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Synthetic Biology Molecular Mechanisms Concepts Applications 09.05.07

Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

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Page 1: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Synthetic Biology

Molecular Mechanisms

Concepts

Applications

09.05.07

Page 2: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

The central dogma - genetic machinery

http://old.mb.au.dk/graphics/dogma.jpg

Page 3: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Transcription (in E. Coli)

DNA ➞ RNA

Page 4: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Translation

RNA ➞ Protein

Page 5: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

(Alberts, MB of the cell)

Transcriptional regulation

Page 6: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

The lac operon

Jacob Monod

Nobel prize (Physiology/Medicine) 1965

Page 7: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965
Page 8: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965
Page 9: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Nomenclature

promotor: DNA region participating in binding of RNA polymerase to start transcription of a gene

operator: DNA region where a repressor can bind and inhibit binding of RNAp to the neighboring promotor

regulatory protein: protein controlling the transcription of another gene

transcription factor: regulatory protein binding to promotor sequence or other regulatory site and initiating transcription by RNAp

repressor: protein binding to an operator sequence and inhibiting binding of RNAp

Page 10: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

inducer: low molecular weight molecule which induces/reducestranscription of a gene by binding to a regulatory protein

enhancer: cis-active sequence enhancing the activity of someeucaryotic promotors; can be far from promotor, upstream, downstream or even in the transcribed gene

activator proteins: proteins (transcription factors) bound toenhancer sequences stimulating transcription of genes

operon: in bacteria, unit of gene expression containing several genesand accompanying regulatory sequences

cis-regulatory protein: protein which only interacts with the DNA region it was synthesized from

trans-acting element: effect of element insensitive to its position

Page 11: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Biochemical "circuits"

Biophys Journal Volume 67 August 1994 560-578

Computational Functions in Biochemical Reaction Networks

Adam Arkin** and John Ross**Departent of Chenistry and 1Departrent of Neurobiooy, Sdhod of Medicine, Stanford University, Stanford, CA 94305 USA

ABSTRACT In pnor work we demonstrated Fte implementation of logic gates, sequential computers (universal Turing ma-

chines), and parallel computers by means of the kinetics of chemical reaction mechanisms. In the present arficle we developthis subject further by first investigating the computational properties of several enzymatic (single and multiple) reaction mecha-nisms: we show their steady states are analogous to either Boolean or fuzzy logic gates. Nearly perfect digital function is obtainedonly in the regime in which the enzymes are saturated with their substrates. With these enzymatic gates, we constru com-

binational chemical networks that execute a given trut-table. The dynamic range of a network's output is strongly affected by"input/output matching conditions among the intemal gate elements. We find a simple mechanism, similarto the interconversionof fructose-ctposphate between its two bisphWphate forns (fructose-1,bisphophate and fructose-2,6bposphate), thatfunctons analogously to an AND gate. When the simple model is supplanted with one in which the enzyme rate laws are derivedfrom experimental data, the steady state of the mechanism furnions as an asymmetric fuzzy aggregation operator with prop-

erties akin to a fuzzy AND gate. The qualitative behavior of the mechanism does not change when sitated within a large modelof glycolysis/gluconeogenesis and the TCA cycle. The mehanism, in this case, switches the pathway's mode from glycolysisto gluconeogenesis in response to chemical signals of low blood glucose (cAMP) and abundant fuel for the TCA cycle (acetylcoenzyme A).

INTRODUCTION

Biochemical reaction networks (BRNs), such as glycolysisand the tricarboxylic acid cycle, are an integral part of the

machinery by which an organism maintains itself and adaptsto its environment. These networks are responsible for nu-

merous cellular tasks including the maintenance of ho-meostasis and the creation and propagation of chemical sig-nals such as those indicating hunger or satiation. It is oftenvery difficult to determine the underlying logic of the regu-

lation of even relatively small portions of a BRN. First, thesub-network may be highly interconnected and contain manyfeedback loops, branching pathways, etc. Second, it is dif-ficult to determine all the kinetic parameters that determinethe behavior of a BRN in vitro let alone in vivo (Fersht,1985). Third, the great range of temporal and spatial scalesover which a large BRN can react to the perturbation of itsvariables makes it difficult to deduce the laws of biologicalcontrol and signal processing from examination of models ofthe dynamic equations of motion (Acerenza, Sauro, andKacser, 1989). Therefore, it is desirable to develop additionaltechniques for the investigation of reaction mechanisms,their control and signal processing.

In previous papers we have demonstrated the implemen-tation of formal logical computations and functions such as

logic gates, neural networks, and universal Turing machines(Hjehmfelt and Ross, 1992, 1993, 1994; Hjelmfelt et al.,1991, 1992, 1993) by means of macroscopic kinetics ofchemical reaction networks. (This work is briefly reviewed

Received for publication 16 March 1994 and in final form 18 May 1994.

Address reprint requests to Dr. John Ross, Deprtment of Chemistry, Stan-ford University School of Medicine, Stanford, CA 94305. TeL: 415-723-9203; Fax: 415-723-4817; E-mail: ross@cLh istanford.edu.

1994 by the Biophysical Society

0006-3495/94/0W560/19 $2.00

under Implementation of Computation with MacroscopicChemical Kinetics.) The simplest chemical kinetic mecha-nism capable of computation discussed in these networksbears a striking resemblance to parts of many importantmulti-enzymatic pathways found in metabolism. It is natural,therefore, to look for logical computation performed by thesestructures within the known BRNs.

