to the process product. In some

other bio- or food technological processes, theconversion of a substrate from specific state ofaggregation to another is of special interest. Forexample, in the case of fouling of heat exchang-erswhere dissolved proteins are transformed to asolid protein layer, the starting material changesonly its state not its chemical structure. Or, con-sidering drying or thawing where water changesits state (or is removed)which strongly influencethe quality and behavior of the biomaterial in-volved, but no (bio-) chemical conversion takesplace. The classical methods to determine thesequantities are offline laboratory methods so far.This implies that samples are taken, sometimesaseptically, pre-treated, and transported to a suit-able location, where storage of these samplesmight be necessary before analyzing manually.These steps need a lot of human and instrumen-tal resources.

Product. The product is almost the only rea-son why a process should run. Themain concernis in maximizing the profit, which depends di-rectly on the volumetric productivity and/or onthe purity of the resulting product. It is there-fore of essential interest to know the quantities,which require measurements. What was saidabove about substrate determination and the ap-plication of classical methods is valid here, too.

In summary, bioprocess science needs sig-nificantly more quantitative measurements. It isinsufficient to know that something happens, weneed to know almost instantaneously, online andautomatically why, how, and to what extent abioprocess behaves [270].

8.2.2. Online Sensing Devices

The development of increasingly sophisticatedequipments for measuring of starting materialand products represents one trend to satisfythe growing demand for high-quality measure-ments. In contrast to engineering disciplinessuch as mechanical or electrical engineering,in bioprocess engineering almost no measure-ment performed is directly, i.e., none is ob-tained by immediate comparison with a refer-ence quantity. Most measurements are achievedby means of some specific physical or chem-ical property of the process value, often hid-den in the very complex sample matrix com-

76 Biotechnology

mon to bioprocesses. This very complex ma-trix, which consists of the numerous substancesand parameters surrounding the analyte and theirmutual influences, makes the task of measuringdifficult and leads to the development of moreand more complicated measurement techniques[271, 272]. The surrounding equipment, i.e., forthe sample pretreatment, exceeds the pure an-alytical core [273 – 275]. However, the greaterthe measuring system complexity, the more dif-ficult it is to ensure the accuracy and the reliabil-ity of the collected data. The need for accuracyand reliability increases even more if the data ofthe process analyzer should be used as input fora closed-loop controller. When unreliable mea-surement systems are used, the system opera-tor can only hardly decide whether an occurringvariation is due to an error of the analyzer or aresult from the bioprocess itself.

The final goal of the measuring unit of a bio-process is to build up simple online instrumentsin order to collect all necessary information fromthe biosystem, which is necessary to hold the de-fined process targets. Therefore, the process tar-gets should represent the selection criterion fordefining the measuring equipments. The infor-mation should be available online and nomanualinteraction should be necessary to obtain the de-sired measuring results. This aspect touches theterm “system observability” (see Section 8.4.1),which is strictly defined for linear systems, buteludes a definition for nonlinear systems as theyare common for typical biological systems [276,277]. It is not obvious and definable whetherall system information is available und knownto reach the observability in respect to the pro-cess goals or which sensors are still necessaryto reach the specified process goals. The conse-quence is that there are normally one or morestate variables (or linear combinations of them)that are hidden from the view of the observer(i.e., measurement equipment).

Concerning the information treatment therecan generally be made different classifications,which are important for industrial applications.From the communications engineering point ofview the information can be distinguished bet-ween online and offline. If a continuous auto-matic correlation between the signal from thesensor and the product or process quantity is pos-sible an online signal is available, otherwise itis offline. A measurement is called inline, when

a direct interaction between a product propertyand sensor exist, otherwise exline; this is morea technological characterization. Depending onthe site of installation, one discriminates furtherbetween in situ, which means built-in, and exsitu, synonym for a bypass configuration or foran exit line. In the latter case, the withdrawnvolumes are lost for the process. A further clas-sification can be made by the mode of operationof the sensing device. One can discriminate bet-ween continuous and discontinuous signal gen-eration in a predefined time step. This is a veryimportant fact for planning and developing anappropriate process controller considering thetime scales of the bioprocess. In the latter casem,one has to guarantee that the process informationis available in real time.

Only online sensors are of special interestconcerning the process control of biotechno-logical systems. Indeed there exist a couple ofdifferent, very powerful systems for the offlineanalysis of nearly all relevant substances. Foronline sensors, the situation is quite different.In situ instruments exist only for a few quanti-ties like the measurement of temperature, pH,pressure, oxygen, carbon dioxide, density, flowrates, electric power consumption, or redox po-tential. They use different measuring principlesand are widely introduced in industrial processengineering.

However, for the most important biologi-cal quantities in biotechnological systems es-pecially the measurement of active or inactivebiomass or the specific determination of individ-ual biochemical substances there exists a deeplack inonline instruments. In the following, prin-cipal techniques should be presented,which pos-sess a certain online capability. See also → Bio-chemical Separations; → Mass Spectrometry;→ Surface and Thin-Film Analysis.

Chromatographic Methods. A review ofchromatographic methods is beyond the scopeof this contribution. Both (high-pressure) liquidchromatography and gas chromatography havebeen applied in numerous cases to offline anal-yses of biotechnological samples, but online ap-plications, especially for industrial processes,have only recently been reported [278 – 282].The scope of chromatographic methods is theseparation of the individual constituents of mix-tures as they pass through columns filled with a

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suitable stationary phases. As disadvantages theconsumption of almost expensive reagents mustbe mentioned. The samples injected are to bepretreated in such away, that they possess nearlyno impurities, like gas bubbles or solid compart-ments. Any existence of those interferences dis-turbs the analysis in a significant manner, andmanual service procedures are mostly the con-sequence. Furthermore the measuring time lastup to 30 or 40 min; a time period which is of-ten too large for bioprocess supervision. But alsoeconomical aspects must be taken into consider-ation. Chromatographic systems possess a highcost price. Furthermore, high-specialized staffmust service them frequently, an aspect, whichrepresents the major drawback for an industrialapplication.

Mass spectrometry. A further measurementmethod, in principal suitable for online applica-tions represents mass spectrometry (MS). Massspectrometry has beenmainly applied for the on-line detection and quantification of gases such aspO2 , pCO2 , pN2 , pH2 , pCH4 , and even H2S, orvolatile substances such as alcohols, acetoin andbutandiol [283]. Generally, the detection princi-ple allows simultaneous monitoring and, con-sequently, control of metabolites. The princi-ples, sampling systems, control of the measur-ing device and application of MS for biopro-cesses have been summarized elsewhere [284].Of course, mass spectrometry requires very ex-pensive equipment and needs frequent and com-plex service procedures. But it should be takeninto account that automatic multiplexing of dif-ferent sample streams is possible and, in addi-tion, a great variety of different substances canbe determined simultaneously. Thus, one has todecide, whether the high economical and appli-cational expense is justifiable in comparison tothe advantages due to the acquisition of infor-mation.

Biosensors. The most promising techniqueto overcome these problems, represented in thelast decade, is the use of biosensors . Biosensorsconsist of a sensing biological module of eithercatalytic (e.g., enzymes or microorganism) oraffinity reaction type (antibodies, cell receptors)in intimate contact with a physical transducer(electrochemical, optic, calorimetric or acous-tic sensor). The latter finally converts the (bio-)

chemical into an electrical signal (overviews in[285 – 287]). In spite the fact, that principallyfor nearly all biochemical substances an biolog-ical recognition unit exists, biosensor applica-tions in an industrial surrounding often concen-trate only on a small group of substrates, namelyglucose, ethanol, or lactate [288 – 292]. Gener-ally, biosensors are tricky to handle and mustin principle be recalibrated in the matrix wherethe measurement should take place every timethe process conditions change. Due to the sen-sible biological element – an enzyme or livingmicroorganism – they principally cannot be ster-ilized. They also suffer from changes in the envi-ronment, for example, changes in pH or forma-tion or existence of aggressive chemicals suchas H2O2 [293]. Therefore, they must be usedin a suitable environment. Furthermore, the sur-rounding must be constant, because in generalan interrelation to the endogenous matrix exists.If the process media changes significantly, thebiosensor alters its behavior. A compensation ofthe changed matrix interferences becomes nec-essary. This represents one of the most decisivedrawbacks in inline biosensor applications forbioprocess engineering [294 – 296]. The long-term stability under working conditions is oftenpoor; theymust be serviced for several hours perweek preferentially by high-specialized staff. Inthis way, only limited experience could havebeen gained under technical process monitoringconditions. They possess only in some excep-tional cases a real industrial potential.

A reasonable way around the problem repre-sents the measuring of the biotechnological me-dia after removing a sample from the reactor ina bypass configuration. Consequently, the sen-sor is not installed in situ as it would be opti-mal but the information could be provided onlineand can be fed into an automatic process controlsystem. Depending on the analyte of interest,i.e., whether it is soluble or (in) the dispersedphase, one needs to sample either the entire cul-ture liquid or just a supernatant. The latter canbe acquired using for example amembranemod-ule. Concerning sampling, four aspects must betaken into account.

1) The systemmust be opened in such away, thatno infections can enter the reaction space ei-ther during sampling or between the samplingevents.

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2) A representative sample must be provided. Sothe overall volume of the sampling chamberas well as the position of device at the biore-actor must be determined properly. Addition-ally, the sample taken can still continue to re-act on the way to the sensor. In this case thesample would not be representative for theinterior of the reactor, and appropriate addi-tional measures or modeling approaches needto be taken in order to assure representativity.

3) When native samples are applied to the ana-lytical system, problems can arise frommatrixinterferences and matrix effects [297]. There-fore, the sampling device should be chosenin that way that no interaction with the matrixoccur which interferes the measurement (e.g.,absorption of substrate at the membrane). Onthe other side, the sample device should with-hold all the cross-sensitivities for disturbingco-components.

4) Due to the high number of individual sys-tem components and their complex interac-tion (i.e., sample pretreatment and analyticalpart), sampling devices provoke a small reli-ability in regard to the required process time.Furthermore, the hardware, necessary for thewhole analysis system entails high originalcosts. The specific adaptation to one concreteprocess condition implies inflexibility. It ex-cludes the determination of other metabolitesin other process media. Its adaptation is onlypossible by time- and money-consuming al-terations.

Flow Injection Analysis. The most impor-tant technique of sampling methods in biotech-nology, behind some conventional devices likemembrane modules, represents the flow injec-tion analysis (FIA), firstly introduced by Ruz-icka and Hansen [298]. It can be viewed as ageneral solution-handling techniqueor samplingdevice combined with a sensing unit. This com-bination causes a high flexibility with respectto the combined analytical procedure. It can beseen as a principle where a small injected vol-ume (10-200 µL) is introduced into a continuousunsegmented stream of carrier. The sample dis-perses in a well-defined concentration gradientand is transported by the carrier stream to thereaction zone and a subsequent detector mod-ule. FIA cannot generate continuous signals.But, there are several important advantages like

a high sampling frequency (up to >100 h−1),small sample and reagent consumption, high re-producibility and nearly total versatility of thesensing methods.

Software Sensors. The most promisingmethod to get online access to important keycomponents of bioprocess quantities are soft-ware sensors. In general, software sensors sup-ply the estimation of the missing measurementsby using an appropriate model that relates thecorresponding variable with other physical orchemical measurements that are correlated to itin any way. The fact that the model is imple-mented by means of a software package (hencethe expression soft(ware)-sensor) denotes, thatsoftware sensors provide a software backup forunavailable sensors, as an alternative to a hard-ware back-up using spare sensors [299 – 302].

Generally, software sensors are typical solu-tions of so-called inverse problems (see Section8.1.2). In a complex biological system, in par-ticular, the quantities which are normally easiestto measure are the variables, not the parameters.In the case of metabolism, the usual parametersof interest are the enzymatic rate and affinityconstants which are difficult to measure accu-rately in vitro and virtually impossible in vivo[303 – 306]. Yet to describe, understand, andsimulate the system of interest we need knowl-edge of the parameters. In other words, onemustgo backwards from variables such as fluxes andmetabolite concentrations, which are relativelyeasy to measure, to the parameters.

In other words, when using software sensorsthere must always be a model available that re-liably relates the measured variable with the tar-get variable or parameter of interest. Normally,measured variables are easilymeasurable effectsthat are caused and influenced by the target. Itis the special objective of the software sensor toreach a maximal degree of generalization. How-ever, this is very difficult – even impossible – toachieve or furthermore to prove this claim. Thus,the basic question is: Is the available informa-tion representative enough to generate a modelfor the accurate estimation of the quantity of in-terest, or is the desired information inaccessibleby the chosen sensor collection? Consequently,the development of software sensors is often re-stricted to a specific application; any transfer to

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other measuring problems is almost combinedwith considerable alterations. This does not con-cern only the determination of the new param-eter at an identical model structure. Instead acomplete new system definition can be neces-sary, where new input variables must be addedor dispensable one can be deleted.

Spectroscopic Methods. In this context theuse of spectroscopic methods [307 – 310], afteroptical or sonic stimulation play a dominant rule.Theymust be seen asmultisensor systems. Theircommon property is the detection of absorptiondegrees after stimulation at various frequencies.By this, a set of information is generated, whichpossesses more information than the measure-ment at one specific frequency, themeasurementat one frequency can be seen as the output of onesensor. The multivariate evaluation in an appro-priate model should subsequently reveal the ac-curate determination of the desired quantity. Insuch models, normally pure data are used, theintegration of knowledge from well-known fun-damental equations is only very hardly to real-ize. The most important advantage of spectro-scopic methods is their non-invasive character.The sensing device is not in direct contact withthemedia or the process itself, so no interferenceis to be expected.

The situation is even more complicated sincethings develop in relation to time.The estimationof the physiological state of a culture involvesmore than one (measurable) variable at a timeand the recent history of this set of individual sig-nal trajectories involved. Consequently, physio-logical state estimation requires recognition ofcomplex patterns. Various algorithms used forthis purpose to build up the (data-)model havein common that it is not always the present valuesalone that are evaluated, there is always the re-cent history of signal trajectories involved. Con-cerning the models, some authors define soft-ware sensors only on the basis of neural net-works but a broader point of view should beadopted since software-sensor models are alsoobtained by using regression or correlation tech-niques as well as fuzzy logic or first-principlemodels or combinations of all of them [306,311 – 314].

In some cases, the data describing the actualstate and their recent history are compared withso-called reference patterns: these are data from

historical experiments or runs which an experthas associated with a typical physiological state.A physiological state is recognized either if theactual constellation matches any one of the ref-erence sets best – in this case, there is always anidentification made – or if the match exceeds apredefined degree of certainty, e.g., 60%– thenit can happen that no identification or associa-tion is possible when the pre-selected thresholdvalue was not reached. The direct associationwith reference data needs normalization (am-plitude scaling) and, probably, frequency analy-sis in order to eliminate dependencies on (time)shifts, biases or drifts.

In other cases, the data trajectories are trans-lated into trend qualities via shape descriptors.These combinations of trends of the trajectoriesof various state variables or derived variablesdefine a certain system state; the advantage ofthis definition is that the association is no longerdependent on time and on the actual numericalvalues of variables and rates [315].

8.2.3. Further Aspects ConcerningMeasuring Systems

Online measurements produced with in situ sen-sors are difficult to validate. The usual proce-dure for evaluating the quality of a measure-ment is restricted to calibration/checking priorto and after an experimental run. A few sensorssuch as the pCO2- or the Cranfield-glucose sen-sor [316] allow removal and, therefore, recali-bration of the transducer during a run. Externalchromatographs and FIA systems can be regu-larly recalibrated but the sterile interface cannot.Other sensors such as a pH or a pO2 probe can bemounted via a retractable housing, which allowseither sterile exchange or withdrawal for exter-nal recalibration during a run. A further possi-bility to gain information about the reliability ofa measurement is to mount a number of iden-tical sensors in easy-to-compare positions andto check the individual signals for equality. Thisopportunity has been exploited in particular atapplicationswhere a high process safetymust beguarantied. It is highly desirable to have alterna-tive principles of measurement at hand, whichoperate simultaneously (heterogeneous redun-dancy) or to introduce a self-supervision op-

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tion, like pattern recognition to generally iden-tify malfunctioning sensors [276].

Elemental balancing permits the determina-tion of (other) metabolic rates provided that thestoichiometry is known. Carbon balances are themost useful but the carbon lost via the exhaustgas (as CO2) and culture liquid (asHCO−

3 ) mustbe measured. Heinzle et al. [317] determinedthat the state predictions based on experimentswith a small mass spectrometer are not use-ful due to unacceptable error propagation; forinstance, a 1% relative offset calibration errorcould result in a prediction error for an intracel-lular storage material (PHB) of>50%. Instead,using a highly accurate and precise instrument(absolute errors <0.02% gas composition) to-gether with automatic, repetitive recalibrationresulted, however, in reasonable estimation ofsubstrates, PHB, and biomass. Others have alsoexperienced these findings, e.g., [318, 319].

The undelayed evaluation of the state of a cul-ture by using software sensors and computers,based on the quantitative analytical informationprovided by hardware sensors and intelligent an-alytical subsystems, constitutes an excellent ba-sis for targeted process control. Experts – eitherhuman or computer – have the data and the deter-ministic knowledge to trace observed behaviorback to the physical, chemical, and physiologi-cal roots thereby gaining a qualitative improve-ment of bioprocess control, a quantum leap: pro-cess control can act on the causes of effects ratherthan just cure symptoms. A simple standard op-erating procedure [320] has proven useful in thisconcern, namely:

– Measure everything that can be measured atthe very beginning of process development

– Decide whether or not a variable is relevant– Determine the relevant variables to be mea-sured, controlled, and/or documented

– Collect all raw data at any time and distin-guish online between variable and parameterbehavior

– Organize an archive of all these data accord-ingly and do not discard seemingly uselessdata since they contribute to the treasure ofexperience

If it is, as somepeople say, correct that today’sbioengineering with all its tools and methodolo-gies is too slowandnot efficient enough, then it isall the more urgent to improve the performance

of the methods, tools, and equipment currentlyavailable and to invent new and better ones. Inessence, techniques of instrumentation, opera-tion, and causal analytical interpretation of mea-surements need massive impulses.

8.3. Cognitive Computing

Artificial neural networks (ANNs) are datingback to 1943, firstly reported by McCullochand Pitts [321], then fallen into oblivion, butstarted a tremendous comeback in engineeringscience with the publications of Rumelhart in1985, who introduced – again – the backpropa-gation algorithm and demonstrated its potentialas a learning procedure in neural networks [322,323].Fuzzy Logic has been introduced by Zadeh1965, firstly ignored consequently by techni-cians because of their undeterministic and un-certain nature, but provoked a second wave ofinterest in the 1980s predominantly in Japanin respect to their potential in treating highlycomplex, nonlinear multiple input multiple out-put (MIMO) systems in technical applications[324]. JohnH.Holland published 1975 his clas-sicwork “Adaption inNatural andArtificial Sys-tems”; it can be seen as the pioneer work inevolutionary programming [325]. Compared toother mathematical principles, these three meth-ods are fairly new approaches and did not gainnot exclusive acceptance by scientists and engi-neers. The three methods mentioned above canbe seen as the main representatives of a classof problem-solving methods taking nature as amodel, especially how it has learned to solveproblems. They can be summarized to the areaof cognitive computing.

It is not within scope of this contributionto explain these methods in their basic prin-ciples and functionalities. Rather, their intrin-sic principles are assumed to be well known;overviews and extensive explanations can befound for fuzzy in [326 – 328], for ANN in[328 – 330] (see also → Process Control En-gineering, Chap. 13) and for evolutionary pro-gramming in [331, 332]. In the following, thefocus lies on twomethods, on fuzzy logic systemand on ANN, because of their high applicationalpotential, especially in bioprocess engineering[333, 334]. The explanations should show theirpotential, typical aspects and also some critical

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remarks, in particular in respect to bioprocessengineering applications.

Neural networks and fuzzy theory have beenunderground technologies for many years. Theyhave had far more critics than supporters formost of their brief history. However, in particu-lar due to their application potentials in differentengineering areas where classical approachesfailed, their acceptance is growing steadily. Neu-ral networks and fuzzy systems estimate func-tions from sample data, they map inputs to out-puts. Statistical and artificial intelligence ap-proaches also estimate functions. On the otherhand, for each problem statistical approaches re-quire knowledge how outputs functionally de-pend on inputs, in other words they need amathematical model. Thus, their fundamentalideas can be more compared to typical conven-tional mathematical principles. Instead, neuraland fuzzy systems do not require such a mathe-matical model. They can be seen as model-freeestimators [328].

