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Systems Biology

Looking at opportunitiesand challengesin applyingsystemstheory to molecularand cell biology.

S

ystems theory or systems science has never

managed to achieve widespread and inde-

pendent status in curricula, departments, and

journals but instead acts as an umbrella for a

number of research activities across the physi-

calandengineeringsciences. Now, withrevolu-

tionary developments in the life sciences, there is renewedinterest in systems thinking. In this article, we survey op-

portunities and challenges for the application of systems

theory to biology in the postgenomic eraa new area of re-

search also referred to as systems biology.

With the sequencing of DNA for a number of genomes,

scientists now have an inventory of genes available to em-

bark on the study of the organization and control of genetic

pathways. This new phase in the biological revolution, the

postgenomic era, is closely associated with the fields

genomics, transcriptomics, proteomics, and metabolo-

mics (called the omics for short). These fields take us

from the DNA sequence of a gene to the structure of the

product forwhich it codes (usually a protein)to theactivity

of that protein and its function within a cell, the tissue, and

ultimatively the organism. A series of articles in Nature [1]

are recommended for an introduction to these research ar-

eas of the biomedical sciences.

With the emergence of the omics, molecular biology

currently witnesses a shift of focus from molecular charac-

terization to the understanding of functional activity. The

two central questions scientists investigate are What are

the genes functional role? and How do genes and/or pro-

teins interact? Answeringthesequestions hasbecome pos-

sible with new high-throughput technologies to take

measurements at the molecular level. In the past, single

genes were studied, but with DNA microarray technology

we can now measure the activity levels of thousands of

38 IEEE Control Systems Magazine August 20030272-1708/03/$17.002003IEEE

EYEWIRE

By Olaf Wolkenhauer, Hiroaki Kitano, and Kwang-Hyun Cho


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genes at thesame time. Thus it becomes possible to identify

interrelationships between groups of genes (with respectto

their functional role) and to analyze dynamic interactions

among genes (gene networks). Similarly, proteomics re-

search shows that most proteinsinteractwith several other

proteins, andit is increasingly understood that the function

of a protein is appropriately described in the context of its

interactions with other proteins.Most of these interactions are the

consequence of dynamic and con-

trolled processes, and it is not sur-

prising thatthere is renewedinterest

in the application of systems think-

ing to biology.

The rest of this article is orga-

nized as follows. We first introduce systems biology in the

context of the study of complex systems, reviewing a num-

berof relatedandrelevant areasof research anddefine com-

plexity in thecontextof biological systems. Systems biology

has a history, and its early stages in the 1960s involved emi-

nent researchers, including Wiener, Kalman, Bertalanffy,

Rosen, and Mesarovic. We discuss why these attempts dis-

appeared from the research agendas and why there is re-

newed interest in the postgenomic era of the life sciences.

We then followwithtwoexamples of systems biology before

outlining current activities from groups around the world.

We conclude by listing some of the challenges and hurdles

for this (re)emerging field.

Genomic CyberneticsThe understanding of causality and coping with complexity

is not the holy grail of science but part of its very essence.

Notsurprisinglythen, complexity studies have remainedas

elusive as inconclusive.

Weaver [2] defined disorganized complexity as a prob-

lemin whichthenumber ofvariablesis verylarge andany of

these variablesis best describedas a randomprocess. Here

we are at themolecular level, andthe most successful for-

mal methods for representing phenomena at this level de-

rive from statistical considerations. In the context of the

cell, at the cellular level, matters are complicated by the

fact that organization becomes an essential feature of the

processes under consideration. Weaver referred to prob-

lems in which a large numberof factors are interrelatedinto

a wholeas organized complexity. The number of variables

is too large to be dealt with in the Newtonian realm of phys-

ics and mathematical modeling, and the systems are too or-

ganized to allow statistical techniques. At the time, Weaver

described organized complexity as the challenge for sci-

ence in the coming 50 years. The enthusiasm he expressed

in 1948 is very similar to how one feels today in the

postgenomic era of the life sciences: It is doubtless true

that we are only scratching the surface of the cancer prob-

lem, but at least there are now some tools to dig with and

there have been located some spots beneath which almost

surely there is pay-dirt [2].