Abbreviations used: GCP, glucose carrier protein; G Dg, glucose degrada-ton; GK, ghicokinase; HK, hexokinase; G6Pase, glucose-6-phosphatase;PHI, phosphohexose isomerase; PFK1, phosphofiuctokinase-1; F16BPase,frcose-1,6-bisphosphatase; PFK2, phosphofructokinase-2, F26BPase,fructose-2,6-bisphosphatase; a-OP DH, a-glycerol phosphate dehydroge-nase; a-OP Dg, a-glycerol phosphate degradation; TPI, tiose phosphateisomerase; GAPDH, glyceraldehyde phosphate dehydrogenase; PGK, phos-phoglycerate kinase; PGM, phosphoglycerate mutase; PyrK, pyruvate ki-nase; PyrC, pyruvate carboxylase; PEPCK phospho enolpyruvate car-boxykinase; LacDH, lactose dehydrogenase; Lac Dg, lactose degradation;CarbA, carbonic anhydrase; Citl Dg, cytosolic citrate degradation;PyrDHC, pyruvate dehydrogenase complex; CitSyn, citrate synthase;ICDH, isocitrate dehydrogenase; GiuDH, glutamate dehydrogenase;2-KGDHC, 2-ketoglutarate dehydrogenase complex; SucDH, succinatedehydrogenase; MalE, malic enzyme; MaIDH, malate dehydyrogenase; As-pTA, aspartate tansaminase; AlaTA, alanine transaminase; AK, adenylatekinase; OAA, oxalacetate;Glu, glutamate; Ala, alanine; Suc, succinate; Cit,cirate; AsP, aspartate; Pyr, pyruvate; PEP, phosphoenolpyruvate; CoA, co-enzyme a; ACoA, acetyl-coenzyme A; Gluc, ghlcse; G6P, glucose-6-phos-phate; F6P, fructose-6phosphate; F16BP, fructose-1,6-bisphosphate;F26BP, fructose-2,6-bisphosphate; K catalytic subunit ofcAMP-dependentprotein kinase; DHP, dihydroxyacetone phosphate; GAP, glyceraldehydephosphate; 3PGA, 3-phosphoglyceraldehyde; 13DPGA, 1,3-diphospho-glyceraldehyde; 23DPGA, 2,3- diphosphoglyceraldehyde; 2PGA, 2-phos-phoglyceraldehyde; a-GP, a-glycerol phosphate; [AC, lactate; HIP,Hexose-phosphate Interconversion Pathway; sHIP, simplified HIP. In thesemodels, when a chemical species, such as citrate, may take on differentconcentrations in different cellular spaces (i.e., the extracellular space, cy-tosol, and mitochondrion) its abbreviation is postfixed with a number des-ignating the compartment to which it belongs (0, 1, and 2 respectively).

560

Page 12: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

A biochemical "AND gate"

Page 13: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

An "AND gate"in the glycolytic

pathway

Page 14: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

© 1999 Macmillan Magazines Ltd

Although living systems obey the laws ofphysics and chemistry, the notion offunction or purpose differentiates biol-

ogy from other natural sciences. Organismsexist to reproduce, whereas, outside religiousbelief, rocks and stars have no purpose.Selection for function has produced the liv-ing cell, with a unique set of properties thatdistinguish it from inanimate systems ofinteracting molecules. Cells exist far fromthermal equilibrium by harvesting energyfrom their environment. They are composedof thousands of different types of molecule.They contain information for their survivaland reproduction, in the form of their DNA.Their interactions with the environmentdepend in a byzantine fashion on this infor-mation, and the information and themachinery that interprets it are replicated byreproducing the cell. How do these proper-ties emerge from the interactions betweenthe molecules that make up cells and how arethey shaped by evolutionary competitionwith other cells?

Much of twentieth-century biology hasbeen an attempt to reduce biologicalphenomena to the behaviour of molecules.This approach is particularly clear in genet-ics, which began as an investigation into theinheritance of variation, such as differencesin the colour of pea seeds and fly eyes. Fromthese studies, geneticists inferred the exis-tence of genes and many of their properties,such as their linear arrangement along thelength of a chromosome. Further analysis ledto the principles that each gene controls thesynthesis of one protein, that DNA containsgenetic information, and that the geneticcode links the sequence of DNA to the structure of proteins.

Despite the enormous success of thisapproach, a discrete biological function canonly rarely be attributed to an individualmolecule, in the sense that the main purposeof haemoglobin is to transport gas moleculesin the bloodstream. In contrast, most biolog-ical functions arise from interactions among

many components. For example, in the signal transduction system in yeast that converts the detection of a pheromone intothe act of mating, there is no single proteinresponsible for amplifying the input signalprovided by the pheromone molecule.

To describe biological functions, we needa vocabulary that contains concepts such asamplification, adaptation, robustness, insu-lation, error correction and coincidencedetection. For example, to decipher how thebinding of a few molecules of an attractant toreceptors on the surface of a bacterium canmake the bacterium move towards theattractant (chemotaxis) will require under-standing how cells robustly detect andamplify signals in a noisy environment.

Having described such concepts, we need toexplain how they arise from interactionsamong components in the cell.

We argue here for the recognition of functional ‘modules’ as a critical level of bio-logical organization. Modules are composedof many types of molecule. They have dis-crete functions that arise from interactionsamong their components (proteins, DNA,RNA and small molecules), but these func-tions cannot easily be predicted by studyingthe properties of the isolated components.We believe that general ‘design principles’ —profoundly shaped by the constraints of evo-lution — govern the structure and functionof modules. Finally, the notion of functionand functional properties separates biology

impacts

NATURE | VOL 402 | SUPP | 2 DECEMBER 1999 | www.nature.com C47

From molecular to modular cell biologyLeland H. Hartwell, John J. Hopfield, Stanislas Leibler and Andrew W. Murray

Cellular functions, such as signal transmission, are carried out by ‘modules’made up of many species of interacting molecules. Understanding howmodules work has depended on combining phenomenological analysis withmolecular studies. General principles that govern the structure andbehaviour of modules may be discovered with help from synthetic sciencessuch as engineering and computer science, from stronger interactionsbetween experiment and theory in cell biology, and from an appreciation ofevolutionary constraints.

Action potentials are large, brief, highly nonlinearpulses of cell electrical potential which are central tocommunication between nerve cells. Hodgkin andHuxley’s analysis of action potentials29 exemplifiesunderstanding through in silico reconstruction. Theystudied the dynamical behaviour of the voltage-dependent conductivity of a nerve cell membranefor Na+ and K+ ions, and described this behaviour ina set of empirically based equations. At the time,there was no information available about thechannel proteins in nerve cell membranes that arenow known to cause these dynamical conductivities.From (conceptually) simple experiments on theseindividual conductivities, Hodgkin and Huxleyproduced simulations that quantitatively describedthe dynamics of action potentials, showed that theaction potentials would propagate along an axonwith constant velocity, and correctly described howthe velocity should change with axon radius andother parameters. Just as explanations ofhydrodynamic phenomena do not require knowledgeof the quantum chemistry of water, those who are interested in the behaviour of neural circuits need notknow how the particular channel proteins give rise to the Hodgkin–Huxley equations.