From another point of view, artificial intelli-gence expert systems can also be seen as model-free estimators, they map conditions to actions.However, experts do not articulate a mathemati-cal transfer function from the condition spaceto the action space and the AI framework issymbolic. Symbolic processing favors a prepo-sitional and predicate calculus approach to ma-chine intelligence. It does not favor numericalmathematical analysis or hardware implementa-tion. In particular, symbols do not have deriva-tives; only sufficiently smooth functions havederivatives. Symbolic systems may change withtime, but they are not properly dynamical sys-tems, nor systems of first-order difference or dif-ferential equations. Therefore, they are unsuit-able for modeling purposes in bioprocess engi-neering.

A small excurse to neural (pre-) attentiveprocessing in human intelligence should illus-trate which ideal result a cognitive system pro-vides after its learning phase. Take a look at theKanizsa-square illusion (Fig. 30).

What do we see when we look at the Kanizsasquare [335]. We see a square with bright inte-rior. We see illusory boundaries, or do we? Werecognize a bright square. Indeed, technicallywecannot see it, because it is not there. The onlythings which are shown are four three-quartercircles, nothing more. The square exists only

in our brain, not “out there” in physical realityon this page. Immanuel Kant called these fourink stains 1783 noumena “things in themselves”.Applied to technical problems, this means thatwe recognize information or generalize artifactsin that way, that we see not only the input–outputmapping, but the real relations or laws the datastands for. The data can be seen as facilities orindications for the perception of the underlyingintrinsic information.

Figure 30. Kanizsa-square illusion

Today, many of the neural mechanisms thatKant could only guess are understood. We takefor granted our high-speed, distributed, nonlin-ear, massively parallel pre-attentive processing.In our visual processing, we pay no attentionto how we segment images, enhance contrasts,or discount background luminosity, even if theyare the carrier of the real information. When weprocess sound, we pay no attention to how ourcochleas filter out high-frequency signals evenif we collect the data. We likewise ignore ourreal-time pre-attentive processing. We experi-ence these pre-attentive phenomena, but we ig-nore them and cannot control or completely ex-plain them. Natural selection has ensured onlythat we perform them, ceaselessly and fast. Butwhat we really do, we recognize segmented im-age pieces and parsed speech units; we try toextract and introspect the intrinsic carried infor-mation based upon measured quantities.

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Neural network studies both pre-attentive andattentive processing of stimuli. This leaves unad-dressed the higher cognitive functions involvedin reasoning, decisionmaking, planning, or con-trol. The nonlinear neurons and synapses in ourbrain perform these functions; they can dealwithill-posed problems or with a high degree of un-certainty. Natural selection evolved this capabil-ity in exemplary manner. Furthermore, it is theattempt of engineers to copy exactly this capa-bility to technical problems by developing andusing fuzzy logic systems or artificial neural net-works.

Both methods try to imitate nature and bothestimate input–output functions, in spite of to-tally different underlying principles. In contrastto statistical estimators, they estimate a functionwithout a mathematical model of how outputsdepend on inputs; this represents their main fea-ture. They learn “from experience”with numeri-cal, sometimes, linguistic sample data. Learningcan be accomplished either by changing the sys-tem structure or by changing the system param-eter; both principles are described for both algo-rithms [336 – 339]. Thus, the principle modusoperandi is comparable. After the system prob-lem is defined, the procedure can be divided intolearning phase and prediction phase. The learn-ing phase of ANN is quite obvious. Input dataand corresponding output data are provided tothe ANN; the system changes its structure or pa-rameter, to minimize a predefined cost function.At first sight, the adaptive and learning characterof fuzzy systems is not obvious. Indeed, thereexist some techniques where the fuzzy systemis built up autonomously based upon availabledata sets [339, 340]. On the other hand, in mostheuristic applications a highly experienced tech-nician does the “learning” step. He collects data,recognizes the intrinsic information, and formu-lates upon this information rules upon sets. Thus,the modeler performs the learning step and itsresult is introduced into the fuzzy system.

The most important features intelligent sys-tems may show are generalization and learn-ing. All intelligent systems should generalize.Their behavioral repertoires exceed their learnedrepertoire in that way, that intelligent systemsassociate similar responses with similar stim-uli, small input changes produce small out-put changes. Furthermore, intelligent systemsshould learn or adapt. They learn new associ-

ations, new patterns, new functional dependen-cies. They sample flux of experiences and en-code new information. They compress the sam-pled flux into a small but statistically repre-sentative set of prototypes or exemplars. Sam-ple data, provided directly or transformed intolinguistic rules, changes the system structure orparameters. In all cases, learning means chang-ing. Neural networks learn patterns, functions,or probability distributions to recognize futurepatterns, filter future input streams of data, orsolve future combinatorial optimization prob-lems. Fuzzy systems can learn or formulate as-sociative rules to estimate functions or controlsystems, either directly by means of data and anoptimization add-on or guided by a highly expe-rienced supervisor. It can be shown under someassumption that a neural net can approximateto any degree of accuracy using a fuzzy expertsystem and vice versa [341].

Neural and fuzzy systems ultimatively learnor estimate some unknown probability functionp(x). The probability density function p(x) de-scribes the distribution of vector patterns x, a fewof which the neural or fuzzy system samples.When a neural or fuzzy system estimates a rela-tion r:X → Y, it in effect estimates the joint prob-ability densities p(x,y). Then the solution points(x,r(y)) should reside in high-probability regionsof the input–output product space X × Y. We donot need to learn if we know p(x,y). We couldproceed directly to our computational task withthe techniques from numerical analysis, combi-natorial optimization, calculus of variations, orother mathematical discipline. The need and theextent to learn varies inversely with the quan-tity of information available. Supervised learn-ing uses or creates class–membership functions.It compares every input with the correspondingclass D and proceeds learning based upon thedifference. Unsupervised learning system pro-cesses each sample x but does not know thatx belongs – or not – to class D. Neither super-vised nor unsupervised learning systems assumeknowledge of the underlying probability densityfunction p(x).

8.3.1. Fuzzy Logic Systems

Is uncertainty the same as randomness? Bay-esian statistician Dennis Lindley [342] stated

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that probability is the only sensible descriptionof uncertainty and is adequate for all problemsinvolving uncertainty. Lindley directs his chal-lenge in large part at fuzzy theory, the theorythat all things admit degrees, but admit them de-terministically. Although, both expressions areused simultaneously in the same context, proba-bility and fuzziness differ conceptually and the-oretically. In this contribution some importantdifferences are illustrated, more theoretical as-pects and proves can be found elsewhere [343].Fuzzy sets can be more compared with pos-sibilities than probability. However, probabil-ity and fuzziness also share many similarities.Both systems describe uncertainty with num-bers in the unit interval [0,1], consequently de-scribe it numerically. Both systems combine setsand propositions associatively, commutatively,and distributively. The key distinction concernshow the systems jointly treat a set A and its op-posite Ac. Classical set theory demands A∩Ac,and probability theory conforms: P(A∩A)=0.So A∩Ac represents a probabilistically impossi-ble event. But fuzziness begins when A∩Ac=0,hence P(A∩A)�=0.

Fuzziness measures the degree to which anevent occurs not whether it occurs. Probabilitydescribes the uncertainty of event occurrence.Whether an event occurs is “random”; to whatdegree it occurs is fuzzy. Consider an illustratingexample. The probability this book will be pub-lished is one thing. The degree to which it willbe published is another. From this book, onlysome special chaptersmaybe editedor thewholeentity gets public; this is typical for fuzziness.Fuzziness is a type of deterministic uncertainty;it handles with ambiguities. Unlike fuzziness,probability dissipates with increasing informa-tion. In fuzziness the uncertainty arises alwaysform the simultaneous occurrence of two prop-erties. More formally, does mA(x), the degree towhich element x belongs to fuzzy setA, equal theprobability that x belongs to A? This statementcan be true or not, it depends on the problemexisting, but in fact the point of views are quitedifferent and so far hardly to compare. The fol-lowing explanation should not explain the fuzzysteps in great detail, The focus lies upon two as-pects. How are the sets defined and which rulescan be determined. This is judged as the cru-cial aspects for fuzzy application in bioprocessengineering [344].

Following the introduction of fuzzy sets,Zadeh began steadily to develop the necessaryinferencing mechanisms and modeling tech-niques to bring this concept to fruition [345].In this period, it was recognized that fuzzy logiccould aid in the development of control systemsin which nonlinearities and time variance makethe development of traditional control systemsvery difficult. In these cases, a human operator isoften capable of controlling the plant, so a fuzzycontroller can often be designed based on the op-erator’s expert knowledge. Since that time, con-trol applications based upon fuzzy logic modelshave been extensively investigated [346]. Fuzzylogic systems are similar to expert systems intheir use of linguistic relationship, but the sub-stantial difference is that the outputs are in gen-eral continuous by variation of the inputs [347].It is exactly this property which causes the highinterest in mathematical modeling, and hence inengineering science.

Mapping with fuzzy logic is analogous toclassical modeling. The maps have variableswhich influence system behavior and relation-ships among the variables which describe thesystem. In classical models, variables have realnumber values, the relationships are defined interms of mathematical functions. In fuzzy logichowever, the values of variables are expressedby linguistic terms such as “large, medium, andsmall”, the relationships are defined in terms of“if-then” rules. By using defuzzification tech-niques, exact numerical outputs are calculatedfrom fuzzy subsets.

A fuzzy subset A is defined by a membershipfunction mA(xi ), where xi is the domain, of thevariable on which A is defined.

mA (xi) ∈[0,1] ∀ xi∈A (10)

The value of mA(xi ) for each x determinesthe degree to which each element in the domainbelongs to A. Although both classical and fuzzysubsets are defined by membership functions,the degree towhich an element belongs to a clas-sical subset is limited to being either zero or one.This means that m(x) may only be a step func-tion. In fuzzy logic, the degree to which an ele-ment belongs to a subset may be any value in theinterval [0, 1]. Since mA(xi ) may be defined byany function, it is easy to see that a classical sub-set is a special case of a fuzzy subset [348]. Thefuzzification process transforms exact values x

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of fuzzy variable vj tomAi (xi ) for all subsetsAi ,which are defined for the variable vj . More rig-orous definitions of membership functions maybe found elsewhere [327, 349].

One technique for determining the shape ofmembership functions is seeking knowledge ofthe system from experts then constructing mem-bership functions that represent expert opinion.Observations of many experts should be pre-ferred. Based upon this observations, member-ship functions can be constructed using statis-tical methods accompanied by using the analy-sis of preferences [350, 351]. Many researchershave investigated more “rational” techniquesfor determining membership functions. One ofthese approaches represents one variant of fuzzyclustering [352, 353]. It requires a set of inputand/or output data from the system to be col-lected. Patterns of data are foundwithin the inputor output space such that the variance within thesets is minimized and the variance between setsis maximized. The prototype, or center point, ofa clusterX is determined and a distancemetric isused to determine the degree ofmembership thata value has toX. Another technique for determin-ing membership functions involves neural net-works. Neural networks are networks of simpleprocessors connected by adjustable weightingfactors [328]. The weights are adjusted by usinga backpropagation or other known algorithm tominimize the difference between the predictedoutput and the measured output. The parametersof membership functions can be “learned” by aneural net from a set of input–output data [354,355]. Genetic algorithms have also been usedto determine the optimal shape for membershipfunctions [356, 357].

For fuzzy subsets to be useful in “modeling”,there must be a way to define relationships bet-ween them. This is accomplished by means oftheir membership functions mAi (x) with logicaloperators and inferences, to extract ambiguities.There are three basic operators in fuzzy logic, (1)OR, (2) AND, and (3) NOT, which link two ormore membership functions mAi (x) of differentvariables. The activation level of each rule is nor-mally equal to that of the premise (activation).All rules are evaluated. If some sets of an outputvariable are activated more than once, an aggre-gation method must be chosen to determine thefinal activity. After defining the interferencingmethod, the output fuzzy sets are defuzzified to

get a precise value, for further explanation see[327, 349].

Expert knowledge is the most common tech-nique for determining rules. The expert is askedto summarize the knowledge about the system inthe form of cause and effect relationships. Fromthese the rules are formulated. When no expertsare available or a more analytical approach isdesired, other techniques of system identifica-tion must be used. One approach is to apply aset of metarules to adaptively acquire informa-tion about the system. Linguistic self-organizingcontrollers issue control actions and observe theenvironment [358]. The metarules evaluate theperformance of the control actions from the ef-fect of the control actions on the environment.The metarules then determine whether the con-trol actions should be modified and perform themodification, if necessary. This technique hasbeen applied especially to robotics applications[359]. Fuzzy classifier systems are another wayto discover rules. Fuzzy classifier systems aregeneralizations of genetic classifier systems. Agenetic classifier system uses some of the ideasfrom genetic algorithms to develop expert sys-tems [360, 361]. Standard genetic algorithmshave also been used to discover rules [362].Sugeno and Yasukawa use the second variant offuzzy clustering [fuzzy clustering methods canalso be used to determine the membership func-tions (see above).Both approaches are addressedby the term “fuzzy clustering”] on data in theoutput space to determine fuzzy rules [363]. Theclusters in the output space are used to induceclusters in the input space. Then, input spaceclusters are projected onto the axes to find thefuzzy subsets for the input variables. Since theinput clusters are induced from the output clus-ters, rules can be constructed between the inputsubsets and the output subsets. Yoshinori et al.(1996) discuss another method of fuzzy modelconstruction based upon clustering techniques[364]. This method produces a relational as op-posed to a functional model, and consists of aseries of local, linear models to approximate aglobal nonlinear one. Recently; neural networkshave become an active field of interest and havebeen used to learn rules as well as member-ship functions [365, 366]. However, all meth-ods, which automatically generate rules, havedeficiencies in their interpreting ability. They aregenerated to fit available data best and it is not

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obvious that the rules express reasoning rela-tions. This can be seen as a strong disadvantagecompared to the generation of rules by means ofexpert knowledge.

The application area of fuzzy systems iswidespread and should only be presented herefor applications in bioprocess engineering. Thefermentation industry was one of the first torecognize the potential of fuzzy logic for bio-logical processes [367, 368]. Konstantinov andYoshidadeveloped a fuzzy logic system for iden-tifying physiological states in fermentation pro-cesses and used it to control distributed an E.coli fermentation [369]. Meanwhile the range ofapplications has enlarged tremendously. In prin-ciple, they can be divided in two fields. The firstone are those where the knowledge bases is con-structed such that it is basedupon existing (quali-tative) information, mainly from high-specialiststaff. This represents the area of fuzzy model-ing [370, 371], fuzzy controller [372, 373], orestimator for biosystem states or process faults[374, 375]. One of the intrinsic characteristics isthat significant knowledge engineering effort isrequired for setting up a complete and consistentrule base. The other domain of fuzzy logic is re-lational model-based systems where the linguis-tic information is extracted from available datato build a system model. This second approachis also directed to the field of bioinformaticsor experimental design [376, 377]. This ap-proach does not need any preinformation aboutthe intrinsic relation, however a knowledge basecan arise which is hardly to evaluate or inter-pret. Additionally, no information can be givenabout the generality of the established rules. Atall accounts, when a data driven constructionis used, a subsequent mathematical or manualevaluation, especially of the rule bases, is nec-essary after the automatic construction of thefuzzy system. For the process industry, expert-knowledge-based approaches possess more in-terests because they follow a concrete processtarget. The second one can be seen as a spe-cial form of knowledge discovery which enablesto accomplish some linguistic evaluation basedupondata, if no expert knowledge exists. In someexceptional cases, these clustering techniquesare exploited for control purposes [378]. An ag-gregation of both principles is also fruitful in thecase where the existing information is too littleto build up a representative “model”. In this case,

a data driven approach can help to complete thefuzzy structure.

8.3.2. Artificial Neural Networks (ANN)

Artificial neural networks are designed tomimicthe actions of neurons in the human brain. Fordetailed information, see [328 – 330]. They re-present massive collections of interconnectedneurons (nodes), which individually perform arelative simple signal processing; they are inter-connected via synaptic links. Like brains, neu-ral networks recognize patterns or relations bet-ween inputs and outputs we cannot even define.They can be seen as pure function approxima-tors whose abilities depend on the behavior ofthe individual nodes, the structure of the net-work, the learning procedure used, and on a verystrong degree of the quantity of representativedata. Not the total amount of data is crucial butthe information content with small degree of re-dundancy. Almost the whole quantity space ofthe input and output values should be spannedby the database. Recently, e.g., Cybenko [379]and Hornik et al. [380], have proved that anycontinuous function can be approximated to anarbitrary degree of exactness on a compact set bya feedforward neural network comprising two ormore hidden layers and a continuous nonlinearactivation function, providing appropriate data.

Working with neural networks always con-sists of two phases, a training and a predictionphase. In the first one, the variable parameters⇀p (t) of the net are changed in that way, theANN fits best to the trainings database. In most

cases,⇀p (t) represents weight parameters of the

intrinsic functions, e.g., the weight how the out-put of a neuron contributes to the activation ofa subsequent node. On the other hand, there ex-ist also some nets where the structure of the net(nodes and their synapses) can be seen as theirparameters which are changed during learning.

The indicator for changing⇀p (t) represents a

formulated performance index J. Based upon theabsolute value J, the parameters are changed inorder to minimize J.

min(J

(⇀p

))=‖ ∆J

∆p1,∆J∆p2

, · · · , ∆J∆pn

‖ (11)

where pi is a parameter i with i = 1 . . . n param-eters in the ANN.

86 Biotechnology

The definition of J depends on a couple ofdifferent aspects, but in most cases the Euclidiandistance between the desired target and the cal-culated output integrated over all training datasets is used. Performing learning should resultin the global optima concerning the specifiedproblem. However, most training procedures arelocal methods, requiring a gradient calculation.And obviously, any algorithm that relentlesslycrawls downward must have a very lucky start-ing position if it is to settle into the lowest lo-cal minimum. Avoiding false minima requirestwo separate procedures. First, we must elute inthe initializing phase starting in their vicinity. Ifwe settle into the neighborhood of a broad mini-mum, it will be very hard to escape later. Second,we require a procedure for determining whetheror not we are in a local minimum, and escapingif we are. This procedure will be constructive inthat we were in a local minimum. Otherwise, weassume that we have found the global optimum.In the second phase, the prediction phase, the netis conditioned to treat new input patterns, hope-fully gaining the correct corresponding outputvalues.

The nodes are connected in that way that theoutput of one node represents one input of theadjacent node. A node is stimulated by one ormore inputs −→x (t) and it generates one output,a scalar y(t) that is sent to other neurons. Theoutput y(t) is dependent on the weighted activ-ities of each input, on the nature all inputs aretransformed within the node and the parametersconstellation −→p (t) in each neuron. The actualrelationship between the inputs and outputs canbe enormously complex, depending on the cho-senof entryE(·), aggregationS(·), activationA(·)and output function O(·):y (t) =O (A(S (E(

⇀x (t) ,

⇀p )) )) (12)

Thus, the resulting behavior of individualneurons can be modeled by a simple weightedsum of inputs, a complex collection of interre-lated or subsequent set of (differential) equa-tions, or anything in between. There can besignificant time delays in the steady-state out-put value after stepwise input stimuli. Neuronsdo not always respond in the same way to thesame inputs. Even random events can be con-sidered in the operation of neurons. Luckily,a large body of research indicates that simplemodels, which account for only the most ba-

sic neural processes, can provide excellent so-lutions to practical problems. The human braincontains roughly 1011 neurons. As many as 104

synaptic junctions may abut onto single neuron.That gives roughly 1015 synapses in the humanbrain [381]. Consequently, the brain representsan asynchronous, nonlinear, massively parallel,feedback dynamical system of nearly “cosmo-logical” proportions.