Following Hakens synergetics, chaos theory, and frac-

tals, the science of self-organized criticality [3], nonequili-

brium physics, power laws, and emergent phenomena

renewed the interest in complexity studies over the last de-

cade orso.Thesestudiesdevelopedmostlywithintheareas

of physics and mathematics. They are seeking general prin-

ciple of phenomena that can be observed in a wide range of

disciplines. Kauffmans workon genetic networks [4] paved

the way for complexity studies in biology. The work of

Goodwin [5], [6], Harrison [7], and Meinhardt [8] marked a

trend toward an approach more focused on specific organ-

isms but continued to investigate cellular processes and

morphological development in evolutionary terms. As Har-

old pointed out in his recent book: Complexity studies is a

fresh label for a well-known pigeonhole: general systems

theory, that waspioneered by LudwigvonBertalanffy in the

1930s [9, p. 222]. Systems biology is an emerging field that

continues this research into the postgenomic era of the life

sciences [10]-[12]. Complexity studies and systems biology

are different in that the latter takes a signal- and sys-

tems-oriented approachto describe the dynamic processes

within and between biological cells. As we shall see later,

systems biology has more to do with the application of sys-tems and control theory to cellular systems than with the

application of physics to biology.

Systems biology provides a vital interface between cell

biology and biotechnological applications. Before we dis-

cuss this area in greater detail in subsequent sections, we

note that complexity in the context of biological systems

can be defined as

a propertyof an encoding(mathematicalmodel) (e.g.,

its dimensionality, order, or number of variables)

an attribute of the natural system under consider-

ation (e.g., the number of components, descriptive

and organizational levels that ensure its integrity)

our ability to interact with the system and to observe

it (i.e., to make measurements and generate experi-

mental data).

On all three accounts,genes,cells, tissues,organs,organ-

isms, and populations are individually and as a functional

whole a complex system. It is the availability of experimen-

tal techniques, modern microscopy, laser tweezers, and

nanotechnology, as well as DNA microarrays, gel technol-

ogy, and mass spectrometry that drives this renewed inter-

est in complexity studies and systems biology. While the

August 2003 IEEE Control Systems Magazine 39

Systems biologyapplyingsystems theory to biology

in the postgenomic era.


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technology to generate and manage data races ahead, it be-

comes apparent that methodologicaladvances in the analy-

sis of data are urgently required if wewantto turn the newly

available data into information and knowledge. This need

for research into new methodologies and the development

of novel conceptual frameworks has been neglected in the

euphoria about new technology. Problems in the post-

genomic era of the life sciences will not only be experimen-

tal or technical, but also conceptual. The interpretation of

data, turning information into knowledge, is as important

for scientific and biotechnological progress as the possibil-

ity of generating and managing data.

With the generation of vast amounts of data, computer

scientists have been the natural allies of biologists in the

management of these data. The growth of bioinformatics

parallels the excitingdevelopments in biology. However the

availabilityof genomesequencedata has ledto a focus shift

from molecular characterization and sequence analysis to

an understanding of functional activity and now interac-

tions of genes and proteins in pathways. Gene expression

and regulation, i.e., to understand the organization and dy-

namicsof genetic, signaling, andmetabolicpathways, is the

challenge for the next 50 years. The nature of the experi-ments and the data thereby generated requires an alliance

of the biological and biomedical sciences with physical sci-

entists (engineers, mathematicians, and physicists). The

following discussionon the challenges andhurdleswillclar-

ify why such an alliance is so important.

(Not) A New Kid on the BlockAlthough generallyconsidered to be a newareaof research,

systems biology is not without history, and as early as the

1960s the term was used to describe the application of sys-

tems and control theory to biology [12]. At the time,

Mesarovic wrote: In spite of the considerable interest andefforts, theapplication of systems theory in biology has not

quite lived up to expectations. ...oneof themain reasons for

the existing lag is that systems theory has not been directly

concernedwith some of theproblemsof vital importancein

biology. Today, scientists in this field are motivated by the

availability of experimental data, including, for example,

DNA microarray time series, and interdisciplinary collabo-

rations are widely supported. In fact, the importance of in-

terdisciplinary research and close collaborations between

biologists and physical scientists is evident in the many

multidisciplinary research centers that are being built

around the world, gently forcing researchers to interact by

confining them into purpose-built housing.