Box 1Phenomenological analysis of action potentials in nerve cells

–V (m

V)–V

(mV)

100806040200

100806040200

0 1 2 3 4 5 6

0 1 2 3 4 5 6

ms

ms

Modelling action potentials. The upper trace shows three membrane action potentials, responding to different strengths of stimulus, calculated by Hodgkin and Huxley, while the lower trace shows acorresponding series of experimentalrecordings. (Adapted from ref. 29.)

© 1999 Macmillan Magazines Ltd

Although living systems obey the laws ofphysics and chemistry, the notion offunction or purpose differentiates biol-

ogy from other natural sciences. Organismsexist to reproduce, whereas, outside religiousbelief, rocks and stars have no purpose.Selection for function has produced the liv-ing cell, with a unique set of properties thatdistinguish it from inanimate systems ofinteracting molecules. Cells exist far fromthermal equilibrium by harvesting energyfrom their environment. They are composedof thousands of different types of molecule.They contain information for their survivaland reproduction, in the form of their DNA.Their interactions with the environmentdepend in a byzantine fashion on this infor-mation, and the information and themachinery that interprets it are replicated byreproducing the cell. How do these proper-ties emerge from the interactions betweenthe molecules that make up cells and how arethey shaped by evolutionary competitionwith other cells?

Much of twentieth-century biology hasbeen an attempt to reduce biologicalphenomena to the behaviour of molecules.This approach is particularly clear in genet-ics, which began as an investigation into theinheritance of variation, such as differencesin the colour of pea seeds and fly eyes. Fromthese studies, geneticists inferred the exis-tence of genes and many of their properties,such as their linear arrangement along thelength of a chromosome. Further analysis ledto the principles that each gene controls thesynthesis of one protein, that DNA containsgenetic information, and that the geneticcode links the sequence of DNA to the structure of proteins.

Despite the enormous success of thisapproach, a discrete biological function canonly rarely be attributed to an individualmolecule, in the sense that the main purposeof haemoglobin is to transport gas moleculesin the bloodstream. In contrast, most biolog-ical functions arise from interactions among

many components. For example, in the signal transduction system in yeast that converts the detection of a pheromone intothe act of mating, there is no single proteinresponsible for amplifying the input signalprovided by the pheromone molecule.

To describe biological functions, we needa vocabulary that contains concepts such asamplification, adaptation, robustness, insu-lation, error correction and coincidencedetection. For example, to decipher how thebinding of a few molecules of an attractant toreceptors on the surface of a bacterium canmake the bacterium move towards theattractant (chemotaxis) will require under-standing how cells robustly detect andamplify signals in a noisy environment.

Having described such concepts, we need toexplain how they arise from interactionsamong components in the cell.

We argue here for the recognition of functional ‘modules’ as a critical level of bio-logical organization. Modules are composedof many types of molecule. They have dis-crete functions that arise from interactionsamong their components (proteins, DNA,RNA and small molecules), but these func-tions cannot easily be predicted by studyingthe properties of the isolated components.We believe that general ‘design principles’ —profoundly shaped by the constraints of evo-lution — govern the structure and functionof modules. Finally, the notion of functionand functional properties separates biology

impacts

NATURE | VOL 402 | SUPP | 2 DECEMBER 1999 | www.nature.com C47

From molecular to modular cell biologyLeland H. Hartwell, John J. Hopfield, Stanislas Leibler and Andrew W. Murray

Cellular functions, such as signal transmission, are carried out by ‘modules’made up of many species of interacting molecules. Understanding howmodules work has depended on combining phenomenological analysis withmolecular studies. General principles that govern the structure andbehaviour of modules may be discovered with help from synthetic sciencessuch as engineering and computer science, from stronger interactionsbetween experiment and theory in cell biology, and from an appreciation ofevolutionary constraints.

Action potentials are large, brief, highly nonlinearpulses of cell electrical potential which are central tocommunication between nerve cells. Hodgkin andHuxley’s analysis of action potentials29 exemplifiesunderstanding through in silico reconstruction. Theystudied the dynamical behaviour of the voltage-dependent conductivity of a nerve cell membranefor Na+ and K+ ions, and described this behaviour ina set of empirically based equations. At the time,there was no information available about thechannel proteins in nerve cell membranes that arenow known to cause these dynamical conductivities.From (conceptually) simple experiments on theseindividual conductivities, Hodgkin and Huxleyproduced simulations that quantitatively describedthe dynamics of action potentials, showed that theaction potentials would propagate along an axonwith constant velocity, and correctly described howthe velocity should change with axon radius andother parameters. Just as explanations ofhydrodynamic phenomena do not require knowledgeof the quantum chemistry of water, those who are interested in the behaviour of neural circuits need notknow how the particular channel proteins give rise to the Hodgkin–Huxley equations.

Box 1Phenomenological analysis of action potentials in nerve cells

–V (m

V)–V

(mV)

10080604020

0

10080604020

0

0 1 2 3 4 5 6

0 1 2 3 4 5 6

ms

ms

Modelling action potentials. The upper trace shows three membrane action potentials, responding to different strengths of stimulus, calculated by Hodgkin and Huxley, while the lower trace shows acorresponding series of experimentalrecordings. (Adapted from ref. 29.)

Page 15: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

© 1999 Macmillan Magazines Ltd

from other natural sciences and links it tosynthetic disciplines such as computer science and engineering.

Is cell biology modular?A functional module is, by definition, a dis-crete entity whose function is separable fromthose of other modules. This separationdepends on chemical isolation, which canoriginate from spatial localization or fromchemical specificity. A ribosome, the mod-ule that synthesizes proteins, concentratesthe reactions involved in making a polypep-tide into a single particle, thus spatially isolating its function. A signal transductionsystem, on the other hand, such as those thatgovern chemotaxis in bacteria or mating inyeast1–3, is an extended module that achievesits isolation through the specificity of the initial binding of the chemical signal (forexample, chemoattractant or pheromone)to receptor proteins, and of the interactionsbetween signalling proteins within the cell.Modules can be insulated from or connectedto each other. Insulation allows the cell tocarry out many diverse reactions withoutcross-talk that would harm the cell, whereasconnectivity allows one function to influ-ence another. The higher-level properties ofcells, such as their ability to integrate infor-mation from multiple sources, will bedescribed by the pattern of connectionsamong their functional modules.