The ability of ANN to emulate complex dy-namical systems stems from the interconnectiv-ity of neurons. In general an ANN has n neuronsdistributed in an input, an hidden, and an outputsection.Connections between neurons can be or-ganized in layers such that information flows inone direction only, or it can circulate throughoutthe network in cyclic patterns. All neurons canbe updated simultaneously or time delays canbe introduced. All responses can be strictly de-terministic, or random behavior can be allowed;the variations are nearly endless and should notbe described in detail. Mathematically they canbe formulated in the following vector equation.⇀y (t) =

⇀h

(−→o ( ⇀z (t) ,

⇀x (t) ,

⇀p )) (13)

where

−→y (t) Vector of yi with i = 1 . . . ni outputs of theANN−→x (t) Vector of xj with j = 0 . . . nj inputs of theANN

⇀p Vector of pm with m = 0 . . . nm variable

trainable parameters of the ANN⇀z (t) Vector of zj with n = 0 . . . nn for all outputs

of all individual nodes⇀o (t) Vector function of function om with m = 0 . . .

nm for the calculation of the individualcontribution to the output yi

⇀

h (t) Vector function of function hi with i = 0 . . .ni for the calculations of the real outputs ofthe ANN

There also exist some criteria to decidewhether a structure is appropriate for a goodmodeling approach. Always, a compromisemust be made between the desire to have a sim-plemodelwith fewer parameters andmore accu-rate predictions at the cost of a large number ofparameters. The Akaike’s information criterion(AIC) uses the number of data points, the over-all error, and the number of parameters to de-termine an index, which can be analyzed to fixthe optimal quantity of free parameters [382].Another concept that may be of interest is the

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use of a periodiograms, which gives an indica-tion regarding the frequency content of the in-put signals, typical frequency can be character-ized, which fixes the optimum degree of free-dom [383]. Another way is the determination ofprincipal components before fixing the structureof the net, thus, incorrect or redundant informa-tion can be rejected before entering in the learn-ing phase [384]. However, care must be takenin reducing system complexity or rejecting datasets, because they can be meaningful and repre-sent the behavior of the biological system eventhough, this may not be obvious from the dataset.

When considering a particular application,it is to decide what type of network should beused. Principally, ANN structures are classifiedas either global or local. Both possess specificproperties and hence applicational preferences.Global networks are the most popular und im-portant ones. Among them, different types exist:feedforward backpropagation neural networks(FBNN); recurrent neural networks (RNNs), orcascade correlation networks (CCNs). Unlikethe FBNN, RNNs are more general in the sensethat connections are allowed both ways betweena pair of neurons and even from a neuron back toitself on anyway. The RNN allows the dynamicsof the network to be considered intrinsically. Incontrast, in FBNN for mapping a dynamic be-havior the input variables must be supplementedby their corresponding deviations or time series.However, the number of weights (for a fully re-current network) to be determined may easilybecome quite large. Therefore structural opti-mizers are recommended, e.g., the cascade cor-relation, where connections and nodes are addedas required, [385]. Local networks, such as theradial basis function network (RBFN) processdata in discrete areas not continuously over thewhole space of the data. They are particularlysuitable for applications in online control andoptimization [386]. In the RBFN, it is necessaryto locate centers among the input/output pairssuch that the sum of the squares of the distancefrom the center to the training data set is mini-mized. These centers are equivalent in concept tothe weights in the FBNN. However, the RBFNmay fail in predicting values if the predictionspace does not contain any center. This must beseen as a crucial disadvantage, especially for on-line applications. So, global networks should be

preferred if high uncertainty within the data ex-ists.

The “explosion” of ANN applications innearly all areas of process engineering can beattributed to the following reasons:

– The tremendous hardware advances in digi-tal technology over the past decade have en-abled simulations of neural nets to be madeboth economically andwith relative ease handspeed.

– Applications of neural networks for sensorpattern classification have been found to besuperior to the traditional algorithmic tech-niques or the expert system approaches.

– Neural networks offer the promise of beingable to extract information from a plant inan efficient manner with normal availabilityof data, especially if severe or unknown non-linearities and time invariances, typically forbiosystems, exists.

– Some practitioners claim that neural networksmay be easier to use and apply in the real pro-cess plant, with difficult to handle nonlineari-ties, as comparedwith themodeling approach,which can be subject to various modeling er-rors.

– Finally, the versatility in structure and appli-cation of neural networks enables them to beutilized in the middle ground between con-ventional model-based approaches and black-box approaches for solving many classes ofproblems.These hybrid-type approaches havebeen another factor, which has further at-tracted their use in chemical process systemsrecently.

The applications utilizing neural networkbased strategies in bioprocess engineering arewide ranging. A detailed description of all ap-plication is outside the scope of this contribu-tion. Neural networks can be used for classi-fication, to specify typical classes in the setsof the data base. Any task that can be doneby traditional classification analysis can be ac-complished at least as well and almost alwaysmuch better by networks. Very important appli-cations in bioprocess engineering are the multi-variate exploitation of sensor arrays [387, 388]or the fault diagnosis [218, 389].AnANNcan betrained for pattern recognition. These patternscan be a intervals of time, cultivation states, fer-mentation series, images et cetera. If a version

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of one of these patterns, corrupted by noise, ispresented to a properly trained network, the net-work can provide the original pattern on whichit was trained [390, 391]. A very common prob-lem and the most popular application field ofANN is that of modeling, identification, or esti-mating the value or the state of a variable, givenhistoric values of itself and other variables; e.g.,they were used to estimate the state of microbi-ological cultures [392], the concentration of un-measurable mostly intracellular biological keycomponents [393 – 395], or for the identifica-tion of bioprocesses [396, 397]. ANNs can alsoreplace or act as process controllers. The ap-plications utilizing these neural-network-basedstrategies are wide ranging and vary from lin-ear time invariant to highly nonlinear time vari-ant systems [398, 399]. Typical advanced re-presentatives are the model-predictive control,the inverse model-based and the adaptive con-trol techniques. These methods have all in com-mon, that theANN reacts as a process controller,modified or expanded in some way by a refer-ence model or by an online training unit.

8.4. Modeling Aspects of BiologicalSystems

By definition, biochemical engineers are con-cernedwith biochemical systems, with reactionsand artifacts of biochemical substances like pro-teins or with systems employing growing cells.Even the simplest living cell is a system of sucha forbidding complexity that any forward math-ematical description of it is – in most cases – anextremely modest approximation. This situationprompts for bioprocess modeling the questionfrom a formal logical viewpoint: “What kindof relation ship should exist between the un-derlying physical system and its mathematicaldescription and which approaches should be ex-ploited?”.

Contemplation of this question leads, in onedirection, into the labyrinths of philosophy sci-ence. Guidance into this territory from the learn-ing guide Rutherford Aris led to John Casti’stwo-volume treatise “Reality Rules”, which ex-plores the general definitions of a mathemati-cal model as well as numerous specific exam-ples of models in different contexts [400, 401].Gratefully for engineers, who are (or should be)

engaged in discovery and development of use-ful modeling technology, Casti asserts an inex-tricable coupling between a model and its in-tended application: “Basically, the point of mak-ing models is to be able to bring a measure oforder or probable system performance to our ex-perience and observations, as well as to makespecific predictions about certain aspects of theworld by experience”. Therefore, mathematicalmodeling does not make sense without formu-lating before making the model what is its useand what problem it is intended to help to solve.Thus, a definition of a model can be given asfollows: A model is an image of a real systemthat shows analogues behavior in the importantproperties, and that allows within a limited re-gion in respect to the intended purposes a de-scription of the behavior of the original systems.

The emphasis represents the word important.A model should be always as simple as possi-ble; the degree of simplicity depends only onthe model purpose and on the modeler skills.Modeling the same real system with a differ-ent focus, the significant properties are mostlyquite different. Single parameters can representin one case the most specifying quantities andin the other case they are completely unimpor-tant. The focus and the intention of the modeldetermine not only the parameter, but also theproceeding in the formulation of the relations.The modeler must identify the important vari-ables (inputs, outputs, states) and their separateeffects, which in practicemay have a very highlyinteractive combined effect on the overall pro-cess. This is only possible if the modeler hata profound technological knowledge. Once themodel is established, it can be used, with rea-sonable confidence, to predict performances un-der differing process conditions. But this is onlyallowed under specific boundary conditions, un-der those which were used for the set-up. If youleave them, the describing ability gets lost. Anyextrapolation should be seen as critical.

8.4.1. Steps in Creating a Model

Model building is always a combination of the-oretical studies and practical experiments ina very iterative sequence (Table 15). The de-scribed sequence should be observed in order

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to get proper problem awareness and to mini-mize the efforts to reach the main targets. Theobjective of a scientific investigation is to im-prove understanding of a system by testing ahypothesis about that system. The formulationof the hypothesis is the most important action inthe modeling process. In a scientific process, aproblem or question is posed and stated as a hy-pothesis or theoretical statement of the problem.

Table 15. Step sequences for model building in bioreactionengineering. The steps must be seen as a loop whose termination isdetermined by the defined cost function

Step Action1 Proper definition of the problem (hypothesis), the

goals, and the objectives of the study, fixing ofpresuppositions, boundary conditions and constraints,defining the evaluation and error criteria

2 Analysis of the system, determination of thestructural elements, description of the key elements(variables, processes)

3 Running typical representative experiments,exploiting the parameter space of the controlvariables (experimental design)

4 Establishing the type of model by use of balances,physical–chemical–biological laws, available data,empirical equations, uncertainty treatment, problemformulation in mathematical or linguistic terms

5 Simplifying assumption (e.g. about mixing, processstructure and dynamics, metabolism, kinetics,neglecting aspects)

6 Choice and definition of the important processvariables: input and output variables, states, andparameters for the model

7 Simulation of the model, parameter identification,sensitivity analysis, determination of estimationproperties

8 Evaluation of the model by using test dataconsidering the evaluation criteria from step 1, ifnecessary starting again with step 1

For complex biological systems, however,the hypothesis may need to be translated intoa mathematical tractable building and the modelpredictions are compared to the observed dataas a basis for rejecting or accepting a hypoth-esis. This step also includes the definitions ofthe goals and the objectives. The purpose of themodel usually dictates the form and the detail aswell as the data that are required to develop andtest it. Typical goals can be the identification oftypical parameters or simulations, to predict re-sponses to a perturbation. If the purpose is to geta general idea on how the system would behaveunder a new condition, the accuracy may not beimportant, however, if the purpose is to deter-mine an “optimal” process trajectory, the modelaccuracy may be critical. Finally, the purpose

may be to derive the mechanistic principles un-derlying the system behavior. So, it is useful toobtain as much data from the system as possi-ble. It can be necessary to examine all differentportions of the curves individually. Even, smalldeviations can hold large insight into the intrin-sic physico-chemical effects of the biosystem.

In all real cases of system modeling, alsoboundary conditions or system constraints mustbe fulfilled. This requirement addresses two dif-ferent aspects. The first one considers the factthat the created model is only valid inside typi-cal boundary limits of the system variables andinside formulated constraints in respect to re-alized assumptions. The second one indicatesthat building up a model in bioprocess engineer-ing is always accompaniedwith an identificationstep for optimizing the parameters. But the pa-rameters are reasonable only within some lim-its, though they fulfill the mathematical optimalcriteria. Otherwise, they can loose any physi-cochemical meaning or interpretational ability.Having more than one parameter to determinecan lead, depending on the complexity of thesystem under consideration, to ambiguities. Inthis case, although available knowledge, e.g., inform of experimental data is represented well,the meaning of the parameters is outside of anyinterpretation scope. The extrapolation abilitygets lost.

To accomplish a mathematical treatment ofthe model in order to reach the predefined goals,an evaluation criteria must be formulated. Thiscan be seen as the mathematical description tothe formulation of the modeling purpose andis realized by formulating a scalar performanceindex. Its quantity gives information about themodel quality. In bioprocess engineering, it isnormally calculated from the results obtainedby the model compared with those of the cor-responding experimental data.

There are twocommonapproaches tofitmod-els to data, the least-square approach whereparameter are adjusted to minimize the sum(over all target values and existing data sets)of squares of the residuals between the calcu-lated and experimental data and the maximum-likelihood approach, whichmaximizes the prob-ability of the assumed parameterized probabilitydensity function [234]. Also, the above men-tioned boundary conditions and system con-straints must be considered in the performance

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index. This aspect is from the logical point ofview strictly necessary, but invokes in somecases crucial mathematical problems [402].

The system under study may consist of a sin-gle biomolecule, a cell, or a whole plant. Biolog-ical systems are normally open systems. Theyhave inputs and losses, although they may alsobe reduced to closed systems as in vitro stud-ies. Once the system to be studied is defined, theinputs and outputs are identified. Additionally,its processes, subsystems, and key compoundscharacterize the system. Processes are move-ments or changes in the system (e.g., absorp-tion,metabolism, transport, chemical reactions),subsystems are components of the whole system(e.g., cells by considering the fermenter as thesystem), and compounds/states are the quanti-ties under consideration (e.g., metabolites).

Almost all modeling approaches in biopro-cess engineering involve an identification stepof unknown parameters in the model by ex-perimental data. The aim of the experiment isto obtain data to confirm or reject a hypothe-sis. It is essential to decide from which envi-ronmental and operating conditions measurablequantities should be gained, and to which preci-sion and in which measuring range. The phys-ical, chemical, and biological ones should bewide ranging, and should also include the qual-ity of the whole environment and pre-cultures orpre-states. All measurable bioprocess data mustbe treated initially as variables. Afterwards, itcould be decided, whether they remain constantor not. Automated measurement and control ofbioprocesses, presently an art but – hopefully –routine in the near future, generates a tremen-dous amount of data. This requires judgmentof the importance of these data for documen-tation to reduce data effectively without loss ofvaluable information. Experts – either human orcomputer – have the data and the deterministicknowledge to trace observed behavior back tothe physical, chemical, and physiological roots.

Once the biosystem is structured and experi-ments are done, themodel should be fixed. Thereexist different approaches to find the best ap-propriate model type for the underlying prob-lem (see below). In principle, the model shouldrepresent the theories or hypothesis about howthe systemworks. Normally, there exist differentstructures and parameter constellations whichwill fit a particular set of data. The crucial task

for the modeler is to decide which one should beaccomplished from the viewpoint of deep under-standing of the biosystem. It is important and inmost cases very effective that new approachesare built up upon information of earlier studiesgained from literature. Differences between themodels should be evaluated to choose the modelmost acceptable with current and new biologi-cal knowledge. Since the problem of parameteridentification and model verification increasesrapidlywithmodel complexity, one should beginwith as far as possible simplified assumptionsand withdraw them step by step, if the modelquality is judged to be not sufficient. In this way,a most simple initial model grows step by stepin complexity and accuracy, without becomingtoo complicated.Modeling includes not only theselection of correct model structure, but also thedetermination of the undefined system parame-ters.

To give a final statement about the quality ofmodel, its robustness has to be evaluated. Thisconcerns sensitivity, identifiability, and stabilityaspects. Sensitivity refers to the relative influ-ence of individual parameters on the solution.Identifiability refers to the uniqueness of themodel and of its parameters. They should be de-termined in that way that a data-fitting processshould return the same estimates of parametersmore or less independently of the starting pointsfor estimation and noise in the data. Stability isthe behavior of a systemwith respect to a pertur-bation. Since nearly all real biological processesare time-variant and highly nonlinear in nature,the powerful collection of methods concerningthe aspects of controllability, stability, and oper-ability of linear systems cannot be applied [403].

Sensitivity analysis is one of the most impor-tant tools to optimizedmodels. It refers to calcu-lations and analyses performed to describe therelative dependency of themodel parameters. Itsanalysis can be used formodel validation as wellas for fixing variables to constant values in orderto decrease the degree of freedom. The sensitiv-ities are also very important for interpretationpurposes, e.g., to detect more or less importantpartial processes or quantities of the biosystem.From a practical perspective, it represents calcu-lations which reveal the impact of assumptionsmade in conjunction with model development.A distinction can be made between point sensi-

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tivity, relative sensitivity, and overall sensitivity.Considering the model⇀y (t) =

⇀y (t,

⇀p )) (14)

with t representing the process time and⇀p the

parameter to identify;⇀y (t) are the measurable

output variables. The relationship between thestate variables and the point sensitivity can beshow using the first two terms in the Taylor ex-pansion.⇀y (t) =

⇀y (t,

⇀p 0)+GT ∂

⇀p +K (15)

where G is the partial derivative matrix of⇀y (t)

with respect to⇀p (t). An element of G is de-

fined as the point sensitivity of yi with respectto pj . An alternative scale-free sensitivity is therelative sensitivity Sij

Sij=pi

yi

∂yi

∂pi(16)

To represent the overall sensitivity of a statevariable as opposed to point sensitivity to pa-rameter pi over some time interval, SG

ij can bedefined

SGij=

1T2−T1

T2∫

T1

∂yi

∂pjdt (17)

As applied to modeling, identifiability is amathematical concept, which attempts to steermodel development largely on the basis of ananalysis reflecting whether features of a modelfor a presumed system can be extracted from aproposed experiment. Selecting a standardizedmethod for an experiment directed for elucidat-ing a model and its features involves selectingtime points for observations, observation sites,sites for tracer input, as well as forms of tracerapplication. In bioprocess engineering, it is sel-dom possible to sample all units, and the numberof units to which tracer can be applied is limited.Each choice we make here stands for a chanceof mitigating against identifying aspects of themodel, even in an error-free situation. If the lackof identifiability makes it impossible to estimateunique parameters or test relevant hypothesis,we should like to know this before conductingthe experiment or interpreting the modeling re-sults. If the experiment is adequate to identifythe model, we are still left with the estimationproblem, that of resolving the parameters amidst

noisy data and to reach the observability. Con-sider the following general representation of atime-variant nonlinear system:

⇀z (t) =T ∗

(⇀x

(t),

⇀y (t),

⇀p ) with

⇀z

0=

⇀z (t=t0)

(18)

A system is defined as observable at the time

t0, if, admitting an arbitrary initial state↼z

0, a

finite point of time t1>t0 exist, so that assuming

known input⇀x (t) and output

⇀y (t) trajectories

the state⇀z (t) can uniquely be determined in a

time interval t0<t<t1. In contrast to linear sys-tems, this problem involving time-variant non-linear equations as they arise in bioprocess engi-neering results in nonlinear algebraic equationsfor the solution of which similar powerful meth-ods do not exist. Consequently, several assump-tions and individual specifications and transfor-mations have to be made to make the problemmathematically tractable with reasonable effort[404].

8.4.2. Reasons for Making a Model

There is a zoo of mathematical models in thebiochemical engineering and mathematical bi-ology literature. Many of these appear to naiveas well as to sophisticated reader to have lit-tle more purpose than calculating numbers andwriting scientific papers which conform reason-ably to experimental data. This is, in itself, not adistinguished endeavor; it is not particularly dif-ficult, and it teaches little. One reason that math-ematical biology receives so little respect frombiological scientists – and is generally not recog-nized as a credible research tool in biological sci-ence and a biotechnological discovery – is thatit represents the failure to communicate clearlyand persuasively the reasons and the motivationfor constructing a model. Sometimes the mod-eler himself has not clearly asked this questionwhile doing the work, sometimes biochemicalscientists make one experiment after the other,not reflecting which mathematical approach ex-ists to enhance the evaluation or interpretation ofthe experiments. However, modeling as a typicaldomain of engineering gets increasing interest inthe study of biological systems. The challenge of

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modeling biological systems is not to determinean arbitrary function to fit the data but to use themodeling process to understand the system.

Modeling can be used to determine the struc-ture of a system where structure refers to therelationships between various parts or processesof a system. A model can be used to determinethe type of relationship aswell as the sequence ofevents that occur between the various substancesof interest. In some systems, this informationmay be known, whereas in others the structurecan only be inferred from the data. Modelingimproves understanding, and it is through un-derstanding that progress is made. In formulat-ing a mathematical model, the modeler is forcedto consider the complex cause-and-effect se-quences of the process in detail, together withall the complex inter-relationships that may beinvolved in the process. The comparison of amodel prediction with actual behavior usuallyleads also to an increased understanding of theprocess, simply by having to consider the waysin which the model might be in error. The resultsof a simulation can also often suggest reasons asto why certain observed and apparently inexpli-cable phenomena occur in practice.

Models can be used to determine parametersof interest, such as pool sizes clearance rates ortransport rates. If the purpose is to accuratelydetermine a particular parameter, it is impor-tant that the model can be uniquely determined.However, themodelmust also be consistent withknown biological information. A model may bewell-determined but incorrect.

The purpose of the model may be to deter-mine the interaction of parts of a system.Modelscan integrate information on a number of sub-systems into an integrated form to represent theprocess under consideration in the whole sys-tem. Because of the interactions, dependencies,and feedback of the processes, large complexsystems can only be understood by using mod-eling. An example could be to link metabolismin one tissue with metabolism in another, or tolink metabolism of a nutrient in one form withthe metabolite in a second form. Large systemsand systems with dynamic properties can onlybe understood with the use of models. Becausemany processes in large systems occur simulta-neously and dynamic systems are accompaniedby complex interactions between processes, it isunlikely that responses to perturbations could be

predicted based solely on intuition and experi-ence without a model.