Mesarovic further suggested that progress could be

made by more directand stronger interactions of biologists

with systemscientists: The real advance in the application

of systems theory to biology will come about only when the

biologists start asking questionswhich are based on the system-theo-

retic concepts rather than using

these concepts to represent in still

another way the phenomena which

are already explainedin terms of bio-

physical or biochemical principles.

... then we will not have the applica-

tion of engineering principles to bio-

logical problems butrather a field of

systems biologywith its own identity and in its own right.

Molecular characterization has led to very accurate spatial

representations of cellular components, and biochemical

modeling has been the main approach to studying cellular

processes. However, the future lies in extending this knowl-

edge to observations at higher organizational levels. There

are fewexamplesof a concertedeffort to translate biologi-

cal representations of gene expression and regulation into

thelanguage of thesystem scientist[13], [14], andall indica-

tions are that the field is going to provide the vital interface

between basic cell biology, physiology, and biotechnologi-

cal applications such as in metabolic engineering.

Systems biology has new technologies available to gen-

erate data from the genome, transcriptome, proteome, and

metabolome, in addition to the physiome. However, while

bioinformatics is usually associated with vast amounts ofdata available in databases, the systems-biological descrip-

tion of cellular processes often suffers from a lack of data.

Gene Expression and RegulationEach cell of a (multicellular) organism holds the genome

with theentire genetic material, represented by a large dou-

ble-stranded DNA molecule with the famous double-helix

structure. Cells are therefore the fundamental unit of living

matter. They take up chemical substances from their envi-

ronmentandtransformthem. Thefunctionsofa cell are sub-

ject to regulation such that the cell acts and interacts in an

optimal relationship with its environment. The centraldogma of biology describes how information, stored in

DNA, is transformed into proteinsvia an intermediateprod-

uct, called RNA. Transcription is the process by which cod-

ing regions of DNA (called genes) synthesize RNA

molecules. This is followed by a process referred to as

translation, synthesizing proteins using the genetic infor-

mationin RNAas a template.Mostproteinsareenzymesand

carry out the reactions responsible for the cells metabo-

lismthe reactions that allow it to process nutrients, to

build new cellular material, to grow, and to divide.

40 IEEE Control Systems Magazine August 2003

Systems biology has more to do withthe application of systems and controltheory to cellular systems than with theapplication of physics to biology.


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Researchconductedin the1960s showedthatmost basic

cellular processes are dynamic and feedback regulated.

Whileinvestigatingregulatory proteinsand the interactions

of allosteric enzymes, Jacob and Monod introduced the dis-

tinction between structural genes (coding for proteins)

and regulatory genes, which control the rate at which

structural genes are transcribed. This control of the rate of

protein synthesis was the first indication that such pro-cesses are most appropriately viewed as dynamic systems.

Figure 1 illustrates the processes of gene ex-

pression and regulation in bacterial cells.

Although bacterial cells are capable of pro-

ducing several thousand different proteins, not

all are producedat the same time or inthe same

quantity. The energy consumption for protein

synthesisandtherelatively short half-lifeof the

RNA molecules are reasons for the cell to con-

trolboth thetypes andamountsof each protein.

One example of a global regulatory network is

the heat-shock response. When proteins are ex-

posed to extremes of heat, they are said to un-

dergo denaturation. Denaturation is the

destruction of the folding properties of a pro-

tein leading (usually) to loss of biological activ-

ity. To counteract possible toxic effects from

insoluble aggregates in the cell, the change in

temperature and the quantity of denatured pro-

teins are sensed by the cell and specific heat-

shock proteins are produced. Figure 2 illus-

trates heat-shock regulationof theDnaKoperon

in the bacterium Bacillus subtilis. The protein

DnaK is such a chaperone, one of a group of

proteins called molecular chaperones, whichhelp other proteins to fold properly. These spe-

cialist proteins produce barrel-like structures,

providing an environment for the denatured

proteins to refold. The described mechanism is

referred to as negative control through

repressor deactivation. A repressor protein is a

regulatory protein that binds to a specific site

on the DNA and thereby blocks transcription.

Drawings like Figure 1 are frequently used in

biology textbooks to illustrate structural as-

pects and the spatial organization of compo-

nents in the cell. Figure 2, on the other hand,

also shows the signal flow and temporal effectscomponents have. The next section introduces

an area in which signal- and systems-oriented

representations play an even greater role.