The notion of a module is useful only if itinvolves a small fraction of the cell compo-nents in accomplishing a relativelyautonomous function. Are modules real?Several lines of evidence suggest that theyare. Some modules, such as those for proteinsynthesis, DNA replication, glycolysis, andeven parts of the mitotic spindle (the cellularmachinery that ensures the correct distribu-tion of chromosomes at cell division), havebeen successfully reconstituted in vitro. Others are intrinsically more difficult to

reconstruct from purified components and,for these, other methods have established thevalidity of the module. One method is totransplant the module into a different type ofcell. For example, the action potentials char-acteristic of nerve and muscle cells have beenreconstituted by transplanting ion channelsand pumps from such cells into non-excitable cells4. Another approach is to createtheoretical models of the system and verifythat their predictions match reality. Thisapproach was used to describe the genera-tion of action potentials long before a molec-ular description of membrane channelsexisted (see Box 1). This was the first exampleof ‘in silico reconstitution’, which will have anincreasingly important role in cell biology.

Functional modules need not be rigid,fixed structures; a given component maybelong to different modules at differenttimes. The function of a module can bequantitatively regulated, or switchedbetween qualitatively different functions, bychemical signals from other modules. High-er-level functions can be built by connectingmodules together. For example, the super-module whose function is the accurate dis-tribution of chromosomes to daughter cellsat mitosis contains modules that assemblethe mitotic spindle, a module that monitorschromosome alignment on the spindle, anda cell-cycle oscillator that regulates transi-tions between interphase and mitosis.

One must also ask how a cell integratesinformation and instructions that comefrom the many different modules that moni-tor its internal and external environment.Neurobiology has an analogous problem,where the central nervous system integratesinformation from different senses and dic-tates the organism’s behaviour. Does cellularintegration merely emerge from a web ofpairwise connections between different sen-sory modules, or are there specific modulesthat act as a cellular equivalent of the central

nervous system — integrating informationand resolving conflicts?

Complete understanding of a biologicalmodule has depended on the ability of phe-nomenological and molecular analyses toconstrain each other (see Box 2). Phenome-nological models have fewer variables thanmolecular descriptions, making them easierto constrain with experimental data, where-as identifying the molecules involved makesit possible to perturb and analyse modules inmuch greater detail. Thus, the demonstra-tion that genetic information for virulencecould be transferred between bacteriaprompted the identification of the informa-tion-carrying molecule as DNA, before themolecular processes involved in virulenceand the structure of DNA were understood.The discovery that genetic informationresided in the DNA encouraged structuralstudies, which then suggested how DNAencodes information and transmits it fromgeneration to generation.

Modular structures may facilitate evolu-tionary change. Embedding particular functions in discrete modules allows thecore function of a module to be robust tochange, but allows for changes in the proper-ties and functions of a cell (its phenotype) byaltering the connections between differentmodules. If the function of a protein were todirectly affect all properties of the cell, itwould be hard to change that protein,because an improvement in one functionwould probably be offset by impairments inothers. But if the function of a protein isrestricted to one module, and the connec-tions of that module to other modules arethrough individual proteins, it will be mucheasier to modify, make and prune connec-tions to other modules. This idea is support-ed by the analogous observation that proteins that interact with many other proteins, such as histones, actin and tubulin,have changed very little during evolution,

impacts

C48 NATURE | VOL 402 | SUPP | 2 DECEMBER 1999 | www.nature.com

The bacterial virus lambda can exist in two statesinside a bacterial cell. In the lytic state, the virusreplicates, producing about 100 progeny virusparticles, and releases them by inducing lysis of thehost cell. In the lysogenic state, the viral DNA isintegrated into the bacterial chromosome and theproduction of a single viral protein, the repressor,inhibits expression of the other viral genes. Thephysiology of the host cell and other factors regulatethe probability that an infecting lambda virus willbecome a lysogen, instead of replicating andinducing lysis30.

Elegant phenomenological experiments inferredthe existence of bacteriophages and the existenceof lytic and lysogenic states31 well before the virusescould be seen as physical particles. The isolation ofmutants that biased the switch between lysis and

lysogeny defined genes whose products formed partof the switch and sites on the DNA at which theseproducts bound. Sophisticated analysis of theinteractions between the mutants led to proposalsabout the circuitry of the switch, and specificproposals for which DNA sites bound whichregulatory proteins. These proposals were verifiedby molecular analyses that showed that therepressor bound to DNA32 and produced a verydetailed description of the biochemical interactionsamong repressor, other DNA-binding proteins andDNA. Key predictions of models of the switch wereverified by reconstructing it in geneticallyengineered bacteria33, and by simulating itsbehaviour using computer models derived fromtools used to simulate the behaviour of electricalcircuits34.

Box 2 A decision-making module in bacteriophage lambda

False-colour transmission electron micrographof lambda bacteriophages (!13,500).

INST

ITU

T P

AST

EUR

/CN

RI/

SPL

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Systems biology

can we understand and compute biological complexity ?

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6564636261605958575655545352515049484746454443424140393837363534333231302928272625242322212019181716151413121110987654321

www.sciencemag.org SCIENCE VOL 298 25 OCTOBER 2002 763

This inevitably leads to widespread hap-loinsufficiency at several gene loci, onlya fraction of which provide the nascenttumor cell with some degree of selectiveadvantage. Do tumor suppressor genesexist for which haploinsuff iciency ismore strongly selected for than completeinactivation? Only accurate and quantita-tive genome-wide expression profiling bymicroarray or proteomic analysis will en-able such gene-dosage defects to be iden-tified. Analyzing targeted hypomorphic

alleles in experimental animals should fa-cilitate the identif ication of modifiergenes, their tissue-specific dosage thresh-olds, and their interaction with more pen-etrant tumor suppressor genes and envi-ronmental mutagens.

References1. A. G. Knudson Jr., Proc. Natl. Acad. Sci. U.S.A. 68, 820

(1971).2. S. B. Gruber et al., Science 297, 2013 (2002).3. K. H. Goss et al., Science 297, 2051 (2002).4. K. Spring et al., Nature Genet. 32, 185 (2002).5. S. Venkatachalam et al., EMBO J. 17, 4657 (1998).