Models can be used to simulate inputs into asystem at various sites and to simulate short-termaswell as long-term responses. Experiments canbe simulated rapidly using a model to predictlikely scenarios before undertaking expensiveexperimental data collection. While models willnever replace experiments, they can be used toavoid experimentswhere insufficient or inappro-priate data will be collected for testing a certainhypothesis. Models may be used predictive fordesign and control. Once the model has beenestablished, it should be capable of predictingperformance under differing sets of process con-ditions. Thus, mathematical models can be usedfor the design of relatively sophisticated controland optimization algorithms, and the model, it-self, can often form an integral part of the controlalgorithm. Optimization usually involves con-sidering the influence of two or more variables,often one directly related to profits and one re-lated to costs. For example, the objective mightbe to run a reactor to produce a product at a max-imum rate, while leaving a minimum amount ofunreacted substrate.

An important use for models is to iden-tify sites of change in a system when studiedunder different conditions. In some cases, thealtered condition may result in large changesin the kinetic curves and several pathways orit may result in a subtle change in the datacaused by a large change in only one parame-ter. A model helps to identify which parameterschange between the conditions and the degreeof change. The conditions may be an untreatedversus treated state, a healthy subject versus adiseased subject, or a normal versus high intakeof a nutrient.

Models form valuable tools for teaching theprocess of scientific inquiry and for challeng-ing students to think creatively and quantita-tively about a system. Models can be used todemonstrate properties of a system, teach princi-ples (such as feed-back loops, saturation kineticsetc.), test theories, and design studies. With theincreased speed and convenience of computers,the availability of modeling software and the ac-cess to it, the researcherswho are developing andpublishing models, need to be cognizant of this.They need to make their models understandable

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and accessible to the biological community atlarge as well as to students.

Models help in experimental design. It is im-portant that experiments be designed in such away that the model can be properly tested. Of-ten, the model itself will suggest the need fordata for certain parameters, which might oth-erwise be neglected, and hence the need for aparticular type of experiment to provide the re-quired data. Conversely, sensitivity tests on themodel may indicate that certain parameters mayhave a negligible effect, and hence these effectscan be neglected both from the model and fromthe experimental program.

8.4.3. Different Types and Basic Approachesfor Building a Model

Incomplete knowledge of the dominant biolog-ical pathways as well as low availability of sen-sor information about the current physiologicalstate is the characteristics of biochemical pro-cess modeling. From system analysis, as alreadydescribed above, the definitions of the describ-ing variables (e.g., inputs, outputs, or states) canbe given.

In principle, there can be postulated thatthese variables are intrinsically interconnectedby means of a set of – indeed real existing – dif-ferential equations, as it is already described inprevious chapters. But even being aware thatthese relations may exist in principal, for math-ematical treatment they can only be specifiedin their structure and intercorrelation as wellas in their parameter constellation in some ex-ceptional cases with a high degree of accuracy.Thus, when dealing with modeling of real bio-logical systems, uncertainty in various aspectscan never be avoided [405]. In bioprocess engi-neering, uncertainty is an inseparable compan-ion of any modeling approach.

Uncertainties in engineering systems can bemainly attributed to ambiguity and vaguenessin defining the architecture, parameters, andgoverning prediction models for the systems.The ambiguity component is generally due tononcognitive sources. These sources include 1)physical randomness; 2) statistical uncertaintydue to the use of sampled information to estimatethe characteristics of these parameters; 3) lack ofknowledge; and 4) modeling uncertainty which

is due to simplifying assumptions in analyticaland prediction models, simplified methods, andidealized representations of real performances.

The vagueness-related uncertainty is due tocognitive sources that include 1) the definition ofcertain parameters, e.g., structural performance(failure or survival), quality, deterioration, skill,and experience of construction workers and en-gineers, environmental impact of projects, con-ditions of existing structures; 2) other humanfactors; and 3) defining the interrelationshipsamong the parameters of the problems, espe-cially for complex systems. Other sources of un-certainty can include conflict in information andhuman and organizational errors [406].

Once the systems are defined, the relationsbetween the outputs and states based upon theinputs and states have to be formulated to per-form simulations. Today we have many typesof models, which can be categorized in variousways such as deterministic versus nondetermin-istic, logical (linguistic) versus mathematical-equations-based or data- versus knowledge-driven. Physical models can be the realizationof the original system in a different (usuallysmaller) scale or with structural modifications.A second type of physical models is obtainedby turning to a different physical system, e.g.,from the original biosystem to a correspond-ing electrical circuit [407]. Another group re-presents verbal models. They give a linguis-tic representation of our knowledge about thesystem, usually formulated as rules, e.g., “IFthis or that happens THEN the system reactsby. . .”. They are widely applied in the areaof artificial intelligence, namely experts sys-tem [408, 409]. In the form of fuzzy-basedsystems, they gain increasing industrial inter-est in biotechnological control theory, which isdue to their effective treatment of qualitativeknowledge of experienced technologists in pro-cess control strategies (see Section 8.3.1), whichcould only hardly realized in terms ofmathemat-ical equations [410 – 412]. Mathematical mod-els form the third class. They can be classifiedfurther depending on the mathematical formal-ism or the methods for model building. Theo-retical models as mechanistic models are basedon physical and chemical laws and our knowl-edge about the inner structure and function of thesystem. In contrast, experimental or nonmecha-nistic mathematical models try to give –without

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looking into the interior of the system – only adescription of the observed reaction of the sys-tem in response to a certain forcing signal.Many“mechanistic” models in biotechnology are ac-tually due to their oversimplification quite closerto black-box models than to real mechanisticmodels, although mostly mechanistic interpre-tations are given [413 – 415].

White-box model. Asdescribed above (Sec-tion 8.3.1), the relation can strictly be mech-anistic, considering the four differential equa-tions for mass and heat balance and correspond-ing transformations. In this so-called white-boxmodeling strategy, the model development ismainly driven by the knowledge of the rel-evant mechanisms, macroscopic balances andpredefined parameters. Indeed, if all the biosys-tem underlying mechanism are known and canbe described by means of the above-specifiedequations and the accompanied parameters areknown, the resultant white-box model is gen-erally applicable and show good extrapolationproperties. However, it is not an easy task to re-veal all the relevant mechanisms and quantifythese mechanisms correctly when dealing withbiological processes. The metabolic modelingapproach is one such approach and has been thefocus of recent attention [416, 417]. Addition-ally, several attempts have recently beenmade inutilizing metabolic information for online iden-tification and control as a consequence of thewhite-box strategy [418, 419].

Black-box model. The black-box model is,on the other hand, mainly driven by measureddata obtained from the process, either online oroffline. In terms of mathematical expressions,probability density functions for the importantparameters are specified by means of availabledata. Based upon these distributions, the time-dependent system behavior can be forecasted[420 – 422]. The earliest stochastic biosystemmodel is perhaps that proposed by Yule, tomimic the evolution of a new species withina genus [423]. However, they are mainly usedfor systems only, where isolated parameters aretreated. It is unrealistic that for a real biologicalMIMO system (multiple input multiple output),all joint probability densities, which are neces-sary to describe the keybehavior, canbe approxi-mated by performed series of experiments. Even

for systemswithout the existence of any driving-force function the expense for their determina-tion is disproportional to the possible result. Ifinputs exist, which direct the system in a defineddirection, reaction patterns must be formulatedadditionally in terms of probabilistic equations.

Another type of black-box models representsthose where a specific functional structure is as-sumed, which is combined with a subsequentparameter estimation to adapt to the specific un-derlying problem (e.g., polynomial regression,ANN, chemometric models). However, on ap-plying this, it has to be considered that the sim-plest and usually fairly accurate method for pre-dicting values of the next data point in a timeseries from a “well-behaved” biosystem is toassume that it is the same as the present datapoint, a principle known as the first-order trivialpredictor. Similarly, in the second-order trivialpredictor the value of data point n+2 is equalto that for point n plus twice the signed dif-ference between the values of point n+1 andn. If any complex nonlinear predictive modellike ANN can do no better than the first- andsecond-order trivial predictors –which are in-deed very often concurrently – then their devel-opment is a waste of time. Nevertheless, espe-cially ANNs have been the focus of much atten-tion for model development and have alreadybeen applied to various biochemical processes(Section 8.3.2). Although the main advantage ofthe development of black-box models is that areasonably accuratemodel can be obtainedwith-out detailed mechanistic system knowledge, itshould be noted that its accuracy depends onlyon the quality of the available input and out-put data. As black-box models are not believedto have any extrapolation properties, one has toobtain a large body of data for process identifi-cation by employing the relevant input variablesin a wide range of fluctuations [218].

Markov chains. Another very attractivewayto use probabilistic approaches are the formu-lation of the problem by means of so-calledMarkov chains [424, 425]. A Markov processallows the modeling of uncertainties in real-world systems that evolve dynamicallywith timewithout using mechanistic approaches. The ba-sic concepts of a Markov process are those ofstates of a system and of state transitions. In spe-cific applications, the modeling “art” is to find

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an adequate state description such that the as-sociated stochastic process has the Markovianproperty that the knowledge of the present stateX(t) is sufficient to predict the future stochas-tic behavior of the system X(t+1). The movingof i=t to j=t+1 is defined by means of the tran-sition probability Pij [426]. For bioprocess en-gineering, controlled Markov chains or Markovdecision processes (MDP) possess a high poten-tial, for simple modeling as well as for processoptimization [427, 428]. Applied in bioprocessengineering, they involve the selection of an ac-tion Ut from a set of containing a finite numberof A actions when observing the current state ofthe system. The problem is to define the prob-ability that the system goes to a state j from astate i under the action of a:

P{Xn+1=j|Xn=i,Ut=a}=Pij(a) (19)

The controlled Markov chain is completelydefined by the optimality criterion for calcula-tion of the transition probability matrix, the statevectorX, the action setA, and the decision epoch.For bioprocess modeling, concentration valuesgrouped in clusters are often used as state vari-ables; considering more than one state variablefor each one a separate model is constructed.One great disadvantage of this approach repre-sents the huge effort to determine the transitionprobabilitymatrixPij (a) for each control action,especially ifmore than onemanipulated variableshould be considered [428].

Grey-box model. A grey-box model may bedefined as a suitable combination of a black-box principle and a white-box model. The ex-pectation is to obtain good interpolation andextrapolation properties. Especially, hybrid ap-proaches,where portions fromeachprinciple arepieced together based upon the amount and thestructure of available knowledge, gain increas-ing importance. From different points of view,such hybrid systems can be build up, i.e., ana-log or discrete consideration, stochastic or deter-ministic combinations [429 – 431]. Only someimportant concepts for bioprocess engineeringshould be presented here. In some cases, sta-tistical methods are included into a mathemat-ical discrete formulation, this concerns mainlyKalman filtering. They exploit statistical infor-mation about the error distribution of the state

and a measurement values, and their propaga-tion with time to predict online a future statebased upon new measurements and already ex-isting quantities. Their potential in bioprocessmodeling and control is obvious [432 – 434].

From the structural point of view, hybrids aredescribed in parallel and serial configurations.In the former case, the black box is placed inparallel with a white-box model and its role isto describe and to weight the difference bet-ween a white-box model and the real process.This model has been applied to several pro-cesses, but its performance might be limitedto some extent to the viewpoint of extrapo-lation properties [435]. In the serial structure,the black box is placed in series with a white-boxmodel. Different applications are described.Combined with a fuzzy expert system, the hy-brid was applied to model a real-time fed-batchculture for baker’s yeast production, a produc-tion scale beer-brewery fermentation, and mam-malian cell cultures [431]. To show the betterextrapolation properties of grey-box-model ex-emplarily, the performance of a serial-type grey-box model containing a neural network and amore knowledge-driven white-box model werecompared with that of a more data-driven black-box model in the enzymatic hydrolysis of peni-cillin G to 6-amino-penicillanic acid and phenylacetic acid by using the enzyme penicillin acy-lase. The grey-box model was shown to havesignificantly more reliable extrapolation prop-erties than the black-box model [436].

Also linguistic models such as fuzzy modelscan be seen as a special type of grey-boxmodels.Although the intrinsic behavior cannot be for-mulated by means of mathematical equations, apriori knowledge is included into the model bythe linkage of various “IF. . ..THEN . . . “ con-clusions, which is built up by the knowledgeof highly specialized staff. Their fine-tuning isoften accomplished by using data from experi-ments [437].

Being aware that different approaches ex-ist how biological systems can be modeled andtreated computationally, an important aspect fora successful application is the proper choicewhich principle fits best to the specific problemand its boundary conditions. Inmost cases, morethan one specific approach can be applied inprincipal. The most important aspect for choos-

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Table 16. Different types of models based upon the system knowledge

High System knowledge LowModel deterministic stochastic, statistical expert systems approximation classificationExamples unstructured,

structured, CFDaMarkov chains,maximum likelihood,Kalman,

fuzzy models, settheory

neural networks, PLSb,PCRc

pattern recognition

a CFD, computional fluid dynamics.b PLS, partial leastsquare.c PCR, principal com-ponent regression.

ing the right model should be the system knowl-edge about the underlying problem (Table 16).

Not only the amount of system knowledge isdecisive, but also its structure, the quality andquantity of accompanied data, and the purposefor which the model should be used is of specialimportance.As abasic principle for buildingupamodel, it could be postulated “use as much a pri-ori information as possible”. In mechanical en-gineering and fluid dynamics, strictly determin-istic approaches are typical, where the underly-ing differential equations are “exactly” known.Thus, there exists no reason to deal with uncer-tainties and the problem is forwarded. However,this situation generally does not exist in bio-process engineering. A biological system con-sists of a very complex, strongly regulated re-action network, where only some simple mech-anisms can be formulated in terms of mathe-matical discrete forwarded formulations. Thus,one has to deal in a very strong degree with un-certainties. If one is able to formulate a set ofdifferential equations, which represents not anexact image of the underlying mechanism but isdescriptive enough for the existing problem andfor the modeling purpose, one should formulatesuch a model and append a subsequent parame-ter estimation step where statistical properties oravailable data are exploited to consider unknownor not structural included information. In thecase where a formal-kinetic approach cannot beformed but qualitative knowledge is available orcan be extracted from available data, expert sys-tems should be preferred. Especially fuzzy sys-tems as a special representation of an expert sys-tem show advantages in modeling approachesbecause of their smoothness concerning the sys-tem output. In the case where a priori knowl-edge of the system is not available but pure datafrom a well-defined experimental design do ex-ist, approximationor classification approach like

ANN or chemometric models should be used. Inthose cases, they are the only alternative to real-ize appropriate prediction ability. They assume asystem structure, which possesses no causal re-lation with the underlying real mechanism, butis able to incorporate and describe the systemdynamics. They possess normally good approx-imation abilities, but their extrapolation prop-erty reveals a low reliability especially if outputsshould be forecasted where the correspondinginputs are outside of the trained data space.

9. Special Applications inBiotechnology

The following chapters highlight some specialaspects in biotechnology. However, there existeven more interesting applications in biotech-nology. The reader should regard the areas cho-sen by the authors as examples for the wide dis-tribution of biotechnology in the daily living.

9.1. Mammalian Cell CultureTechnology

9.1.1. Introduction

Mammalian cell culture technology has becomea major field in modern biotechnology, espe-cially for the area of human health, and fascinat-ing developments achieved in the last decadesare an impressive example for an interdisci-plinary interplay between medicine, biology,and engineering [438, 439]. Among the classi-cal products from cells, we find viral vaccines,monoclonal antibodies, interferon, as well asrecombinant therapeutic proteins. Tissue engi-neering or gene therapy open new, challengingareas [440].

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Under “mammalian cell culture” or “ani-mal cell culture” is to be understood the cellsof a mammalian, isolated from specific tissues(i.e., skin, liver, glands, etc.) and further culti-vated and reproduced in an artificial medium(Fig. 31) [441, 442]. During cultivation of mam-malian cells in vitro, outside of a living organ-ism, some distinct difficulties arise from the ex-traction of the cells from a “safe” tissue. Slowgrowth rates with doubling times between 18and 28 hours, low productivity, a high sensi-tivity against shear stress due to the lack of acell wall [443] and high demands in respect tothe growth medium challenge the techniques re-quired for mammalian cells. Furthermore, manycell lines grow adherent, and a suitable surfacefor attachment must be provided for these cellsto proliferate. As of the origin from multicellu-lar organisms, mammalian cells still hold the ge-netic program of inducing their own cell death, aprocess called “apoptosis” or “programmed celldeath” [444 – 446]. This can limit culture pro-ductivity in biotechnological processes.Anothermajor problem is the finite lifespan of primarycells, which die after several doublings in vitro.This problem was solved by transforming pri-mary cells into immortal “established” or “con-tinuous” cell lines. The engineering targets re-lated to mammalian cell culture technology canbe identified as:

– Production of a valuable product (viral vac-cine, protein) from mammalian cells, devel-opment of processes for the efficient produc-tionof the desiredproduct froma laboratory toa industrial scale under the restrictions givenby the cell properties (products from cells).

– Development of processes for the cultivationof organic tissues, which can be used as sub-stitute to original tissues (tissue engineering,gene therapy, cells as products).

Much effort has been put into the develop-ment of mammalian cell culture technology, andgreat progress was achieved in the last decades[447, 448]. Mammalian cells may now be cul-tured to very large volumes (up to 20 000 L) toprovide the necessary quantities of a desired pro-tein.Media that used to contain up to 10%serumwere continuously improved, and the cultivationin defined serum-free and even chemically de-fined, protein-free media is now common formost relevant industrial cell lines. Bioreactors

were developed that provide the required low-shear-stress environment by, e.g., introducinggentle agitation and aeration with slow movingstirrers in stirred tanks, designing special aera-tors for air-lift reactors, or by separating the cellsfrom the stressful conditions such as hollow-fiber, fluidized-bed, and fixed-bed reactors. Inindustrial scale, adherent cell lines can be cul-tivated on microcarriers (e.g., for vaccine pro-duction) or are adapted to grow in suspension,e.g., cell lines derived from baby hamster kid-ney cells (BHK) or chinese hamster ovary cells(CHO).

Figure 31.Morphology of (a) suspendable and (b) adherentmammalian cells (bar approx. 30 µm)

Genetic engineering contributes a great dealto recent progress [449 – 451]. With this tech-nology, functional proteins can be produced byintroducing recombinant DNA into cell lines,e.g., chimeric (humanized) antibodies are pro-duced for in vivo applications in transfectomaor recombinant CHO cells. New promoters havebeen developed to enhance productivity, andproduct titers over one gram per liter have beenreported for some industrial cell lines. Further,novel cell lines can now be constructed from

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primary cells without loosing functionality bygenetically induced proliferation [452, 453].

The following will provide a basic under-standing of the specific requirements of mam-malian cells, will describe state of the art processtechnology for cultivation of these cells and willgive an outlook to future prospective. A com-prehensive overview on cell culture technologyis given by Ozturk and Hu [454].

9.1.2. Products from Mammalian Cells

A detailed overview on products from mam-malian cells is given in [454, 455].Within “prod-ucts from cells”, viral vaccines [456] againstpolio, hepatitis B, measles, or mumps for hu-man use, rubella, rabies, or food-and-mouth dis-ease (FMD) for veterinary use play an impor-tant role. Viral vaccines are produced very effi-ciently by means of a cell-based vaccine tech-nology. For this, mainly primary cells, diploidcells, or permanent cell lines (e.g., VERO) areapplied, recently even recombinant cell lines. Abreakthrough for the large-scale production ofviral vaccines with anchorage dependent cellswas the development of microcarriers in the late1960s, allowing cultivation in stirred tanks inthousand-liter scale. New targets for cell-basedvaccines are human immunodeficiency virus(HIV), herpes simplex virus, or influenza. Fur-thermore, newdevelopments include geneticallyengineered or DNA vaccines (→ Immunother-apy and Vaccines).

Monoclonal antibodies [457] have become avaluable tool for diagnostic purposes as wellas in therapy. Antibodies synthesized by B-lymphocytes play an important role in theimmunosystem of mammalians. Traditionally,polyclonal antibodies were isolated from bloodsamples. In the 1970sMilstein and Kohler de-veloped a technology to generate hybridomacells producingmonoclonal antibodies [458].Asof the specific binding, monoclonal antibodiesare widely used for diagnostics, where tens ofthousands different monoclonal antibodies areavailable. The importance of monoclonal anti-bodies as therapeutic agents has evolved only re-cently, as immunogenic mouse antibodies werereplaced by chimeric, humanized, or human an-tibodies. Fields of application are organ trans-plantation (OKT3), cancer diagnostic and treat-

ment, rheumatoid arthritis, leukaemia, asthma,ormultiple sclerosis. Nowadays several antibod-ies are produced in kilogram quantities [440].Modern recombinant techniques focus on newantibody formats such as fragmented antibod-ies (FAbs) or bivalent antibodies with a broadspectrum of applications.