Intra- and IntercellularDynamics: CellularWeather ForecastingThepreviousexample described howgenes act

and interact within the context of the cell. Bio-

logical cells are not running a program but rather continu-

ally sensing their environment and making decisions on the

basis of that information. To determinehow cells act and in-

teract within the context of the organism to generate coher-

ent and functional wholes, we need to understand how

information is transferred betweenand within cells. Cellsig-

naling, or signal transduction, is the study of the mecha-

nisms that enable the transfer of biological information.Signaling impinges on all aspects of biology, from develop-


Figure 1. Gene expression and regulation in bacteria. Information, stored inthe DNA, is transformed into proteins via an intermediate product called mRNA.

The short half-life of mRNA and the energy consumption of protein synthesis form

the basis for a sophisticated hierarchy of a control mechanism.

Figure 2. Negative regulation of the dnaK and GroESL operons in Bacillussubtilis. The HrcA repressor is a regulatory protein that binds at specific sites on

DNA and blocks transcription. In the absence of stress, the GroESL co-repressor

binds withHrcAand thereby increases repressionof thednaKoperon genes.Upon

heat shock, the transcription rate of a group of heat shock proteins, called

chaperones, is increased. They build barrel-type structures, which helpdenatured

protein to refold and regain its function.


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mentto disease.Manydiseases, suchas cancer, involvemal-

function of signal transduction pathways. Downward [15]

provided an excellent account of this field.

Figure 3 illustrates a very basic signaling model. As indi-

cated in the previoussection, bacteria regulatecellmetabo-

lism in response to a wide variety of environmental

fluctuations, including the heat-shock example above.

Thus, there must be mechanisms by which the cells receive

signals from the environment and transmit them to the spe-

cific targetto be regulated. Receptorsare proteinsthat span

the membrane, with a site for binding the signaling com-

pound on the outer surface. Binding of the extracellular sig-

naling compound to the outer surface of the receptor

results in an activation of an intracellular protein (the re-sponse regulator), forexample,by phosphorylation. Signal

transduction pathways commonly consist of many more

cascaded modules between receptor and genome. There

can be numerous intermediate steps before the signal

transduction process ends, often with a change in the gene

expression program of the cell. In the figure, the phos-

phorylated response regulator is a DNA binding protein,

which serves as a repressor, preventing the RNA polymer-

ase from transcribing the adjacent gene(s).

In addition to crosstalk between pathways, negative

feedback systems canoccur, and thetime courseof a signal

transduction pathway can be critical. It is therefore impor-

tant to develop experimental techniques that allow quanti-

tative measurements of proteins and protein interactions.

Mathematical modeling and simulation in this field has the

purpose to help and guide the biologist in designing experi-

ments andgenerally to establisha conceptual framework in

which to think. The article by Hasty [16] provides a survey

of such in numero molecularbiology (see also [17]).Smolen

[18]surveysmathematical modeling of transcriptional con-

trol and future directions. The signal-oriented approach to

cellular models by Kremling et al. [13] is an example of sys-

tems and control theory bridging the gap between cellular

biology and metabolic engineering. Progressing from

merely descriptive models to predictive models will require

the integration of data analysis and mathematical modeling

with information stored in biological

databases. The data we currently have

available do not allow parametric sys-

temsidentification techniquesto build

predictive models. Instead, it is the

systems thinking, the modeling pro-

cess itself, that often proves useful.

Example: Modeling ofRas/Raf-1/MEK/ERK

Signal TransductionPathwayThe Ras/Raf-1/MEK/ERK module in

Figure 4 is a ubiquitously expressed

signaling pathway that conveys mito-

genic and differentiation signals from

the cell membrane to the nucleus

[19]-[22]. This kinase cascade appears

to be spatially organized in a signaling

complex nucleated by Ras proteins.

ThesmallG protein Rasis activatedby


EnvironmentalSignal Cell Surface Receptor

(Sensor Kinase)

IntracellularProtein

Cell Membrane

Phosphatase

ResponseRegulator

PhosphorylatedProtein

RNAPolymerase

Promoter Operator Gene(s)

P

P

DNA

Figure 3. Cell signaling (signal transduction). Intracellulardynamics (geneexpression)can be affected by extracellular signals.