6. W. J. Song et al., Nature Genet. 23, 166 (1999).7. R. Smits et al., Gastroenterology 119, 1045 (2000).8. M. L. Fero et al., Nature 396, 177 (1998).9. M. Kucherlapati et al., Proc. Natl. Acad. Sci. U.S.A. 99,

9924 (2002).10. M. Swift et al., N. Engl. J. Med. 325, 1831 (1991).11. N. G. Howlett et al., Science 297, 606 (2002).12. H. Meijers-Heijboer et al., Nature Genet. 31, 55

(2002).13. K. Inoue et al., Genes Dev. 15, 2934 (2001).14. H. Miyoshi et al., Cancer Res. 62, 2261 (2002).15. Y. Zhu et al., Science 296, 920 (2002).16. C. Wetmore et al., Cancer Res. 60, 2239 (2000).17. R. H. Zurawel et al., Genes Chr. Cancer 28, 77 (2000).18. B. Kwabi-Addo et al., Proc. Natl. Acad. Sci. U.S.A. 98,

11563 (2001).

S C I E N C E ’ S C O M P A S S

CRE

DIT

:KAT

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TLIF

F/SC

IEN

CE

Cells and microorganisms have an im-pressive capacity for adjusting theirintracellular machinery in response to

changes in their environment, food avail-ability, and developmental state. Add to thisan amazing ability to correct internal er-rors—battling the effects of such mistakesas mutations or misfolded proteins—and wearrive at a major issue of contemporary cellbiology: our need to comprehend the stag-gering complexity, versatility, and ro-bustness of living systems. Althoughmolecular biology offers many spec-tacular successes, it is clear that thedetailed inventory of genes, pro-teins, and metabolites is not suf-ficient to understand the cell’scomplexity (1). As demon-strated by two papers inthis issue—Lee et al. (2)on page 799 and Miloet al. (3) on page824—viewing thecell as a network ofgenes and proteinsoffers a viablestrategy for ad-dressing thecomplexity ofliving systems.

According to thebasic dogma of molec-ular biology, DNA is the ultimate deposito-ry of biological complexity. Indeed, it isgenerally accepted that information stor-age, information processing, and the exe-cution of various cellular programs residein distinct levels of organization: the cell’sgenome, transcriptome, proteome, and

metabolome. However, the distinctness ofthese organizational levels has recentlycome under fire. For example, althoughlong-term information is stored almost ex-clusively in the genome, the proteome iscrucial for short-term information storage(4, 5) and transcription factor–controlledinformation retrieval is strongly influencedby the state of the metabolome. This inte-gration of different organizational levelsincreasingly forces us to view cellularfunctions as distributed among groups ofheterogeneous components that all interact

within large networks (6, 7). There is clearevidence for the existence of such cellularnetworks: For example, the proteome orga-nizes itself into a protein interaction net-work and metabolites are interconvertedthrough an intricate metabolic web (7). Thefinding that the structures of these net-works are governed by the same principlescomes as a surprise, however, offering anew perspective on cellular organization.

A simple complexity pyramid com-posed of the various molecular compo-nents of the cell—genes, RNAs, proteins,and metabolites—summarizes this newparadigm (see the figure). These elemen-tary building blocks organize themselvesinto small recurrent patterns, called path-

ways in metabolism and motifs in ge-netic-regulatory networks. In turn,

motifs and pathways are seamlesslyintegrated to form functional mod-

P E R S P E C T I V E S : S Y S T E M S B I O L O G Y

Life’s Complexity PyramidZoltán N. Oltvai and Albert-László Barabási

Z. N. Oltvai is in the Department of Pathology,Northwestern University, Chicago, IL 60611, USA. E-mail: [email protected] A.-L. Barabási is in the De-partment of Physics, University of Notre Dame,Notre Dame, IN 46556, USA. E-mail: [email protected]

From the particular to the univer-sal. The bottom of the pyramid

shows the traditional representa-tion of the cell’s functional or-

ganization: genome, tran-scriptome, proteome, and

metabolome (level 1).There is remarkable in-

tegration of the vari-ous layers both at

the regulatory andthe structural

level. Insightsinto the logic

of cellular organization canbe achieved when we view

the cell as a complex network in which thecomponents are connected by functional links.At the lowest level, these components form ge-netic-regulatory motifs or metabolic pathways(level 2), which in turn are the building blocksof functional modules (level 3). These modulesare nested, generating a scale-free hierarchicalarchitecture (level 4). Although the individualcomponents are unique to a given organism,the topologic properties of cellular networksshare surprising similarities with those of natu-ral and social networks. This suggests that uni-versal organizing principles apply to all net-works, from the cell to the World Wide Web.

Org

an

i sm

sp

ec

i fi c

i ty

Un

i ve

rs

al i t y

Processing Execution

LEU1 BAT1 ILV2

Leu3

Proteins Metabolites

ATP

Genes

Functionalmodules

Large-scale organization

Metabolic pathways

UMP

ATP

Mg2+Mg2+

Mg2+

ADP

UDP

Regulatory motifs

mRNA

UTP CTP

ATP ADP ATP ADP

Information storage

Page 18: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Synthetic gene networks

- reconstructingbiological complexity

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Page 20: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965
Page 21: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Construction of a genetic toggle switch in Escherichia Coli

Gardner, Cantor, Collins, Nature 403, 339 (2000)

Page 22: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

http://people.msoe.edu/~tritt/graphics/rsnor.gif

The RS flip flop - an electronic analogon

Page 23: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

T1T2: transcription terminator

lacI: repressor gene

RBS1: ribosome binding site

P1: promotor, either PLs1con or PLtetO-1

Ptrc-2: promotor

rbs E: ribosome binding site

R1: repressor gene; either cIts or tetR

rbs B: ribosome binding site

GFPmut3: gene for a GFP mutant

Implementation in a plasmid

Page 24: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

A mathematical model for the toggle switch

u: concentration of repressor 1v: concentration of repressor 2α1: effective rate of synthesis of repressor 1α2: effective rate of synthesis of repressor 1β,γ: cooperativity of binding of repr. 1,2 to therespective promotors

degradation/dilution of repressors

cooperativerepression

of transcription minimum requirements for bistability:

β,γ > 1 (sigmoidal shape)rates of synthesis must be balancedappropriate initial state

Page 25: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

highercooperativitylargerregion ofbistability

at leastone repressormust have cooperativity > 1

unbalancedpromotor strengths- monostable

Page 26: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

pTAK plasmids use lacI/Ptrc2 + cIts/PLs1con; differ in RBS1

switching the state w/ IPTG

switching the state w/ thermal pulse

all fourpTAK exhibitbistability

Page 27: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Why synthetic biology ?