Glycoproteins are a further important groupof products produced bymammalian cells. Start-ing with the production of α-interferon as ananti-infectious drug by (nonrecombinant) Na-malwa cells in the late 1970s, nowadays a grow-ing number of glycoproteins for treatment of awide variety of diseases are produced by meansofmostly recombinantmammalian cells. Promi-nent examples are cytokines (e.g., interferonsand interleukins), hematopoetic growth factors(e.g., erythropoeitin for treatment of anemia),growth hormones, thrombolytic agents (e.g., tis-sue plasminogen activator [tPA]), coagulationfactors (factor VII, factor VIII, factor IX etc.),and recombinant enzymes (DNAse) [440].

Recombinant proteins may be produced byeither bacterial, yeast, ormammalian cells. Froma technological point of view, hosts such as bac-teria or yeast have an advantage with respectto growth rate, final cell density, and productconcentration. Nevertheless, mammalian cellsare preferred for those proteins requiring aspecific, humanlike glycosylation pattern [459,460], which is difficult to obtain in other hostsystems. Another problem for microorganismsis the maximal size of the produced proteinwhich must be below a molecular mass of ap-prox. 30.0 kDa. Further, in contrast to extra-cellular release of most proteins produced inmammalian cells, products from microorgan-isms are often accumulated intracellularly in “in-clusion bodies”. This demands a more complexdownstreaming. Besides this, for mammaliancells important parameters such as product yield,medium requirements, and growth characteris-tics (suspendable, shear resistant) have been sig-nificantly improved.

Proteins produced in the milk of transgenicanimals have started to compete against “classi-cal” mammalian cell culture [461 – 464]. Theproteins are usually expressed in enormoustiters, over one order of magnitude higher thanthat obtained from cell cultures. The price forthe production in transgenic animals will prob-ably continue to decrease significantly due to

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the development of more efficient reproductiontechnologies. These newapproacheswill signifi-cantly decrease the time for the development of aproduct. Among the disadvantages of transgenicanimals, we find a more sophisticated down-streaming (high protein loads), the long devel-opment time, the inability to produce proteinsthat might impair the health of the animal (e.g.,insulin), higher risk of viral contamination andthe possibility of prion contamination (scrapie,BSE).

The novel field cells as products includesthe development of artificial organs (tissue en-gineering of liver, kidney) and tissues (skin, car-tilage, bone), or the expansion of hematopoi-etic cells for bone marrow transplantation orgene therapy. The exciting prospects of tissueengineering [465 – 467] are outlined in Section9.2. Somatic gene therapy implies transfectionof a specific gene to cells isolated previouslyfrom a patient suffering from a genetic disease[468 – 470]. The transfected cells are then rein-troduced into the patient. Gene therapy involvestransfer of genetic material, encoding therapeu-tic genes and the sequence necessary for expres-sion to target cells to alter their genetic codefor a desired therapeutic effect. Many diseasesare originated by gene defects. Expression ofthe transferred genes can result in the synthesisof therapeutic proteins or correction of a genedefect. Gene transfer could also lead to desiredapoptosis or inhibition of cell proliferation. The-oretically, gene therapy can be applied for repair-ing single entailed gene defects, acquired genedefects such as chronic infectious diseases, mul-tifactor genetic diseases such as cardiovasculardisease, and finally cancer. Since gene therapywas applied the first time in 1990, the numberof clinical trials increased. Today gene therapyis widely used in ongoing clinical trials for thetreatment of cancer and infectious diseases suchas human immunodeficiency virus (HIV) infec-tion.

Cell culture products are currently usedmainly as medicines or in diagnostics. Formedicines, some products have a demand of500 kg/a and generate $ 1–2 billion dollars inrevenue. Among these are tissue plasminogenactivator (tPA), erythropeithin (EPO) products,Remicade r© (Centocor) or MAbThera r© [440].The pipeline for new biopharmaceutical thera-peutics targets a huge number of diseases (e.g.,

425 for U.S.A in 2002 [471]), most of them forcancer therapy, infectious diseases, autoimmunediseases, or AIDS/HIV. It can be expected thatat least some of these will find their way to themarket. Furthermore, new products or medicalprotocols can be expected for tissue engineeringand cell therapy.As for some compounds patentsare already expired or will expire in the near fu-ture, there will be amarket chance for biosimilaror generic products. On the other hand, the costsfor target identification, clinical trials, and pro-cess development will increase (to date approx.$ 0.5–1 billion) and only block busterswillmakeit finally to the market.

9.1.3. Cell Types

In general, mammalian cells relevant for indus-trial processes can be divided into the followinggroups [441, 472]:

– Primary cells have been isolated from a tissueand than taken into culture (primary culture).

– Permanent or established cell lines originatefrom a primary culture, but because of sometransformation became able (at least theoret-ically speaking) to divide and proliferate in-definitely. Those cell lines are kept in a cellbank and are very often used as host cells forthe expression of recombinant proteins.

– Hybridoma cells are cells which are obtainedthrough the fusion of lymphocytes and tumorcells and which are able to express mono-clonal antibodies.

The common procedure for the isolation of amammalian cell from a tissue involves the fol-lowing steps: first, a small piece of tissue is de-composed into single cells or cell clots mechan-ically, enzymatically (e.g., typsin [473], colla-genase), or through a combined procedure. Thecells are separated from the enzymes by cen-trifugation and resuspended in culture medium.After that, the cells are inoculated into a spe-cial glass or plastic vessel with flat bottom. Theanchorage-dependent cells start to adhere to thebottom surface and, after a lag phase, start to di-vide by mitosis. Such a cell culture, in which thecells come from a differentiated tissue, is calleda primary cell culture. The most critical point inthe isolation of primary cells is to avoid externalcontamination by practicing aseptic techniques.

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When the primary cells have covered the bot-tomof the culture vessel almost completely, theyare enzymatically cleaved (i.e., trypsin) fromtheir support and used to inoculate new cultures.This subculture gives origin to the so-called sec-ondary cultures. Part of the cells is stored deeplyfrozen in liquid nitrogen, to remain as a safetystock, from which it is possible, at any time itmight be necessary, to get enough “fresh” cellsto start a new series of subcultures for mass pro-duction. With the primary cells, it is possible torepeat subcultures several times. However, theprimary cultures have a finite lifespan and there-fore, after a certain number of doublings (from50 to 100 times), the cells cease to grow and die[441].

A finite growth capacity is a characteristic ofall cells derived from normal mammalian tis-sue. A cell can be considered as normal when itshows a certain set of characteristics [474]:

– A diploid number of chromosomes (i.e., 46chromosomes for human cells) with which itis shown that no gross chromosome damagehas occurred.

– Adherence: the cells require a surface togrow attached to (anchorage dependent). Thegrowth phase extends until the cells reach astage of confluence (contact inhibition).

– A finite life span in culture.– Non malignant: the cells are not cancerous,i.e., they do not cause tumor in mice.

In the 1960s, normal cells were required forthe production of human vaccines in order toensure the safety of these products [441].

Not all cell types produce exclusively pri-mary cell cultures, which after a limited numberof “passages” die. Some cells acquire an infinitelifespan and such a population is usually calleda “permanent” or “established cell line” (Table17). These cells have undergone a “transforma-tion”, i.e., they have lost their sensitivity to thegrowth control mechanisms. Transformed cellscan also lose the characteristic to grow adheredto a surface and thus become able to grow in sus-pension. These transformations are also some-times reflected in the chromosomes, changingthe genotype of the cells. Transformed cells canbe easily grown in relatively simple media with-out addition of expensive growth factors.At first,transformed cells were identified just by chance,but nowadays there are techniques to cause cell

transformation and “immortalisation”, as for ex-ample the treatment with mutagenic substances,with virus or with oncogenic substances [441].Transformation of cells in vitro shows some sim-ilarities with carcinogenese in vivo, but is notidentical to it; for instance, not all the trans-formed cells are malignant. On the other hand,all cells that are isolated from tumors (i.e, HeLaor Namalwa Cells) can be kept in a permanentculture.

Table 17. Examples of permanent cell lines important for researchand production (data from [441], modified)

Cell Line Origin ApplicationBaby hamster kidneyBKH-21

syrian hamster, 1963 adherent cells, but canbe adapted tosuspension, FMDvaccine, rabiesvaccine, recombinantproteins (Factor VIII)

Chinese hamsterovary, CHO-K1

ovary of chinesehamster, 1957

adherent cells, but canbe adapted tosuspension,recombinant proteins(HBstg, tPA, FactorVIII)

COS monkey kidney transient proteinexpression [475]

NAMALWA human lymphatictissue

alpha-interferon

HeLa tumor in a cervicalvertebra

Fast-growing tumorcell line that wasisolated in thebeginning of the1950s.

HEK-293 human embryonickidney, 1977

transient proteinexpression

MDCK rabbit kidney adherent cell line withgood growthcharacteristics, animalvaccines

MRC-5 human embryoniclung cells

“normal” cells with afinite life span,vaccine production

NS0 and SP2/0 mouse myelomafrom B-lymphocytes

antibody production

3T3 mouse connectivetissue

suspensible, used inthe development ofthe cell culturetechnology

WI-38 human embryoniclung cells

“normal” cells with afinite life spanvaccine production

Vero long-tailed monkeykidney, 1962

established cell line,but with somecharacteristics of thenormal diploid cells.

Hybridoma cells are “artificial” cells pro-duced by the fusion of human or mammalianlymphocytes, which can secret specific antibod-ies, with a myeloma cell. Usually, lymphocytesdo not survive outside the original organism, i.e.,

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cannot be kept alive in an artificial medium.Mil-stein and Kohler [458] were able to overcomethis problemby fusing amousemyelomcell sim-ilar to a tumor cell with a lymphocyte from amouse spleen, which was able to produce anti-bodies. The cells generated in this way are hy-brid cells (also called “hybridoma cells”) andhave the lymphocyte characteristic to produceantibodies against a certain antigen, as well asthe ability from the myelom cells to survive inculture.

An alternative expression system for recom-binant proteins offer insect cells, especially theinsect-cell baculovirus expression system (IC-BEVS) [476]. Mostly applied are Sf9- or Sf21-cells isolated from Spodoptera frugiperda. Pro-duction of heterologous proteins by the IC-BEVS consists of two stages. Insect cells arefirst grown to a desired concentration and theninfected with a recombinant baculovirus con-taining gene coding for the desired protein.Meanwhile, several recombinant proteins pro-duced by this technique have reached the mar-ket.

The largest and most well known interna-tional animal cell culture collections are theAmerican type culture collection (ATCC) [477],the European collection of animal cell culture(ECACC) [478] as well as the German resourcecentre for biological material (DSMZ) [479].

The application of permanent cell lines forthe production of vaccines faced strong opposi-tion, mainly in the 1960s, because there was thesuspect that these cell lines, which behave sim-ilarly to tumor cells, could infect patients withunknown cancerous substances. Fortunately, noobservation of this kind has been done untiltoday. In the meanwhile, more and more per-manent cell lines have been applied for theproduction of therapeutic and diagnosis sub-stances. However, the application of permanentcell lines, and above all, of recombinant celllines, is strictly controlled by supervisory boards(as the FDA [480] in the USA, and the EMEA[481] within the EU). During the whole produc-tion cycle of a pharmaceutics, not only the prod-uct has to be identical from charge to charge, butalso the phenotypical characteristics of the cellline has to be maintained. To assure that, beforethe beginning of the production, a “master cellbank” has to be created, from which a “master

working cell bank” is derived, providing the in-oculation material for the production cultures.

9.1.4. Growth Medium for Cell Culture

Basically, a growth medium for mammaliancells have to supply all the necessary nutrientsrequired for growth and product formation [482,483]. Furthermore, it shouldhave a certain buffercapacity to stabilize the pH (pH optimum 7.0–7.3) and should provide an appropriate osmo-lality in order to avoid damaging of the sensi-tive cell membrane [484 – 488]. A fundamentalcomponent of all media is a salt solution, whichprovides the ions necessary for life, keeps the os-motic pressurewithin the desired range, containsone or more buffer systems (sodium phosphatebuffer, HEPES, and/or carbon dioxide–carbonicacid buffer) for pH regulation, and contains, insome cases, a pH indicator (phenol red). Further-more the media contain glucose and glutamineas a source of carbon and nitrogen, other ami-no acids, vitamins, mineral salts, and trace ele-ments.

A number of medium formulations havebeen developed, e.g., Eagle’s minimal essen-tialmedium(MEM),Dulbecco’s enriched (mod-ified) Eagle’s medium (DMEM), Ham’s F12,and RPMI 1640, among others. Examples ofmedium compositions can be found in the liter-ature [442, 454, 489]. Traditionally these basicmedia were supplied with approx. 5-10% serum(e.g., foetal calf serum [FCS] or horse serum[HS]) in order to supply specific growth factorsand to protect the cells against shear stress. Thedisadvantage of media containing serum are theindefinite composition of such media, the highserum costs, the difficulty of product purifica-tion, variations between the charges and the riskof virus contamination [490].

Serum can be partially substituted by the ad-dition of transferin, insulin, ethanol amine, albu-min, or eventually fibronectin as adherence fac-tor in a serum-free but still protein-containingmedium [491 – 493]. A further step to chemi-cally defined, protein-free media became possi-ble by replacing these animal-derived proteinsby iron salts or iron complexes, IGF-1, chem-ically defined lipid concentrates, precursors orother stimulating agents such as fatty acids, bi-otin, cholin, glycerin, ethanolamin, thiole, hor-

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mones, and vitamins [494 – 496]. Furthermore,addition of peptone or yeast extract can be help-ful [497, 498].However, the growth rate and pro-ductivity in a serum-free medium can decrease[499]. In addition, the sensitivity to shear stressincreases, requiring additives with protectingcharacteristics, such as Pluronic F68 [500, 501].Immobilization can improve the culture stabilityof nonanchorage-dependent animal cells grownin serum-free media [502, 503]

Different cell lines require different compo-sitions. Adaptation of a cell line to grow with-out serum is quite time-consuming and not allcell lines have been adapted to serum-free orprotein-free media [504, 505]. For cultivationof primary cells, for basic cell-culture researchor for vaccine production still mostly complex,serum-containing media are common. In indus-trial production with established, optimized celllines, serum-free, bovine-serumandprotein-freeas well as chemically defined media are state ofthe art [439].

9.1.5. Small-Scale Culture Systems forRoutine Use

Techniques and culture systems for cultivationof mammalian cells differ significantly fromthose used for microbes. For basic research, alarge number of culture systems have been de-veloped (Fig. 32), mostly designed for use inan incubator aerated with 5% CO2 to main-tain the pH within the desired range. Multiwellplates (6–96 wells), T-flasks (25–100 mL), Petridishes or roller bottles (50 mL to 5 L) are usu-ally used for cell maintenance and proliferation,especially for adherent cells [441, 506 – 509].These systems allow for sterile handling proce-dures and are easy to use, disposable, and low-cost. On the other hand, they require individ-ual handling, for example in medium exchangeand cell seeding; their usefulness is limitedwhenlarge quantities of cells or products are required.This can be overcome to some extent by usingsophisticated robotics [510]. Furthermore, envi-ronmental parameters including pH, dissolvedoxygen, and temperature can hardly be con-trolled within the medium. A further drawback

Figure 32. Routine cultivation systems for use in incubators

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is the limited increase in cell number (approxi-mately 10-20 times during cultivation). Alterna-tively, small well mixed bioreactors (for exam-ple, shakeflasks, stirred vessels, and “super spin-ner” [511]) can be used, where adherent cells aregrown on microcarriers.

Microcarriers are small beads, either solid ormacroporous, having a diameter of approx. 100-300 µm and a density slightly higher than thegrowthmedium.When first developed in the late1960s [512 – 515], microcarrier culture intro-duced new possibilities and suspension cultureof anchorage-dependent cells in high density.Nowadays, beads made of DEAE-Sephadex,DEAE-polyacrylamide, polyacrylamide, poly-styrene, cellulose fibers, hollow glass, gelatin,or gelatin-coated dextran beads are in use [442].In microcarrier culture, cells grow as monolay-ers on the surface of small spheres (Fig. 33)or as multilayers in the pores of macroporousstructures that are usually suspended in culturemedium by gentle stirring. By using microcar-riers in simple suspension culture, fluidized orpacked-bed systems, yields of up to 200× 106

cells per milliliter are possible [516]. Microcar-riers are extensively used for vaccine productionon a larger scale (see below) [517 – 521].

Figure 33. Porcine chondrocytes grown on microcarriers(Cytodex 3, GE Healthcare)

Microencapsulation is another method forthe immobilization of mammalian cells [441].Basically, it can be used to protect cells againsthazardous environmental conditions. The threebasic encapsulation systems existing are thebead, the coated bead, and themembrane-coatedhollow sphere [522 – 524]. Typical size forbeads made of polysine alginate is 300 to 500µm, and the molecular-mass cutoff of these cap-sule membranes is 60 to 70 kDa [442]. Thecapsules can be cultivated in suspension reac-tors similar to microcarriers. Detrimental withrespect to scale-up is the fragile nature of themicrocapsule.

In membrane bioreactors, including smallhollow-fiber reactors [525], the miniPerm sys-tem [526] or the tecnomouse [527], cells arecultivated at tissuelike densities in a compart-ment which contains one or several types ofmembrane for nutrient and oxygen supply andremoval of toxic metabolites. Hollow-fiber sys-tems (compare Section 9.1.6) are widely usedin the production of biopharmaceuticals in-cluding monoclonal antibodies. Several exam-ples of modified membrane bioreactors existfor the three-dimensional culture of tissue cells[528 – 530] including hepatocytes [531 – 533],skin cells [534], or other human cells [535]. Thespecific characteristics of hollow-fiber reactorsare discussed below.

9.1.6. Types of Bioreactors

Design and selection of cell culture bioreactorsfor larger scales have to meet special demandssuch as gentle agitation and aeration withoutcell damage, a well controlled environment withrespect to pH, temp, dissolved oxygen, dis-solved CO2 concentration etc., low levels oftoxic metabolites (ammonia, lactate), high celland product concentrations, optimized mediumutilization, surface for adherent cells, and scal-ability [536 – 545]. Bioreactors for mammaliancell culture can be divided into two categories(Fig. 34): Low-density or homogeneous sys-tems (stirred-tank, bubble-column, air-lift reac-tor), where cells are cultivated either in sus-pension or, if required, on microcarriers; andhigh-density or heterogeneous systems (fixed-bed, fluidized-bed, hollow-fibre reactor), wherecells are immobilized either inmacroporous sup-

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Figure 34. Bioreactor systems for cultivation of mammalian cells

port materials or within a compartment createdby membranes. Selection of a reactor systemsdepends to a large extent on the specific pur-pose such as production of a certain amount ofprotein (small amounts for basic scientific stud-ies up to kilogram quantities for medical appli-cation), three-dimensional tissue cultivation, orsingle-purpose or multipurpose facility.

Design of suspension reactors has to dealmainly with cell damage due to shear stresscaused by agitation and/or aeration [546 – 548].Stirred-tank reactors (Fig. 35), which are themost common type of reactor and are nowa-days build up to 20 000 L scale, are espe-cially suited for suspendable cells [549]. Ad-herent cells can be grown on microcarriers and

by this handled similar to a suspension culture(see Section 9.1.5). For mixing, standard im-pellers (e.g., rushton turbine, pitched or marineimpeller) as well as large paddle impellers areused depending on the shear sensitivity of thecells, sometimes combined to achieve a homoge-nous mixing throughout the bioreactor at low-est possible stirrer speed [441, 549]. For aer-ation, different methods are shown in Figure36 [550 – 552]. Surface aeration can be appliedonly for low cell densities and volumes (below1 L). Bubble aeration may cause cell damagemainly due to foam formation, especially in caseof serum-containing medium. By applying spe-cial aerators (ring sparger,microsparger) in com-bination with serum- or protein-free medium,

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the detrimental effects of bubble aeration canbe overcome [553 – 556]. Alternatively, bubble-freemembrane aeration systemswere developed[557 – 560]. They are characterized by lowshearrates, but require large membrane areas. On lab-oratory scale, a tumbling membrane basket canbe used for mixing [511]. Rotating/vibratingsieves provide a gentle aeration, but are suit-able mostly for laboratory scale [561]. Bubble-columns and air-lift reactors were build up tothousand liter scale, are easy to handle, but re-quire serum-free media to prevent cell dam-age through bubbles and foam [549, 562]. Forcontinuous operation of stirred tank reactorsin perfusion mode (see Section 9.1.7), severaltechniques for cell and/or product retention arein use as shown in Figure 37 (e.g., microfil-tration and ultrafiltration, sedimentation, cen-trifugation, spin filters, acoustic filter, dialysisfor cell, and product enrichment [563 – 577]).Suspension reactors are characterized by ad-vantages such as: conventional reactor systems,know-how on design and sterile operation, goodmass transfer, homogeneous mixing, possibilityof sampling and determination of the concen-tration of the cell, and high scale-up potential.Disadvantages are: difficult oxygen supply athigh cell densities, cell damage by shear and/orfoam (bubble aeration), relatively high demandfor control (temperatur, oxygen, pH, flow rates),cell retention required for h igh cell densities.

Among high-density systems, we find fixed-bed and fluidized-bed reactors aswell as hollow-fiber reactors. In fixed-bed and fluidized-bedbioreactors, cells are immobilizedwithinmacro-porous carriers. The carriers are arranged ina column either packed (fixed-bed reactor[578 – 584]) or floating (fluidized-bed reac-tor [585 – 587]). The column is permanentlyperfused with a conditioned medium from amedium reservoir, mostly in a circulation loop.These types of reactor are very efficient forthe long-term perfusion culture of mammaliancells for the production of biopharmaceuticals(e.g.,monoclonal antibodies, recombinant drugsincluding tPA and EPO). With respect to tis-sue engineering, they have been investigatedfor several applications including the cultiva-tion of “liver” cells as an extracorporal liver de-vice [588 – 590] or proliferation of stem cells[591 – 593]. Furthermore they were success-fully used for cultivation of cells producing

viruslike particles for gene therapy [594 – 597].Again, scale-up is critical for both fixed-bed andfluidized-bed reactors. For fixed-bed reactors, aradial-flow fixed-bed geometry was suggested[598 – 600] and successfully applied up to ap-proximately 25 L fixed-bed volume. At perfu-sion rates of 10-20 L L−1

FB d−1, this would corre-spond to suspension reactors in the thousand literscale. Alternatively, rotating-bed bioreactors arein use [601]. For fluidized-bed bioreactors, sev-eral concepts for scale-up have been suggestedas well [514, 585 – 587, 602]. Compared to sus-pension reactors, fixed-bed and fluidized-bedreactors have the following advantages: highvolume-specific cell density (approx. 5 × 107

cells/mL reactor) and productivity, low shearrates, simplemedium exchange and cell/productseparation, productivity on a high level duringlong-term cultures. Disadvantages are: nonho-mogeneous cell distribution, difficult determi-nation of cells and cell harvest.

Figure 35. Multipurpose bioreactor for continuous perfu-sion culture of mammalian cells (Bioengineering AG, CH-Wald, with permission)

Within hollow-fiber reactors (compare Sec-tion 9.1.5), cells are immobilized in the extra-capillar space of a hollow-fi4ber bundle. Nutri-ents as well as oxygen pass through the fibers,metabolites leave the culture chamber by thisway. Cell concentrations comparable to thosefound in tissues are possible. The use of hollow-

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Figure 36. Aeration systems for stirred-tank reactorsA) Surface aeration; B) Sparging/bubble aeration; C) Rotating or vibrating sieve; D) Bubble-free membrane aeration

Figure 37. Devices for cell or/and product retentionA)Micro- or ultrafiltration, internal or external;B) Spinfilter, internal or external;C) Settler, external or inclined;D) Centrifuge,external; E) Acoustic filter, inclined; F) Dialysis, internal or externala) Feed; b) Cell-free harvest; c) Medium from bioreactor; d) Medium back to bioreactor with enriched cells; e) Dialysing fluidin; f) Dialysing fluid out

fiber culture is a convenient method for makingmoderately large quantities of high-molecular-weight products secreted by human and animalcells, at high concentrations and with a higherratio of product-to-medium-derived impuritiesthan is generally achieved using homogeneous(e.g, stirred-tank) culture [603 – 605]. Advan-tages are: very high cell densities, concentratedproduct, lower serum demands (if required),long-term stability, easy handling, and low cost.Disadvantages are: limited scale-up,mass-trans-fer problems/concentration gradients, difficultdetermination of the cell number, proteolytic ac-tivity on the product.

During the last years, a growing number ofdisposable or single-use bioreactor systems ap-

peared on the market besides hollow-fiber re-actors, addressing especially problems relatedto early process development such as flexibil-ity, cost effectiveness, time-to-market as well asquality and regulatory issues [606 – 608]. Thesesystems are mostly based on a bag technology[609, 610]. The bags with volumes up to 250 Lare placed on tilting or rocking devices for mix-ing and oxygen supply. Advantages of single-use bioreactors are reduced cleaning procedures,lack of validation issues (cleaning, sterilization,etc.), lower investment costs, easier adaptationto changing process demands, less contamina-tion risk [611 – 613].

The main characteristics of the culture sys-tems discussed above are summarized in Table

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18. All these systems support growth of mam-malian cells in one or the other way. On labora-tory scale, mostly disposable flask, membrane,or bag systems are the method of choice. Smallsuspension reactors as well as fixed bed and flu-idized bed reactors are mostly used for researchor process development. For larger amounts ofproducts, only suspension reactors or, up to acertain scale, fixed-bed or fluidized-bed reactorshave the required scalability. For a detailed dis-cussion, compare [440, 555, 614 – 618]. Biore-actor design, in particular, is addressed in [619].

9.1.7. Process Strategies

As for all biotechnological processes, processstrategies for operation of bioreactors can beclassified in discontinuous (batch, repeated-batch or fed-batch) and continuous modes(chemostat, perfusion).Batch and fed-batch pro-cesses are mostly performed in small-scale cul-ture systems (e.g., flasks, bags) or on larger scalein suspension reactors. Batch processes are usu-ally starting with an initial cell density of ap-proximately 1–2 105 cells mL−1 and last 1–2weeks. After harvest, the bioreactor has to becleaned and refurbished before the cycle is re-peated [438, 440, 441]. Cell and product yieldcan be significantly improved by applying a fed-batch strategy, were nutrients are added after de-pletion according to an appropriate feeding strat-egy [[621 – 634]. Batch and fed-batch strategiesare applied quite frequently on industrial scale[438, 440, 536].

The drawbacks of discontinuous modes suchas large reactor volumes, high maintenance(cleaning, sterilization, etc.) can be overcome tosome extend by using continuous mode, espe-cially perfusion with cell and/or product reten-tion [438, 563]. Chemostat cultures without cellretention are a valuable tool for research (e.g.,kinetic studies) [635 – 639] but not for produc-tion scale because of low cell and product con-centration. Continuous perfusion present severaladvantages over batch systems [563, 640 – 643].The advantages include the ability to grow cellsto a very high density, the ease of handling me-dia exchanges for the purpose of fresh feed andproduct harvest, the easy removal of metabo-lites and other inhibitors, and the prospect ofeasy scale-up.When dealingwith adherent cells,

perfusion reactor design becomes slightly sim-pler (e.g., fixed-bed and fluidized-bed) becauseof the immobilization of cells within macrop-orous carriers (compare Section 9.1.6). There isa growing number of reports on stable perfusioncultures even on an industrial scale over periodsof several months [563, 644 – 646]. The mainadvantage of perfusion cultures can be seen inthe reduced bioreactor size (approx. 1/10 of asuspension reactor without cell retention). Nev-ertheless, there are some difficulties in the per-fusion concept [438, 563]. Further equipmentsuch as the retention device itself, pumps forfeeding, harvest, and medium circulation, stor-age tanks for feed and harvest are required. Theamount of media needed to complete amoderateto long-term run can be excessively large. A fur-ther possible drawback of continuous cultivationis the possibility for variability over the time ofthe run. Batch or fed-batch cultivations aremuchshorter in duration and have fewer chances forrandom occurrences to happen. This is impor-tant in dealing with current manufacturing prac-tice (cGMP, see below) conditions that dictateprecise reproducibility or at a minimum the evi-dence of non-effects of minor deviations. In themeantime, there is a growing number of pharma-ceutical products in the market, produced suc-cessfully from perfusion systems. Among theseare block busters such as Kogenate-FS r© (BayerHealth Care) or Remicade r© (Centocor) [563].Previous concerns about getting such processesapproved are not longer an issue and it can be ex-pected that because of an increasing pressure onproduction costs more perfusion processes willbe installed in the future.

A process that becomes more and more im-portant for production of small quantities ofrecombinant proteins is transient transfection[647 – 649]. Usually, nontransfected cells (mostcommonly HEK-293, COS, and BHK cells,compare Section 9.1.2) are first cultivated inbatch mode to a certain cell density and thentransfectedwithDNAencoding for a certain pro-tein [441]. Usually, the cells are genetically notstable and soon lose their expression ability butcan produce a certain amount of protein withinone batch. Originally, it was used just as a pre-liminary test of gene expression. New develop-ments show that large scale production up to 100L is possible [650, 651].

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Table 18. Main characteristics of cell culture systems and bioreactors ([620], modified)a

T-flask/rollerbottles/disposable bags

Membrane reactors(hollow-fibre, miniPerm,etc.)

Suspension (stirred-tank,air-lift reactors)

Fixed-bed/fluidized-bedreactors

Cell density 1 3 2 2–3Homogeneity 1 1 3 2Shear stress N N Y NProduct concentration 1 3 2 2Porductivity 1 3 2 2Medium efficiency 3 1 3 2Continuous process(perfusion)

N Y Yb Y

Control 0 1 2 3Downstream process 1 3 2 2Steam sterilizable N N Y YReusable N N Y Ya 0, not possible; 1-3, increasing efficiency; Y, yes; N: no.b Additional equipment for cell retention required.

9.1.8. Downstream Processes

The desired protein product, mostly extracellu-lar, has to be purified after cell cultivation bya combination of unit processes. From an en-gineering point of view, the challenges are 1)low product concentrations in the cell-free su-pernatant, 2) the complex structures of the pro-teins, especially the recombinant, glycosylatedproteins, resulting in a high sensitivity againsttemperature, shear forces, extreme pH, or prote-olytic activity, 3) contamination by nucleic acid(DNA fragments from dead cells), 4) contami-nation by other proteins (e.g., from serum), 5)potential contamination by viruses or virus par-ticles [652, 653]. The main process steps in-volved are [654]: concentration of supernatant(e.g., precipitation, centrifugation filtration, oraqueous two-phase partition), primary purifica-tion/capture (e.g., chromatographic techniquessuch as ion-exchange, gel, or immunoaffinitychromatography as well as precipitation), fur-ther purification (e.g., ion-exchange, hydropho-bic, and gel chromatography), and finally pol-ishing (gel filtration, diafiltration, ultrafiltration)and techniques for virus inactivation (acid/basetreatment, nanofiltration or ultraviolet-c (UVc))[653, 655, 656]. The early stages within a pu-rification strategy will aim to achieve both con-centration and purification. The next step willbe the major purification step. The number ofsteps included in “further purification and “pol-ishing” will depend on the intended use of theprotein, as they are mainly designed to reducecontaminants such as DNA, host cell protein,

endotoxins, process chemicals, etc. The impor-tance of the downstream process is underlinedby the fact, that an estimated 60–80% of the to-tal production costs are attributed to purification[438, 439, 654].

9.1.9. Regulatory and Safety Issues

Pharmaceutical productionprocesses are subjectto strict regulations and regular inspections toensure the high quality demands for medicines[439, 440, 657]. Guidelines for aspects relevantfor the production process are summarized un-der cGMP and corresponding lists were releasedin the United States by the FDA [480] as well asby the EU [481]. There are some special concernwith the use of mammalian cells for productionof biopharmaceuticals, e.g., 1) risk of contam-ination by microbes, mycoplasma, or viruses,2) the genetic stability of the host cell line, or3) the consistency of the product (glycosyla-tion pattern, etc.). The documents to be submit-ted to the authorities include detailed informa-tion concerning the product, the production pro-cess, product characterization including analyt-ical methods, validation procedures, methods,and results of clinical trials. The efforts to fulfillthese requirements are quite high and bind sig-nificant resources within the companies [439].

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9.2. Tissue Engineering

In this chapter, an overview in the field of “redbiotechnology” is presented. Red biotechnologycomprises three major fields:– Gene therapy– Production of proteins, antibodies, and vac-cines for diagnostics and therapy

– Tissue engineeringGene therapy is a therapeutic approach based

on the correction of a gene defect causing adisease. Different techniques exist for achiev-ing this aim. In most cases, the wild-type geneis inserted into the genome by special methodsreplacing the mutant gene, which leads to thedevelopment of the disease. The most commonvectors for the transport of the correct gene intothe genome are viruses.

In the mid-eighties, several treatments withthe geneticallymodified cellswere reported. Thefirst gene therapy approachwas reported in 1990on a young girl suffering from severe combinedimmunodeficiency (SCID). But the therapy didnot work out, after a few months the procedurehad to be repeated regularly.

A major drawback within this field emergedwhen it was evident that a young patient diedafter being treated by gene therapy [658]. Thepatient was suffering from a rare liver diseaseand was a volunteer in a gene therapy program.Similar incidences occurred in the year of 2000within a group of boys after gene therapeutictreatment of SCID. The majority of the boys inthis group developed leukemia. Since then, genetherapy is only broadly applied within science-fiction movies. Researchers working in this fieldwent back a few steps to elucidate the devastat-ing incidences [659].

Biological active compounds produced fromgenetically modified cell cultures have been inuse for several years in the treatment of a wholerange of diseases. In most cases, the active in-gredients are proteins such as blood clottingfactors (e.g., tissue plasminogen activator, t-PA), growth factors (e.g., erythropoitin, EPO),or monoclonal antibodies [660]. All these sub-stances can be produced in animal cell cultures.These proteins can also be applied in diagnos-tic assays (e.g., ELISA). For more “simple”molecules such as non-glycosylated proteins,peptides and hormones, bacterial or yeast cellscan be utilized for production.

9.2.1. Application of Tissue Engineering

Tissue engineering is a rather young and rapidlygrowing interdisciplinary research field whichcombines the principles of engineering, life, andclinical sciences (Fig. 38). The aim of this re-searchfield is the development of biological sub-stitutes that restore, maintain, or improve tissuefunctions [661, 662]. Therefore, the knowledgeof cellular interactions, molecular transport phe-nomena, interactions of cells with material sur-face, mechanical loading, and biochemical in-terventions is applied.

An official definition was presented at a con-ference of the National Science Foundation in1988: “. . . the application of principles andmethods of engineering and life sciences to-ward fundamental understanding of structure–function relationship in normal and pathologi-cal mammalian tissues and the development ofbiological substitutes to restore, remain, or im-prove tissue function”.

Over the past two decades, encouraging re-sults have been achieved. Methods and tech-niques of tissue engineering have already en-tered therapeutic practice, e.g., for the treat-ment of sports injuries and damage to cartilage.The main approach adopted in these cases isknown as autologous chondrocyte transplanta-tion (ACT). Cells isolated from cartilage tis-sue of the patient (autologous) are seeded ontoa collagen matrix and cultivated until a three-dimensional tissue structure is generated. Thenthe construct can be implanted into the defect[663 – 665]. Also tissue-engineered skin is al-ready used, e.g., for therapy of severely burnedpatients [666 – 669].Researchers are attemptingto engineer almost every human tissue or organbecause the demand of tissue-engineered cellsand organs is still growing because of the fact oforgan shortage for transplantation. The creationof three-dimensional tissue constructswhich canmimic all functions and metabolic activity likeorgans (e.g., liver [670] or pancreas [671]) is stilla crucial challenge. The first tissue-engineeredheart valves have been implanted to a patient andshow promising results in function and mechan-ical stability [672, 673].

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Figure 38. Disciplines involved in tissue engineering

Figure 39. Principle of tissue engineering

9.2.2. Principle of Tissue Engineering

The principle of tissue engineering can bedemonstrated in the following way: First, cellshave to be isolated. The cells are seeded to amatrix and are extracorporal cultivated in a suit-able culture systemor bioreactor inmediumcon-taining nutrients and growth factors. After cul-tivation, the resulting tissue is reimplanted intothe patient (Fig. 39).

Tissue engineering is driven by the hopes ofthe patients of increased availability of trans-plantable organs, to ease their suffering, fore-stall imminent death, and enabling a better qual-ity of life. Alongside these medical objectives,there are also huge economic issues at stakebecause treatment of tissue and organ damageor dysfunction often involves long hospitaliza-tion and thus is very expensive. In contrast toalternative approaches of xenogenic (from an-

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other species) and allogenic (same species butanother individual) organ transplant, tissue en-gineering involves transplanting cells from thebody of the patient itself. This is the reason forwhich such great attention has been focused inthe last few years on this new area of research.By growing artificial autologous tissue and or-gans in vitro, the risk of rejection by the im-mune system of the body can be avoided. Tis-sue engineering techniques can be used to gener-ate replacement tissue for internal organs (liver,pancreas, heart, kidneys, lungs), sensory organs(eyes, ears, nose), the skeleton (bones and car-tilage), the brain and nerves (in particular forthe treatment ofAlzheimer’s disease and Parkin-son’s disease), or the skin (e.g., treatment of se-vere burns).

A closely related concept to tissue engineer-ing is regenerative medicine. Here, the empha-sis is placed on supporting the body’s naturalhealing processes. The potential of stem cells iscrucial for both fields.

9.2.3. Strategies

Tissue engineering can be performed by usingone of the three approaches:

In vitro cultivation of autologous cells on or-ganic, synthetic, or natural matricesIn vitro cultivation of autologous cells onxenogenic matricesIin vitro tissue generation from embryonicstem cells

Depending on the tissue of interest, eithera close system (e.g., extracorporal device, ar-tificial liver) or an open system based on abiodegradable polymer scaffold is developed.The autologous cells can be isolated tissue-specific primary cells or stem cells.

9.2.4. The Essentials

The essentials for tissue engineering are shownin Figure 40. For a successful generation ofthree-dimensional tissues, suitable cells have tobe isolated and cultivated on a matrix in anappropriate medium containing all necessarygrowth and differentiation factors by using anoptimizedbioreactor system.During cultivation,

the application of amechanical load (e.g., strain,compression) is beneficial for the developmentof three-dimensional cell–cell contacts resultingin functional tissue [674 – 682].

Figure 40. The “essentials” for tissue engineering

9.2.5. Cells

For tissue engineering, either tissue-specificcells can be isolated and cultured or stem cellscan be used for the directed differentiation to thedesired tissue. Due to their potential for differen-tiation into several different tissue types, nowa-days the use of stemcells is intensively discussed[683]. In numerous publications adult stem cellsisolated from bone marrow (bone marrow stro-mal cellsBMSC[684 – 686], fat tissue (adipose-derived stem cells adSC [687]), and peripheralblood or umbilical cord blood (both haematopoi-etic cells [688, 689]) are applied. The potentialfor differentiation into several different types oftissues is already approved. Mesenchymal stemcells (MSC) play a major role in modern tissueengineering [690, 691]. These cells are easy toisolate from bone marrow or fat tissue and theiravailability is not so limited compared with thatof the small number of stem cells present in, e.g.,peripheral blood (0.1–0.2%). Treatment con-cept based on the use of autologous stem cellswould eliminate problems of donor site scarcity,immune rejection and pathogen transfer.

9.2.6. Biomatrices

There has been also huge progress in the de-velopment of biomaterials for use in tissue-engineering applications [692, 693]. Research

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has concentrated on the development and pro-duction of biocompatible and biodegradablema-terials [694 – 697]. Such biomaterials take overthe task of the extracellular matrix (ECM),which naturally provides cells with a support-ive framework of structural proteins, carbohy-drates, and signaling molecules. The ideal scaf-fold has to mimic ECM features and would bemade of a biomaterial that provides all the nec-essary signals for the cells to grow, differenti-ate and interact, forming the desired structure.Regardless that general properties and designfeatures of the biomaterials for discussed pur-poses have been published in a numerous re-views [698 – 702], the subject still represents in-tensive scientific and practical interest. Besidesnatural occurring compounds or scaffolds, alsosynthetic biomaterials are reviewed in the liter-ature [703 – 705]. Along with polysaccharide-and protein-based (e.g., hyaluronic acid, colla-gen) materials, also many synthetic polymersare used for tissue-engineering application (e.g.,polylactic acid, polyglycolic acid, polyurethane,polyesters).

9.2.7. Bioreactors for Tissue Engineering

The design, control, and operation of bioreac-tors in technical and engineering disciplines arewell-established. Their use in medical sciencesrequires an interdisciplinary approach aiming atthe development of functional tissues. There-fore, frequently the application of mechanicalloading is needed [706 – 709]. The loading canbe disposed in different ways; for example, alongitudinal straining leads to the formation ofligaments or tendons; compression is appliedwhen aiming at the development of, e.g., car-tilage; pressure (perfusion) can be applied forbone tissue formation and pulsed bioreactorsare used for blood vessel generation. In Figure41, a selection of bioreactor schemes for tissue-engineering applications is presented. Unlikein conventional mammalian cell culture, whereonly a limited number of bioreactor types areused (e.g., T-flaks, roller bottles, spinner sys-tems, stirred-tank reactors), in tissue engineer-ing tailor-made bioreactors have to be devel-oped and constructed for each specific tissuetype, considering in particular the fluid dynam-icswhich are realized by these different systems.