• simplify and build biological systems to learn more about biology - test current understanding of biology

• control over all variables - stronger link between theory and experiment

• biology as an extension of chemistry

• biology as an engineering discipline

• technological applications: - "better" or novel organisms with novel applications

Page 28: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

©!!""#!Nature Publishing Group!

!

Foundations for engineering biologyDrew Endy1

Engineered biological systems have been used to manipulate information, construct materials, process chemicals,produce energy, provide food, and help maintain or enhance human health and our environment. Unfortunately, ourability to quickly and reliably engineer biological systems that behave as expected remains quite limited. Foundationaltechnologies that make routine the engineering of biology are needed. Vibrant, open research communities and strategicleadership are necessary to ensure that the development and application of biological technologies remainsoverwhelmingly constructive.

Please complete one of the following projects in the next hour:write down the DNA sequence that programmes a biofilm totake a photograph and perform distributed edge-detection onthe light-encoded image; or, the DNA sequence that encodes a

ring oscillator that works inside yeast; or, the DNA sequence thatprogrammes any mammalian cell to count up to 256 in response to ageneric input signal; or, the DNA sequence that programmes anyprokaryote to produce 25 g l21 artemisinic acid. Alternatively,describe in convincing detail the starting materials, experimentalscreens and genetic selections that could be used to evolve biologicalsystems to perform these tasks… Time’s up!The abovementioned applications of synthetic biology could find

uses in the construction of templated surfaces for nanoscale materialsfabrication, the characterization of physical performance limits foreukaryotic gene expression, the study and control of cell division,animal development, ageing and cancer by modest amounts ofgenetically encoded memory and logic, or the cost-effective pro-duction of an anti-malaria drug precursor, respectively. Further-more, each application is physically plausible, or is the directextension of an already demonstrated result1–3. Yet, each projectwould be a unique, expert-driven research problem, with uncertaintimes to completion, costs and probabilities of success. For example,construction of the first genetically encoded ‘ring oscillator’ inbacteria took two of the world’s best biophysicists at least one yearof research2,4. Constructing a second genetically encoded ring oscil-lator would probably cost another group as much effort. In contrast,any competent electrical engineering student could build manyworking electronic ring oscillators in under one hour.In 1978, Szybalksi and Skalka wrote5, “The work on restriction

nucleases not only permits us easily to construct recombinant DNAmolecules and to analyse individual genes, but also has led us into thenew era of ‘synthetic biology’ where not only existing genes aredescribed and analyzed but also new gene arrangements can beconstructed and evaluated.” Twenty-seven years later, despitetremendous individual successes in genetic engineering and biotech-nology6–8, why is the engineering of useful synthetic biologicalsystems still an expensive, unreliable and ad hoc research process?The first possibility is that we don’t yet know enough about

biological systems, or that biological systems are too complex toreliably engineer, or both. For example, some descriptions of naturalbiological systems are notoriously complex9,10. The large number ofunique functional components combined with unexpected inter-actions among components (for example, pleiotropy) makes it hardto imagine that we might reliably engineer the behaviour of complexbiological systems. Furthermore, it is possible that the designs of

natural biological systems are not optimized by evolution for thepurposes of human understanding and engineering11. Thankfully,these concerns are best evaluated by attempting to surmount them12.The second possibility is that the engineering of biology remains a

research problem because we have never invented and implementedfoundational technologies that would make it an engineeringproblem. Stated plainly, the engineering of biology remains complexbecause we have never made it simple (T. F. Knight, personalcommunication). As above, the practicality of making biologicalengineering simple can best be evaluated by attempting to make itsimple. Success would help to “create the discipline of syntheticbiology: an engineering technology based on living systems”13.Failures would directly illuminate and help prioritize the mostrelevant gaps in our current understanding of natural living systems,and suggest how we might best eventually come to understand andapply nature’s original technology14.

What and why is synthetic biology?The recent and ongoing interest in ‘synthetic biology’ is being drivenby at least four different groups: biologists, chemists, ‘re-writers’ andengineers. Briefly, for biologists, the ability to design and constructsynthetic biological systems provides a direct and compellingmethod for testing our current understanding of natural biologicalsystems4,15; disagreements between expected and observed systembehaviour can serve to highlight the science that is worth doing. Forchemists, biology is chemistry, and thus synthetic biology is anextension of synthetic chemistry; the ability to create novel moleculesand molecular systems allows the development of useful diagnosticassays and drugs, expansion of genetically encoded functions, studyof the origins of life, and so on16. For ‘re-writers’, the designs ofnatural biological systems may not be optimized for human inten-tions (for example, scientific understanding, health and medicine);synthetic biology provides an opportunity to test the hypothesis thatthe genomes encoding natural biological systems can be ‘re-written’,producing engineered surrogates that might usefully supplant somenatural biological systems11. Finally, for engineers, biology is atechnology; building upon past work in genetic engineering, syn-thetic biology seeks to combine a broad expansion of biotechnologyapplications with—as the focus of this article—an emphasis on thedevelopment of foundational technologies that make the design andconstruction of engineered biological systems easier.

Foundations for engineering biologyMany times over, individuals and groups have adapted and applieddifferent resources from nature to the service of human needs such as

REVIEWS

1Division of Biological Engineering, Massachusetts Institute of Technology, Room 68-580, Koch Biology Building, 31 Ames Street, Cambridge, Massachusetts 02139, USA.

Vol 438|24 November 2005|doi:10.1038/nature04342

449

©!!""#!Nature Publishing Group!

!

Foundations for engineering biologyDrew Endy1

Engineered biological systems have been used to manipulate information, construct materials, process chemicals,produce energy, provide food, and help maintain or enhance human health and our environment. Unfortunately, ourability to quickly and reliably engineer biological systems that behave as expected remains quite limited. Foundationaltechnologies that make routine the engineering of biology are needed. Vibrant, open research communities and strategicleadership are necessary to ensure that the development and application of biological technologies remainsoverwhelmingly constructive.