Figure 41. Selected bioreactor types for tissue engineering

Since human cells are anchorage-dependent,they must be attached to a matrix in the bioreac-tor during the cultivation. In a rotating-wall sys-tem, the cell-seeded matrices are cultured whilethe reactor wall is rotated, thus the cells floatthrough the medium and are affected by mi-crogravitylike conditions, and the mass-transferrates are improved. Moreover, cell-seeded ma-trices can be cultured while hanging in spinnerflasks, which results also in an improved masstransfer. Another possibility is the continuouspumping of themedium through a fixed bedwithcells or the cells can be placed into a chamberwhere a mechanical stimulation (here compres-sion) is applied.

9.2.8. Growing New from Old

The idea of organs one day being freely avail-able “off the shelf” is still an aspiration today.The need is great, however, and patients are ofcourse very eager to have “personalized” treat-ment from “organ designers” using tissue engi-neering. The most ambitious concepts involveapplying a sort of “building-block” principle tothe culture of each tissue or organ type: so-called precursor or stem cells would be used,whose potential for differentiation could be ex-ploited through the use of appropriate growth ordifferentiation factors. Thanks to this method,one would “only” need the appropriate “ingre-dients”: Further advantages of this approach arethe fact that an organ or tissue replacement canbe carried out at any time – in other words it can

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be scheduled in advance. Regenerativemedicinehas great potential: according to a report in TimeMagazine, over the next 10 to 20 years, tissue en-gineering will become one of the top careers anda new era in life sciences and medicine dawns.The potential applications are highly diverse butare to be found essentially in the treatment ofcells and tissue which possess little or no capac-ity to repair themselves such as cartilage, heartmuscle and nerve tissue.

The medicine of tomorrow will strive to de-velop personalized therapeutic procedures in-volving the patient in each treatment choice.There will be a move from a general “one-size-fits-all” approach to personalized treatmentbased on the principle of “one drug, one ther-apy, one patient”. This will mean using a sys-tems biology approach to fully restore the origi-nal healthy state of the patient. This dream couldbe realized thanks to the emerging technologiesof regenerativemedicine and tissue engineering.

Groups of cells and tissue generated by usingtissue engineering can also serve as test systemswhich exhibit characteristics specific to a givenorgan. These can be used to test the effective-ness and duration of certain drugs or to studytheir mechanisms and metabolism, thus repre-senting an alternative to animal experiments, anda rapid, broadly applicable and cost-effectiveway of screening pharmaceutically active sub-stances.

Over the last ten years, there has been an ex-plosion of research in tissue engineering, whichshows the enormous importance attached to thistechnology.Within Europe, German researchersare playing amajor role. Leading research is alsobeing carried out by teams in France, Britain,Italy, and the Netherlands. Large numbers ofsmall biotechnology companies are working intissue engineering and more and more are beingset up all the time.

In the 21st century, major scientific questionsconcern modern developments within the fieldof regenerative medicine, which includes tissueengineering, transplantation, prosthesis, as wellas pharmacological interventions stimulating insitu tissue regeneration.

9.3. Biotechnology and Food

It is not presumptuous to claim that modernbiotechnology has developed from traditionalprocesses of food preparation. The production ofalcoholic beverages, the coagulation ofmilk pro-teins, and the preservation of vegetable food byfermentation has already been familiar to mostancient civilizations. Thus, several microorgan-isms have a very long history of safe use in hu-man food production. The main focus of thischapter is not on traditional fermentations buton recent developments in food biotechnology.Since this field is remarkably ambitious andcomplex, only some techniques of high indus-trial relevance are covered exemplarily. Geneticengineering of plants has yielded, for exam-ple, herbicide tolerant “RoundUp Ready” soy,Bacillus thuringiensis toxin expressing maize(Bt maize) and transgenic rice lines with anincreased concentration of β-carotene in theendosperm (“golden rice”). Genetically engi-neered animals may produce milk with an al-tered protein profile, and modification of thehormone expression of fish imparts acceleratedgrowth rates. A number of comprehensive re-views on foodbiotechnology are available [710].

9.3.1. Production of Food Additives by CellCulture Systems

9.3.1.1. Amino Acids

l-Amino acids represent the largest group offood and feed additives produced by means ofbiotechnology [711]. Consequently, the litera-ture published within the last decade is notablyversatile. l-Glutamic acid ((S)-2-aminoglutaricacid, E 620) was the first amino acid pro-duced biotechnologically on an industrial scale,and is still the leading amino acid by volume.More than 800 000 t/a of monosodium gluta-mate,which serves as a flavor enhancer (“umamitaste”), are produced worldwide by cultivationof Corynebacterium glutamicum and related or-ganisms. The production of l-glutamic acidmaybe enhanced by overexpression of the glutamatetransporter gene (yhfK) [712], by use of strep-tomycin resistant Brevibacterium lactofermen-tum strains [713], and by optimization of the

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culture conditions [714]. The second most im-portant amino acid is l-lysine ((S)-2,6-diami-nohexanoic acid), which is mainly used to sup-plement animal feedwith this essential and oftenlimiting amino acid. It is industrially producedbyC. glutamicum or byE. coli species [711]. Ef-ficient industrial producer strains have been de-veloped by multiple rounds of mutagenesis. De-fined l-lysine producingC. glutamicum mutantshave been generated for example by modifica-tion of the enzyme aspartate kinase (lysC muta-tion) [715], attenuation of the transcription reg-ulator tipA gene [716], and the introduction of amutation (S361F) into the 6-phosphogluconatedehydrogenase gene (gnd) [717]. In the lattercase, the modified strain was less sensitive toallosteric inhibition by intracellular metabolitesthan the wild-type strain. Product yields of upto 100 g/L are attainable within 24 h of culti-vation with the recombinant production strains.A third amino acid, l-cysteine ((R)-2-amino-3-mercaptopropanoic acid), which is applied as adough conditioner in the bakery industry,may beobtained from recombinant E. coli [718 – 720].

9.3.1.2. Organic Acids

Citric acid (2-hydroxy-1,2,3-propanetricarbox-ylic acid, E 330) is widely used to lower thepH of food (candy) and beverages (soft drinks,wine). Furthermore, it acts as an efficient chela-tor of prooxidative iron and copper ions. Variousfungi and bacteria may be used for the biotech-nological synthesis of citric acid starting fromnumerous substrates. The process has recentlybeen comprehensively reviewed in [721]. As-pergillus niger grown as surface cultures on di-luted molasses has been employed as early asin the 1920s, and still is the most popular pro-ductionorganism.Today, distillers’ grains [722],palm oil mill effluents [723], or cassava starchfactory wastes [724] may serve as cheap andabundantly available substrates. By optimiza-tion of the growth conditions (sufficient oxygensupply in submerged cultures; media deficient inmanganese, copper, iron, and zinc ions; high glu-cose concentrations), and by selection of over-producing A. niger strains, yields of > 100 g/Lare attainable [725]. The yeast Yarrowia lipoly-tica represents a future potential production or-ganism. The strain Y. lipolytica 187/1 showed

maximum synthesis rates when grown on rawagro-industrial fats as substrates [726], whileRymowicz and Cibis [727] selected the acetate-negative strain Y. lipolytica AWG-7, cultivatedon glucose syrup, for citric acid production. Asa second important organic acid, l-lactic acid((S)-2-hydroxypropanoic acid, E 270), has beenused from ancient times as a food preservativeby lowering the pH during fermentation. Morerecently, lactic acid has gained increasing in-terest since the monomer is used for genera-tion of the biodegradable food packing poly-mer “polylactic acid (PLA)”. However, only fewPLA-generating processes have been commer-cialized and the cost of PLA is still not ableto compete with synthetic plastics [728, 729].Apart from traditional lactic acid bacteria suchas Lactobacillus delbrueckii [730] or L. plan-tarum [731], a wide range of further microor-ganisms, especially strains of Rhizopus oryzae,has been employed for the biosynthesis of l-lactic acid (for example [732, 733]). A currentreview on metabolic engineering approaches oflactic acid bacteria is available [734].

9.3.1.3. Vitamins

l-Ascorbic acid (vitamin C, E 300) is producedby a combination of chemical synthesis and en-zymatic reaction steps. Known as “Reichsteinprocess”, the procedure was established alreadyin the 1930s. The key biotransformation step,the oxidation of d-sorbitol to l-sorbose, is cat-alyzed by Acetobacter suboxidans [735]. Dueto the worldwide use of l-ascorbic acid as vita-min in dietary supplements and as antioxidant infood and feed, the annual need sumsup to 60 000t [711]. Novel approaches use either recom-binant microorganisms in one-step procedures[736] or Azotobacter chroococcum strains [737]for l-ascorbic acid production. For a reviewon biotechnological routes to vitamin C, see[738]. Further water-soluble vitamins, includ-ing riboflavin (vitamin B2) and cyanocobalamin(vitamin B12), are currently produced by mi-crobial cell cultures. The chemical synthesis ofthe yellow-greenish-colored riboflavin has beenlargely replaced by biotechnological processesusing either the overproducing fungus Ashbyagossypii [739] or mutant strains of Bacillus sub-tilis. The “generally recognized as safe” (GRAS)

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organismB. subtilis iswidely used in the food in-dustry [725] and, for example, the mutant strainCJKB0002 produced 26.5 g/L riboflavin com-pared to 22.4 g/L obtained from the parent strain[740]. Detailed mechanistic studies on the ri-boflavin synthesis in Ashbya gossypii [739] andin the lactic acid bacterium Lactococcus lac-tis ssp. cremoris [741] have been published.Cyanocobalamin is derived from Propionibac-terium or Pseudomonas denitrificans species[711]. Recently, a fermented milk product con-taining Propionibacterium freudenreichii ssp.shermanii B369 bacteria, capable of produc-ing cyanocobalamin in situ, was patented [742].Thus, a vitamin B12 enriched yogurt may bemanufactured as a “nutraceutical” without ex-ternal vitamin fortification. However, compara-tively lowworld-wide annual production rates of3000 t for vitamin B2 and 10 t for vitamin B12document the predominant use of theses vita-mins in dietary supplements and in pharmaceu-ticals [711]. The main plant-derived carotenoid,β-carotene (E 160a), serves as provitamin A inthe human diet. Since β-carotene additionallyimparts an intense yellow-orange coloring, itis widely used as a natural food additive. Thedevelopment of biotechnological processes forthe production of β-carotene is mainly drivenby the increasing demand of the food industry.Besides the extraction from plants (for exam-ple from lucerne), β-carotene is industrially ob-tained from the algae Dunaliella salina [743]or from the fungus Blakeslea trispora [744].The carotenoid astaxanthin, which is used ona large scale in aquaculture for the coloring ofsalmonids and crustaceae, is formed by the yeastPhaffia rhodozyma [745].

9.3.1.4. Sweet Compounds

Palatinit (isomalt, E 953) is a non-cariogenicsugar substitute used in low-calory food,desserts, ice creams, and diabetic food. It isa white, crystalline, and non-hygroscopic sub-stance with a sweetening power of roughly0.45–0.60 compared to that of sucrose (1.0). Inthe first production step, sucrose is convertedenzymatically (by action of a glycosyltrans-ferase) into palatinose (isomaltulose, O-α-d-glucopyranosyl-(1–6)-d-fructofuranose) by im-mobilized cells of Protaminobacter rubrum.

Subsequently, palatinose is reduced to palatinit,an equimolar mixture of the corresponding alco-hols (1-O-α-d-glucopyranosyl-d-mannitol and6-O-α-d-glucopyranosyl-d-sorbitol) by hydro-genation on a fixed-bed Ni catalyst. In the in-dustrial process, it is advantageous to use im-mobilized non-viable cells of P. rubrum in apacked-bed reactor for the enzymatic step [746].Apart from P. rubrum, immobilized cells of Ser-ratia plymuthica [747], Erwinia sp. D12 [748],orKlebsiella singaporensisLX3T (isolated fromsoil) [749] have beenused to convert sucrose intopalatinose. In 2005, palatinose was approved ac-cording to the EU regulation (EC 258/97) con-cerning novel foods and novel food ingredients,and the GRAS registration was granted in 2006by theUSFoodandDrugAdministration (FDA).Recent inventions suggest a mixture of palati-nosewith cocoapowder for the preparation of in-stant beverages [750]. Further applications com-prise the use of palatinose for the production oflow-alcohol beer or beerlike soft drinks [751],and the preparation of fermented soybean milk[752].

In the industrial production of the sweet-ener aspartame (α-l-aspartyl-l-phenylalanine1-methyl ester, E 951), the methyl ester of the“DF” dipeptide, both components are producedby biotechnological processes. l-Phenylalanineis usually manufactured by large-scale cultiva-tion of the soil bacteria Serratia marcescens orC. glutamicum. Immobilized aspartase or alter-natively intact cells of E. coli are used to syn-thesize l-aspartic acid starting from the precur-sors ammonia and fumarate [725].Usually, amidformation is performed chemically although anenzymatic approach exists [753]. Thermolysine,immobilized on an agarose–aldehyd gel, is usedas biocatalyst for the synthesis of enantiopureaspartame [754]. Thermolysine is a metallopep-tidase with a zinc ion in its active site and fourcalcium ions, which stabilize the tertiary struc-ture of the enzyme.

9.3.1.5. Sugar Alcohols

Sugar alcohols such as d-sorbitol (d-glucitol; E420) and d-xylitol (E 967) have attracted the at-tention of the food and pharmaceutical industrybecause of their sweetening and non-cariogenicproperties. Due to the fact that their metabolism

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is insulin-independent, they are well suited forsubstituting sugar in diet schemes for diabetics.Since the biotechnological production of xyli-tol represents an economically attractive alter-native to the industrialized chemical process,various process variants have been published.Somewhat less literature is available on sor-bitol, which is mainly formed by cultures of Zy-momonas mobilis [755, 756]. The synthesis ofxylitol by Candida guilliermondii depends onseveral crucial process parameters, one of whichis the concentration of phosphate buffer in theculture broth (optimum at 0.6 M) [757, 758].In another approach, the d-xylitol productionof the yeast Debaryomyces hansenii UFV-170was monitored with respect to temperature andstarting pH [759]. Various substrates, includingfor example hydrolysates of corn cobs [760] andsugarcane bagasse [761] have been used for thebioconversion of the pentose d-xylose to xylitol.

9.3.1.6. Microbial Saccharides

The most popular polymer currently producedby means of biotechnology is xanthan gum (E415). Xanthan gum is a complex bacterial ex-opolysaccharide structurally closely related tocellulose. Today it is mainly produced by mu-tants of Xanthomonas campestris (40 000 t/a[711, 762]), a Gram-negative bacterium thatis pathogenic to many crops. Due to its out-standing properties (high salt and pH toler-ance, thermostability, and unusual rheologicalbehavior), xanthan is predominantly used as athickener in dressings and convenience food[763]. Sago starch has been suggested as analternative carbon source instead of glucose[764]. Trehalose (α-d-glucopyranosyl-(1-1)-α-d-glucopyranose) is a nonreducing disaccharidethat is thought to play a role as storage carbo-hydrate for many microorganisms. It may serveas an additive to improve the freeze stabilityof processed food. In 2000 and 2001, trehalosehas gained the FDA GRAS and the EU novelfood approval, respectively. Trehalose is ob-tainable inter alia from Brevibacterium [765],Cellulosimicrobium cellulans [766], or Debary-omyces hansenii strains. The latter twowere cul-tivated under moderate saline stress for extra-cellular trehalose production [767]. Literature[768] reviews trehalose production (with a fo-

cus on the conversion of starch to trehalose) andpotential applications in the food industry.

9.3.1.7. Conjugated Linoleic Acids (CLA)

Lactic acid bacteria play a prominent role in thetraditional fermentation of food. One of the ex-ceptional features of some lactobacilli is theirability to isomerize linoleic acid to differentconjugated linoleic acid isomers (CLA). TheZ9,E11- and E9,E11-octadecadienoic acid areassumed to possess beneficial physiological andanticarcinogenic properties [735].Lactobacillusplantarum AKU 1009a formed up to 40 mg/mLCLA (33% molar yield) in 108 h under opti-mized conditions from 12% (w/v) linoleic acidas substrate [769]. Further lactobacilli capable ofCLA production encompass for example L. del-brueckii ssp. bulgaricus [770] and L. plantarumJCM 1551 [771]. Castor oil proved to be an ade-quate substrate for CLA formation. Presumably,a two-step reaction mechanism, involving hy-dration of a double bond with subsequent elimi-nation of water, is leading to different CLA iso-mers [772].

9.3.1.8. Lactulose

Lactulose acts as a prebiotic compound and thusmay contribute to increased levels of “health-promoting” bacteria (bifidobacteria and/or lac-tobacilli) in the human intestinal tract [773].The term “prebiotics” comprises a wide vari-ety of oligosaccharides that are not digested inthe stomach and in the small intestine. Theynaturally occur in garlic, asparagus, onion, andothers. Apart from soybean oligosaccharides, anumber of uncommon fructo-, galacto-, xylo-,and isomalto-oligomers are currently in use, es-pecially in Japan [773]. The disaccharide lac-tulose (4-O-β-d-galactopyranosyl-d-fructose) isa non-naturally occurring compound with po-tential applications in infant milk formulationsand foods [774]. Although lactulose is chieflymanufactured chemically using boric acid oraluminates as catalysts, a biotechnological routestarting from lactose (as galactose donor) andfructosewith permeabilizedKluyveromyces lac-tis yeast cells has been developed [775]. In

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another approach, β-galactosidase from As-pergillus oryzae and a hyperthermostable β-glycosidase fromPyrococcus furiosuswere usedfor the transgalactosylation of lactose in thepresence of fructose [776]. Important food ad-ditives and food supplements produced by cellculture systems are summarized in Table 19.

9.3.2. Enzyme-Catalyzed Processes

The modification of starch and lipids as wellas the production of cheese and fruit juices areprime examples of enzyme-catalyzed biopro-cesses in the food industry. For economic rea-sons, the necessary enzymes are often employedas crude products or even as mixtures of en-zymes with different properties. If required, theenzymes are purified by means of precipitation,ultrafiltration and/or fast protein liquid chro-matography (FPLC). Common FPLC protocolscomprise one ore more of the steps hydropho-bic interaction chromatography (HIC), ion ex-change chromatography (IEX), and size exclu-sion chromatography (SEC). Reusability andsometimes also stabilization may be achievedby immobilization of the biocatalyst on an inertcarrier material.

9.3.2.1. Starch-Modifying Enzymes

Traditionally, the degradation of starch to mal-todextrins and glucose/fructose syrups was per-formed by acid-catalyzed hydrolysis, usingstrong mineral acids such as hydrochloric orsulfuric acid. The harsh reaction conditions af-forded a variable spectrum of unwanted by-and breakdown products. Therefore, the acidhydrolysis has been replaced more and more byenzymatic processes. Today, starch-convertingenzymes comprise about 30% of the enzymemarket worldwide. Amongst the starch con-verting enzymes, α-amylase (EC 3.2.1.1, 1,4-α-d-glucan glucanohydrolase) holds the largestmarket share with major applications in thestarch and bakery industries [777, 778]. α-Amylases may be derived from several plantmaterials, bacteria, yeasts, and fungi [779].For commercial applications, mainly strains ofBacillus amyloliquefaciens andBacillus licheni-formis are employed as enzyme producers. α-Amylase attacks the α-(1-4)-links within the

starch molecule (endoamylase), while the α-(1-4)-bonds near α-(1-6)-branches are resistantto hydrolysis. Thus, the end products of ex-tensive α-amylase treatment are oligosaccha-rides of varying length with α-configurationand α-limit dextrins, which constitute branchedoligosaccharides. Sinceα-amylase ofB. licheni-formis is remarkably thermostable (it remainsactive for several hours at temperatures of > 90◦C), its application in the industrial starch hydro-lysis is highly efficient [780].β-Amylases (EC 3.2.1.2; 1,4-α-d-glucan

maltohydrolase) are present mainly in higherplants, but also in some microorganisms. Com-mercial products are available from malt, soy,and wheat. They hydrolyze α-(1-4)-glucosidicbonds, effecting successive removal of maltoseunits from the nonreducing end (exoamylase).In contrast to amylose, amylopectin is not com-pletely hydrolyzed, as all reactions stop beforebranch points are reached (β-limit dextrin).