Please complete one of the following projects in the next hour:write down the DNA sequence that programmes a biofilm totake a photograph and perform distributed edge-detection onthe light-encoded image; or, the DNA sequence that encodes a

ring oscillator that works inside yeast; or, the DNA sequence thatprogrammes any mammalian cell to count up to 256 in response to ageneric input signal; or, the DNA sequence that programmes anyprokaryote to produce 25 g l21 artemisinic acid. Alternatively,describe in convincing detail the starting materials, experimentalscreens and genetic selections that could be used to evolve biologicalsystems to perform these tasks… Time’s up!The abovementioned applications of synthetic biology could find

uses in the construction of templated surfaces for nanoscale materialsfabrication, the characterization of physical performance limits foreukaryotic gene expression, the study and control of cell division,animal development, ageing and cancer by modest amounts ofgenetically encoded memory and logic, or the cost-effective pro-duction of an anti-malaria drug precursor, respectively. Further-more, each application is physically plausible, or is the directextension of an already demonstrated result1–3. Yet, each projectwould be a unique, expert-driven research problem, with uncertaintimes to completion, costs and probabilities of success. For example,construction of the first genetically encoded ‘ring oscillator’ inbacteria took two of the world’s best biophysicists at least one yearof research2,4. Constructing a second genetically encoded ring oscil-lator would probably cost another group as much effort. In contrast,any competent electrical engineering student could build manyworking electronic ring oscillators in under one hour.In 1978, Szybalksi and Skalka wrote5, “The work on restriction

nucleases not only permits us easily to construct recombinant DNAmolecules and to analyse individual genes, but also has led us into thenew era of ‘synthetic biology’ where not only existing genes aredescribed and analyzed but also new gene arrangements can beconstructed and evaluated.” Twenty-seven years later, despitetremendous individual successes in genetic engineering and biotech-nology6–8, why is the engineering of useful synthetic biologicalsystems still an expensive, unreliable and ad hoc research process?The first possibility is that we don’t yet know enough about

biological systems, or that biological systems are too complex toreliably engineer, or both. For example, some descriptions of naturalbiological systems are notoriously complex9,10. The large number ofunique functional components combined with unexpected inter-actions among components (for example, pleiotropy) makes it hardto imagine that we might reliably engineer the behaviour of complexbiological systems. Furthermore, it is possible that the designs of

natural biological systems are not optimized by evolution for thepurposes of human understanding and engineering11. Thankfully,these concerns are best evaluated by attempting to surmount them12.The second possibility is that the engineering of biology remains a

research problem because we have never invented and implementedfoundational technologies that would make it an engineeringproblem. Stated plainly, the engineering of biology remains complexbecause we have never made it simple (T. F. Knight, personalcommunication). As above, the practicality of making biologicalengineering simple can best be evaluated by attempting to make itsimple. Success would help to “create the discipline of syntheticbiology: an engineering technology based on living systems”13.Failures would directly illuminate and help prioritize the mostrelevant gaps in our current understanding of natural living systems,and suggest how we might best eventually come to understand andapply nature’s original technology14.

What and why is synthetic biology?The recent and ongoing interest in ‘synthetic biology’ is being drivenby at least four different groups: biologists, chemists, ‘re-writers’ andengineers. Briefly, for biologists, the ability to design and constructsynthetic biological systems provides a direct and compellingmethod for testing our current understanding of natural biologicalsystems4,15; disagreements between expected and observed systembehaviour can serve to highlight the science that is worth doing. Forchemists, biology is chemistry, and thus synthetic biology is anextension of synthetic chemistry; the ability to create novel moleculesand molecular systems allows the development of useful diagnosticassays and drugs, expansion of genetically encoded functions, studyof the origins of life, and so on16. For ‘re-writers’, the designs ofnatural biological systems may not be optimized for human inten-tions (for example, scientific understanding, health and medicine);synthetic biology provides an opportunity to test the hypothesis thatthe genomes encoding natural biological systems can be ‘re-written’,producing engineered surrogates that might usefully supplant somenatural biological systems11. Finally, for engineers, biology is atechnology; building upon past work in genetic engineering, syn-thetic biology seeks to combine a broad expansion of biotechnologyapplications with—as the focus of this article—an emphasis on thedevelopment of foundational technologies that make the design andconstruction of engineered biological systems easier.

Foundations for engineering biologyMany times over, individuals and groups have adapted and applieddifferent resources from nature to the service of human needs such as

REVIEWS

1Division of Biological Engineering, Massachusetts Institute of Technology, Room 68-580, Koch Biology Building, 31 Ames Street, Cambridge, Massachusetts 02139, USA.

Vol 438|24 November 2005|doi:10.1038/nature04342

449

Page 29: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

INSTITUTE OF PHYSICS PUBLISHING NANOTECHNOLOGY

Nanotechnology 16 (2005) R1–R8 doi:10.1088/0957-4484/16/1/R01

TUTORIAL

Synthetic biology for nanotechnologyPhilip Ball

Nature, 4-6 Crinan Street, London N1 9XW, UK

E-mail: [email protected]

Received 19 October 2004, in final form 21 October 2004Published 8 December 2004Online at stacks.iop.org/Nano/16/R1

AbstractSynthetic biology—the redesign of biological molecules, structures andorganisms—is potentially one of the most powerful emerging technologiestoday. The modification of biological structures has already been pursuedfor a variety of nanotechnological objectives; but synthetic biology couldprovide the tools and understanding needed both to develop‘nanobiotechnology’ in a more systematic manner and to expand the scopeof what it might achieve. In this article I shall review what has been attainedso far in this field, and look at some of the nanoscale possibilities that anengineering approach to cell biology might herald.

1. Introduction

The benefits of biomimesis in nanotechnology are widelyrecognized: evolution has already encountered and solvedmany of the challenges that nanotechnologists face, and evenif there is no guarantee that nature’s solutions can be translatedto a technological setting, nevertheless biology does seem bean abundant storehouse of ideas [1]. There is, furthermore,a grey area where biomimesis merges with bioengineering—where the pre-existing nanoscale devices and structures of thecell can be adapted to suit technological goals. This too isan avenue that now has a substantial and fertile (if relativelyrecent) history of exploration, for example in the use of proteinmolecular motors to achieve directed transport of nanoscaleparticles [2, 3].

Such studies display a spirit of ‘bringing biology intonanotech’—which is perhaps another way of saying that theytend not to hold much intrinsic appeal to the molecular or cellbiologist. Yet there is now an emerging area of research thatone can regard as moving in the other direction: rooted inbiology, it reaches out to embrace the biological strands ofnanotechnology. This is the field of synthetic biology [4, 5].It has at some level one of the boldest and most controversialagendas in fundamental biological research: to turn biologyinto an applied, engineering science, ultimately to the degreethat entirely new organisms will be designed and chemicallysynthesized from scratch [6].