Glucoamylase (EC 3.2.1.3; 1,4-α-d-glucanglucohydrolase) is formed predominantly byfungi and yeasts (Aspergillus). It starts at thenonreducing end of α-(1-4)-d-glucans and suc-cessively liberates β-d-glucose units. The α-(1-6)-bonds in amylopectin are also cleaved, butaround 30 times slower than the α-(1-4)-bonds.At higher concentrations, glucoamylase reformsall the glycosidic bonds that it hydrolyses. Thus,it condenses glucose, for example, to isomaltose(6-O-α-d-glucopyranosyl-d-glucose).

Debranching enzymes exclusively hydrolyzeα-(1-6)-bonds in amylopectin, glycogen, andpullulan. Linear amylose fragments are formedfrom amylopectin. Pullulanase (EC 3.2.1.41;pullulan α-(1-6)-glucanohydrolase) hydrolyzesα-(1-6)-bonds in pullulan (a linear polymer ofα-(1-6)-linkedmaltotriose units) and amylopectin,while isoamylase (EC 3.2.1.68) is only capableof the hydrolysis of the α-(1-6)-bonds in amy-lopectin. Pullulanases have been found in a va-riety of bacteria; industrially used is an enzymederived from Bacillus acidopullulyticus.

Cyclodextrins are produced via an intramo-lecular transglycosylation reaction in whichthe enzyme cyclodextrin glycosyltransferase(EC 2.4.1.19; 1,4-α-d-glucan 4-α-d-(1,4-α-d-glucano)-transferase) cleaves the α-(1-4)-glycosidic bond and concomitantly links the re-ducing to the nonreducing end.

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Table 19. Examples of food additives and food supplements produced by cell cultures

Product Use E number Production organisml-Glutamic acid flavour enhancer E 620 Corynebacterium glutamicuml-Lysine feed supplement – C. glutamicuml-Cysteine bread enhancer E 920 Escherichia coliCitric acid acidity regulator E 330 Aspergillus nigerl-Lactic acid preservative E 270 Lactobacillus ssp.l-Ascorbic acid vitamin C; antioxidant E 300 Acetobacter suboxidansRiboflavin food coloring;vitamin B2 E 101 Bacillus subtilisCyanocobalamin vitamin B12 – Propionibacterium ssp.β-Carotene food coloring; provitamin A E 160a Dunaliella salina, Blakeslea trisporaPalatinit (Isomalt) sugar substitute E 953 Protaminobacter rubrumAspartame sweetener E 951 Serratia marcescens, C. glutamicumd-Xylitol sugar substitute E 967 Candida guilliermondiiXanthan thickener; stabilizer E 415 Xanthomonas campestrisTrehalose cryoprotectant; stabilizer – Brevibacterium ssp.Conjugated linoleic acids (CLA) active ingredient of functional food – Lactobacillus ssp.Lactulose prebiotic – Kluyveromyces lactis

Syrups high in fructose level (42–44%, high-fructose corn syrups, HFCS) are obtained fromglucose solutions by action of the enzyme xy-lose (glucose) isomerase (EC 5.3.1.5, d-xyloseketol isomerase) [725]. Technical enzymes arederived for example from Streptomyces, Actino-planes, Arthrobacter, or Bacillus cultures. Thelarge-scale isomerization of glucose is per-formed with immobilized enzymes, and productyields of up to 22 tons of isosugars per kilogramof catalyst are obtained. Fructose concentrationsof about 90% are accessible via a subsequentchromatographic purification step. An excellentsurvey of enzymes involved in the industrial pro-cessing of starch is presented in [781]; and themost important starch converting enzymes aresummarized in Table 20.

9.3.2.2. Lipases

Lipases (EC 3.1.1.3, triacylglycerol acyl-hydrolase) are valued biocatalysts with numer-ous applications in food biotechnology. Manyrepresentatives of this enzyme class are highlystable in organic solvents, show broad substratespecificity, and high regio- and/or stereoselec-tivity [782]. Main fields of application includethe rational design of lipids with specific struc-tures (“functional oils and fats” [783]), andthe production of volatile fatty acids and fla-vor esters (“natural food flavorings”). Volatilefatty acids are the so-called “character impact”flavor compounds of various types of cheese,while numerous flavor esters are responsible forfruity aroma impressions [784]. Industrial high-level production of lipases requires not only

the efficient overexpression of the correspond-ing genes, but also a detailed understanding ofthe mechanisms governing the folding and se-cretion of the proteins [785]. Immobilized li-pases from the yeast Candida rugosa are tra-ditionally popular, as a wide core in the cat-alytic centre allows for acceptance of variousfatty acids for trans-esterification or synthesis ofmono- and diacylglycerides, for example, [786].Especially C. antarctica lipase B features out-standing properties such as high thermostabil-ity, sn-2 recognition in hydrolysis of triacylglyc-erides, and selectivity towards (E)-fatty acids. Areview on applications of the lipases A and BfromCandida antarcticawas published recently[787]. Several strains of another member of theCandida family, Yarrowia (Candida) lipolytica,were screened for lipase secretion. The high-est lipase activity produced on rapeseed oil assole carbon and energy source was found to be2760 U/mL for Y. lipolytica 704 [726]. Whencultured in a 2000 L bioreactor, the lipase activ-ity reached 1100 U/mL after 53 h [788]. Lipaseswith novel catalytic properties are produced forexample by the basidiomycetous fungusPleuro-tus sapidus. An extracellular enzyme of the typeB carboxylesterase/lipase family efficiently hy-drolysed xanthophyll esters [789].

9.3.2.3. Pectin-Degrading Enzymes

Pectin is a complex polysaccharide (α-d-(1-4)-polygalacturonic acid, esterified with methanolin varying degree) that represents an essential

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Table 20. Starch-converting enzymes

Enzyme Enzyme number Producer organism Specificityα-Amylase EC 3.2.1.1 Bacillus licheniformis endo-α-(1-4)β-Amylase EC 3.2.1.2 malt, soy, wheat exo-α-(1-4)Glucoamylase EC 3.2.1.3 Aspergillus ssp. exo-α-(1-4); exo-α-(1-6)Pullulanase EC 3.2.1.41 Bacillus acidopullulyticus endo-α-(1-6)Cyclodextrin glycosyltransferase EC 2.4.1.19 Bacillus macerans intramolecular transglycosylationXylose isomerase EC 5.3.1.5 Streptomyces ssp. isomerization of glucose to fructose

part of plant cell walls. The group of pecti-nolytic enzymes comprises pectinases (poly-galacturonases), lyases, and pectin esterases. Atotal market share of 25% of the global salesof food enzymes is currently attained by this en-zyme family [790]. To improve the yields in fruitand vegetable juice production, and to obtainclear and concentrated juices, especially pecti-nases from the mold Aspergillus niger are em-ployed [791, 792]. For example, the efficiencyof kiwi juice production was increased > 10%by addition of 40 mg/L pectinase for 150 min[793], and the yield of loquat juice was 16%higher when 160 mg/L pectinase were addedfor 4 h [794]. Under these conditions, the lossof vitamin C was low and the juice was clar-ified. Endo- and exopectinases (EC 3.2.1.15,endopolygalacturonase; EC 3.2.1.67, exopoly-galacturonase) depolymerize the pectin chain byhydrolytic cleavage of the glycosidic α-(1-4)-bonds.

Usually, pectinases are combined with hemi-cellulases (mixture of cell-wall-degrading en-zymes) and cellulases (EC 3.2.1.4) to improvefruit liquefication, leading to high-quality juicesin terms of oxidation, aroma levels, and sta-bility [795]. As an auxiliary enzyme, α-l-arabinofuranosidase (EC 3.2.1.55), was sug-gested for applications in the wine industry andfor clarification of fruit juices [796]. Pectin lyase(EC 4.2.2.10) and pectate lyase (EC 4.2.2.2)catalyse the eliminative cleavage of pectin andpectate, respectively, to oligosaccharides with4-deoxy-α-d-gluc-4-enuronosyl groups at theirnonreducing ends. Potential production organ-isms include Erwinia species as well as a num-ber of phytopathogenic fungi. Pectin esterases(EC 3.1.1.11) catalyze the hydrolytic cleavageof the methyl ester group. These enzymes gen-erally exhibit a high specificity for pectin sub-strates.

9.3.2.4. Chymosin (Aspartic Protease)

Chymosin (EC 3.4.23.4) is a key enzyme withpeptidolytic activity used in the production ofcheese. The casein fraction of milk protein iscoagulated by hydrolysis of κ-casein betweenPhe105 und Met106 to form para-κ-casein anda glycopeptide (κ-casein glycomacropeptide).From a mechanistic point of view, chymosin be-longs to the class of aspartate peptidases, whichare characterized by the presence of two asparticacid residues in the catalytic center.

Since recombinant chymosin does not exhibitunwanted side activities and is not contaminatedwith microorganisms possibly causing qualityproblems, it replaces rennin from the stomachsof calves (abomasus) to an increasing degree.The gene encoding chymosin has been clonedinto different microorganisms, and the proper-ties of the recombinant enzymes expressed in theyeasts Kluyveromyces lactis and Pichia pastorishave been compared [797]. Since K. lactis is re-garded as a safe organism traditionally used infood processing, it is of outstanding importancein the biotechnological production of chymosinon an industrial scale [798]. The technologicalcharacteristics of various enzymes from geneti-cally modified organisms were nearly identicalto those of natural rennin, and only slight dif-ferences in the coagulation time and curd firm-ness were observed [799]. Chymosin obtainedfrom transgenic sheepmilk and the recombinantprotein expressed by K. lactis differed only intheir action on low-molecular-weight substrates,but not in enzymatic activity on protein sub-strates [800]. Recently, extracellular acid pep-tidases from Rhizopus oryzae have been sug-gested as rennin substitutes in cheese manufac-turing [801, 802].

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9.4. Biotechnology and Health

9.4.1. Individualized Medicine

Single nucleotide polymorphisms (SNPs, a sin-gle base is exchanged by an alternate) are themost common form of genetic variant in the hu-man genome, occurring on average 1 per 1000base pairs. Since the influence of SNPs on med-ical therapy or diet is better understood, the SNPdiagnostic is coming in the focus of interest. Oneimportant goal of genome research is studyinggenetic variance among individuals to improvethe knowledge and treatment of disease and toidentify the variations in our genes that influencethe risk of an individual of becoming ill. Be-cause inherited differences influence most com-mon diseases and many drug responses, the un-derstanding of existing genetic variations in thehuman population is a promising approach in thedevelopment of tailor-made therapeutics. Eightypercent of the individual variations are SNPs,which have a direct impact of gene regulation orof a protein product of a gene. In cases whereone SNP is sufficient to cause disease it is possi-ble to analyze the causal change and to improveour understanding of disease [803 – 806]. Thehope is that a complete human SNP map willincrease the efficiency of drug development andmedical treatment of disease. Many genotypingplatforms have been developed to detect manySNPs at once in a efficient, cost-effective man-ner including microarrays by the use of allel-specific oligonucleotides containing the variablebase. Large-scale SNP genotyping should leadto a fully understanding of the genetic architec-ture of common traits underlying disease, drugor diet response. For example, microarray ex-periments could help to define the ideal dose ofa drug and quantify the impact of this dose onhuman health. Since these are the most person-ally data we know, the access to these data mustbe protected so it is not misused by employersor insurers. The goal of this analysis is to relateSNP variation to disease or diet.

9.4.2. Clinical Diagnosis as Indicated inGenetic Anomalies in Cancer

Solid tumors often show alterations in thegenome that lead to changes in DNA sequences

and copy number. These changes can be de-tected and mapped by using comparative ge-nomic hybridization (CGH). CGH is a molec-ular cytogenetic method of screening for ge-netic alterations. These changes are classified asDNA gains or losses and show a characteristicpattern that includes mutations at chromosomallevel. In the past few years, microarray-basedformats for CGH have been developed madefrom large genomic clones and cDNAs. Thesearrays provide many advantages over conven-tional CGH, including higher resolution, directmapping of changes to the genome sequence,and high throughput. Together with the analysisof complex differences in gene expression bet-ween normal and cancer cells, these data provideinsight into malignancy and reveal genes thatmay be useful as diagnostic or prognostic mark-ers. One problem performing cancer diagnosisusing gene expression profiles from microarrayanalysis considers the heterogeneity of a tumor.Samples taken from different locations in thetumors are dramatically different. Therefore, bi-ological classification on the basis of randomsample analysis leads to poor reproducibility andmay not be adequate [807 – 809].

9.4.3. Pharmaceutical Development

Drug biotransformation in man and animalscan lead to a modulation of pharmacodynamiceffects, drug elimination, but also drug tox-icity. Knowledge of drug biotransformationpathways at early stages of drug developmentcould considerably speed up drug development[810 – 813] and increase the safety of drug de-velopment in the pharmaceutical industry. Eachyear,more than 668 000 animals are used in toxi-cological drug research in Europe. Animal mod-els suffer from serious shortcomings regardingthe prediction for a human situation as signif-icant species differences in enzyme expressionexist betweenman and animals. Specifically, en-zyme induction experiments are frequently mis-leading. 10–20% of all new drugs are requiredto return into preclinical development trials afterfirst clinical tests because of unforeseenmetabo-lite patterns, whose toxicological relevance isunclear or untested.

Classical animal-testing strategies are time-consuming and therefore rather expensive in

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Figure 42. Human liver cells

view of a delayed market penetration and re-turn of investment. The relevance of a strict con-trol of development time is immediately expli-cable by the fact that preclinical development ofa novel drug can total up to several hundred mil-lion Euros. In spite of this, modern cell-culturetechnology has progressed to the extent that it isnow possible to culture primary human cells un-der conditions that maintain differentiated func-tions to perform drug biotransformation and en-zyme induction studies with great reliability inindividual laboratories. Human liver cells are atpresent the preferred target for drug studies asthe use of such cells avoids species extrapolationproblems (Figs. 42 and 43).

Another significant breakthrough in pharma-ceutical drugmetabolism researchwas that thesemodels could be sufficiently automated to enterinto the field of accelerated screening as a sig-nificant initiation step towards high-throughputscreening. Industrial strategies are aiming at in-tegrating pharmacokinetics and toxicology intothe early phases of drug compound selection tosave time, cost, and effort in drug development.Therefore, the cell cultures and the chip tech-

nology were brought together by the pharma-ceutical industry for preclinical drug research.Induction phenomena could be correlated withmicroarray chips containing all known genes in-volved in drug biotransformation. This informa-tion is used to describe the culture models usedin this study and to develop the tools to finger-print interactions of inducing agents with drugs.The use of microarray technology in the field ofdrug discovery and development provide foursignificant advantages:

Figure 43. Bioreactor systems for in vitro testing

122 Biotechnology

– Gene expression in response to drug treat-ments

– Gene expression in model systems– Gene expression patterns in disease– Gene expression patters in pathogens (host–pathogen interaction)

9.4.4. Define Molecular Mechanisms ofToxicity

DNA microarrays can be used to determine therelative safety of natural or synthetic chemicalsto which humans are exposed. Lots of diseasesare influenced by environmental factors, for ex-ample, chemicals, radiation, drugs, nutrition,viruses, carcinogens, reproductive toxins, neu-rotoxins, and immunotoxins [814 – 819]. Al-though most of these chemicals are harmless,it is important to identify potentially hazardoussubstances. In the past, toxicological assays haveused rodent, required high doses, were expan-sive, and take years to complete. Unfortunately,the information gained from established life an-imal models such as rodent to investigate en-zyme induction and toxicity cannot really lead tocorrect assumptions of hazards to humans withrespect to species extrapolation. The shortcom-ing with respect to predictivity of animal modelsfor human drug biotransformation and toxicityis based upon a well accepted species differencein enzyme expression betweenman and animals.Nevertheless, at present animal models in drugmetabolism and toxicology are still legally re-quired in all European countries for a varietyof reasons and legislative bodies are still hes-itative to recommend specific human liver cellbased tests. In spite of this, modern cell-culturetechnology has progressed to the extent that itis now possible to culture primary human livercells under conditions that maintain differenti-ated functions to perform drug biotransforma-tion and enzyme induction studies with greatreliability in individual laboratories. These de-velopments have not yet found entry into legalrecommendations as no prevalidation of suchmodels on a European scale has been achievedso far. The widespread use of such alternativemodels to animal investigations is being ham-pered both by insufficient prevalidation and byfurther improvements still required in the fieldof automation. It would be a significant break-

through in toxicology research if these modelscould be sufficiently automated to enter into thefield of accelerated screening as a significant ini-tiation step towards high-throughput screening.Microarrays are powerful tools for investigatingthe mechanism of toxicity (Fig. 44). A microar-ray chip for cytochrome P450s and Phase II en-zymes can be used to evaluate the liver culturemodels [820].

9.4.5. Detection of Genetically ModifiedOrganisms

The use of genetically modified organisms(GMOs) in food industry has been rising ex-ponentially over the past years. In the space oftime from 1996 to 2003, global area of trans-genic crops increased 40-fold, from 1.7× 106

hectares in 1996 to 67.7× 106 hectares in 2003,with a dominant trait of herbicide tolerance, fol-lowed by insect resistance (www.isaaa.org, In-ternational Service for the Acquisition of Agri-biotech Applications). A GMO is “an organism,with the exception of humanbeings, inwhich thegenetic material has been altered in a way thatdoes not occur naturally by mating and/or natu-ral recombination” [821].Microarrays are a use-ful tool for GMO detection and the applicationof array techniques to GMO detection impliesthe capability of sensitivity and standardizationof the assay. Today, commercialized arrays of-ten do not fulfill these requirements, in spite ofa few promising attempts initialized by biotech-nical companies and research projects. Never-theless, microarrays are already being used inGMO analysis but well established quantitativereal time PCR techniques are state of the art.GMOdetection becomes increasingly importantbecause by-products of these organisms couldend up in the food chain and affect human health.

10. Concluding Remarks

As it has been demonstrated in the previouschapters, biotechnology offers manifold possi-bilities in the production of different productsand in medical applications. It can be used forbetter food, better health, and a cleaner environ-ment. However, in the area of production of fineand bulk chemicals, biotechnology has still to

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Figure 44. Investigation of toxicity through a combined cell-culture and microarray analysis

compete with the chemical industry. The chem-ical industry owes its success to the principle ofunit construction: from simple basic substances,like ethylene, carbon monoxide or hydrogen,more complex precursors can be produced un-der controlled conditions by chemical reactions;because of the variety of combination options,the latter can in turn be converted into inconceiv-able quantities of derivatives and end products.Chemistry learned how to produce chemicallypure basic substances from oil that are simpleto handle and exactly defined; this is performedhighly efficiently in refineries. This was the keyto its success. Without exact knowledge of thisfunctional principle, the triumphant success ofplastics would have been just as impossible asthe production of thousands of other chemicalproducts that todaymake our lives safe and com-fortable. This principle can also be applied to theso-called ‘biorefinery’ which has similar poten-tial and could thus provide a further argument forthe concept of material application of renewableresources and biotechnology. However, a tech-nically viable separation procedure that wouldpermit the separate use or further processingof all the primary products (such as cellulose,hemicellulose, and lignin) is still in its infancy.The main focus is on glucose, which can be de-rived by microbial or chemical methods fromcellulose, since a wide range of biotechnologi-cal and chemical products can be obtained fromglucose. The potential for the microbial conver-sion of glucose-based substances is huge and thereactions are advantageous from the viewpointof energy. It is necessary to link the degrada-tion processes to bulk chemicals via glucose asclosely as possible to the synthesis processes offurther derivatives.

Microorganisms still have a great poten-tial for inducing new or novel enzyme sys-tems capable of converting foreign substrates.It is possible to obtain and cultivate microor-ganisms that can survive or grow in extraor-dinary environments, e.g., psychrophilic, ther-mophilic, acidophilic, and alkaliphilic microor-ganisms. These microorganisms are capable ofproducing unique enzymes stable to heat, alkali,and acid. In addition, techniques for cultivationand enzyme purification, and gene and enzymetechnology have made great advances and arestill under development. Certainly biotechno-logical process development must be foundedon a broader genomic basis. Ten years ago, onlya handful of microorganisms with a completelysequenced genome existed, today there are sev-eral hundreds. Hitherto priority was given to se-quencing pathogenic microorganisms, but it isnow high time to sequence microorganisms thatare relevant to industry. Taking all of these intoconsideration, there must be far more ways thanone can imagine in using biotechnology for ourdaily life.

11. Acknowledgement

The current update was coordinated by RolandUlber.

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