It is worth asking, even (especially?) at this nascentstage in the field’s development, what synthetic biology

has to offer the nanotechnologist. At present, ‘borrowing’from biology has tended to happen in a rather piecemeal,opportunistic manner, and it generally fails to take advantageof the extraordinary degree and hierarchy of organizationthat biology is clearly capable of generating. If syntheticbiology realizes even a part of its promise, the implications fornanotechnology could be profound. What has already beenachieved, and what might be in store?

2. What is synthetic biology?

In retrospect, synthetic biology seems an inevitable enterprise.Indeed, the notion was debated at least 16 years ago [7]. Onecould even argue that any manipulation of living organismsintroduces a ‘synthetic’ element into biology, and from thatperspective one might have to admit medical prosthesis(which has a history at least two millennia old) as an aspectof the field. Moreover, the advent of recombinant DNAtechnology in the 1970s made it possible to ‘synthesize’ newgenetic configurations, giving rise to the discipline of geneticengineering, which, in its very name, acknowledges an elementof artificiality.

Simon [8] has proposed four criteria for ‘artificialsciences’ that distinguish them from the natural sciences.

(1) Artificial things are synthesized (though not always orusually with full forethought) by human beings.

(2) Artificial things may imitate appearances in natural thingswhile lacking, in one or many respects, the reality of thelatter.

0957-4484/05/010001+08$30.00 © 2005 IOP Publishing Ltd Printed in the UK R1

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Synthetic biology and physics

• physics of complex systems

• non-linear dynamics

• stochastic dynamics

• non-equilibrium systems

• biophysics of gene regulation

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Synthetic biology groups

http://syntheticbiology.org/

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Synthetic biology groups

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Synthetic biology groups

Page 34: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Synthetic biology groups

Page 35: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Synthetic biology groups

Page 36: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Top 500 World University

World

RankInstitution Region

Regional

RankCountry

National

Rank

Score on

Alumni

Score on

Award

Score on

HiCi

Score on

N&S

Score on

SCI

Score on

Size

Total

Score

1 Harvard Univ Americas 1 USA 1 100 100 100 100 100 73.6 100

2 Univ Cambridge Europe 1 UK 1 96.3 91.5 53.8 59.5 67.1 66.5 72.6

3 Stanford Univ Americas 2 USA 2 39.7 70.7 88.4 70 71.4 65.3 72.5

4 Univ California - Berkeley Americas 3 USA 3 70.6 74.5 70.5 72.2 71.9 53.1 72.1

5 Massachusetts Inst Tech (MIT) Americas 4 USA 4 72.9 80.6 66.6 66.4 62.2 53.6 69.7

6 California Inst Tech Americas 5 USA 5 57.1 69.1 59.1 64.5 50.1 100 66

7 Columbia Univ Americas 6 USA 6 78.2 59.4 56 53.6 69.8 45.8 61.8

8 Princeton Univ Americas 7 USA 7 61.1 75.3 59.6 43.5 47.3 58 58.6

8 Univ Chicago Americas 7 USA 7 72.9 80.2 49.9 43.7 54.1 41.8 58.6

10 Univ Oxford Europe 2 UK 2 62 57.9 48 54.3 66 46 57.6

11 Yale Univ Americas 9 USA 9 50.3 43.6 59.1 56.6 63 49.3 55.9

12 Cornell Univ Americas 10 USA 10 44.9 51.3 56 48.4 65.2 40.1 54.1

13 Univ California - San Diego Americas 11 USA 11 17.1 34 59.6 54.8 65.6 47.1 50.5

14 Univ California - Los Angeles Americas 12 USA 12 26.4 32.1 57.6 47.5 77.3 34.9 50.4

15 Univ Pennsylvania Americas 13 USA 13 34.2 34.4 57 41.7 73.6 40 50.1

16 Univ Wisconsin - Madison Americas 14 USA 14 41.5 35.5 53.3 45.1 68.3 29.3 48.8

17 Univ Washington - Seattle Americas 15 USA 15 27.7 31.8 53.3 47.6 75.5 27.8 48.5

18 Univ California - San Francisco Americas 16 USA 16 0 36.8 55.5 54.8 61.1 48.2 47.7

19 Tokyo Univ Asia/Pac 1 Japan 1 34.8 14.1 41.4 51.5 85.5 35.2 46.7

20 Johns Hopkins Univ Americas 17 USA 17 49.5 27.8 40.7 52.2 68.8 25.3 46.6

21 Univ Michigan - Ann Arbor Americas 18 USA 18 41.5 0 61.5 41.6 76.9 31.2 44.5

22 Kyoto Univ Asia/Pac 2 Japan 2 38.3 33.4 36.9 36.2 72.4 31.7 43.9

23 Imperial Coll London Europe 3 UK 3 20.1 37.4 40 39.7 64.2 40.2 43.4

24 Univ Toronto Americas 19 Canada 1 27.1 19.3 38.5 36.5 78.3 44.8 42.8

25 Univ Illinois - Urbana Champaign Americas 20 USA 19 40.1 36.6 45.5 33.6 57.7 26.3 42.5

Copyright © 2006 Institute of Higher Education, Shanghai Jiao Tong University, All Rights Reserved.

!

Synthetic biology groups

✓✓✓✓

✓✓

(✓)

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Synthetic biology conference

Page 38: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

Science in 2020

Page 39: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

DNA-SelbstorganisationKarlsruhe - 30.01.07

Page 40: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

DNA-SelbstorganisationKarlsruhe - 30.01.07

Systems biology emerges as a new discipline

Ex vivo molecular-computer diagnosis

2005 2010

New representations ofbiological systems as ...

Page 41: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

DNA-SelbstorganisationKarlsruhe - 30.01.07

In vivo molecular-computer diagnosis

Individualised medicine

2010 2015

made for purpose “informed matter”(from supramolecular chemistry)

?

Page 42: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

DNA-SelbstorganisationKarlsruhe - 30.01.07

2015 2020

Personalised in situ molecular-computer “smart drug”

Novel biologically inspired computing architectures andparadigms

Biological productsdesigned by simulation/synthetic biology

Molecular computers increasinglyused to design alternative “smart”medical therapies

Page 43: Synthetic Biology - uni-muenchen.de · The lac operon Jacob Monod Nobel prize (Physiology/Medicine) 1965

http://www.nature.com/nature/comics/syntheticbiologycomic/index.html

and finally ...

http://openwetware.org/wiki/Main_Page