Receptors spanning the cell membrane receive signals and transmit

the information to activate intracellular proteins (the response

regulator). In the figure, theresponse regulatorbinds to theoperator

region of a gene and prevents the RNA polymerase from

transcription of the adjacent gene. A phosphatase ensures that theprocess is continuous.

Figure 4. Biologistsdrawingfor theRas/Raf-1/MEK/ERKsignaltransductionpathway.


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many growth factor receptors and binds to the Raf-1 kinase

with high affinity when activated. This induces the recruit-

ment of Raf-1 from the cytosol to the cell membrane. Acti-

vated Raf-1 then phosphorylates and activates MAPK/ERK

kinase(MEK), a kinasethat in turn phosphorylates andacti-

vates extracellularsignal regulated kinase (ERK), the proto-

typic mitogen-activated protein kinase (MAPK). Activated

ERKs can translocate to the nucleus and regulate gene ex-pression by the phosphorylation of transcription factors.

This kinase cascade controls the proliferation and differen-

tiation of different cell types. The specific biological effects

arecrucially dependenton theamplitudeandhistoryof ERK

activity. The adjustment of these parameters involves the

regulation of protein interactions within this pathway and

motivates a systems biological study. Figures 5 and 6 de-

scribe the circuit diagrams of the biokinetic reactions for

which a mathematical model is used to simulate the influ-

ence of ligand variations on the pathway

S E SE E P k

k

k+

+

1

2

3 .

Signal transduction pathways can be represented as se-

quences of enzyme kinetics reactions which turn a sub-

strate S into a product P via an intermediate complex SE

and regulated by an enzyme E. The rate by which the en-

zyme- substrate complexSE is formed is denoted by k1. The

complexSE holds twopossibleoutcomesin thenext step. It

can be dissociated into E and S with a rate constant k2 or it

canfurther proceed to form a productPwith a rate constant

k3. It is required to express the relations between the rate of

catalysisandthechangeof concentration for thesubstrate,

theenzyme, thecomplex,and theproduct.Based on this re-

action kinetics[23], wefirstconsidera basicmodeling block


Figure 5. Basic pathway modeling block. The pathway model isconstructed from basic reaction modules like this enzyme kinetic

reaction for which a set of four differential equations is required.

Figure 6. Graphical representation of the Ras/Raf-1/MEK/ERK signal transduction pathway (the shadowed part represents thesuppression by RKIP): a circle represents a state for the concentration of a protein and a bar a kinetic parameter of the reaction to be

estimated. The directed arc (arrows) connecting a circle and a bar represents a direction of a signal flow. The bidirectional thick arrows

represent anassociationand a dissociationrateat thesame time. Thethin unidirectionalarrows represent a productionrateofproducts.


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of signal transduction pathways. This basic modeling block

is illustrated in Figure 5 and can be described by the follow-

ing set of nonlinear ordinary differential equations:

dm t

dtk m t m t k m t

dm t

dt

k m t m

11 1 2 2 3

21 1 2

( )( ) ( ) ( )

( )( ) (

= +

= t k m t k m t

dm t

dtk m t m t k m t k

) ( ) ( )

( )( ) ( ) ( )

+ +

=

2 3 3 3

31 1 2 2 3 3m t

dm t

dtk m t

3

43 3

( )

( )( ).=

From these we have

m t m t C

m t m t m t C

2 3 1

1 3 4 2

( ) ( ) ,

( ) ( ) ( ) .

+ =

+ + =

Hence we can describe the basic reaction module by two

nonlinear equations subject to two algebraic conditions.

In general, for a given signal transduction system, the

whole pathway can be modeled by a set of nonlinear dif-

ferentialequationsand a setof algebraicconditions in the

following form:

d t

dtt t

m t Cii p

j

mf m k

( )( ( ), ( )),

( ) ,{ , }

=

=

1

where m( ) [ ( ), ( ), , ( )]t m t m t m t p= 1 2 K , k( ) [ ( ), ( ), ,t k t k t = 1 2 K

k tq ( )], p is thenumber ofproteinsinvolved inthe pathway,q

is the required number of parameters, and j J{ , , }1K with

the number of algebraic conditions J p< .

Parameterestimation is widely regarded as a majorprob-

lem in dynamic pathway modeling [25], [26]. A simple

method firstdiscretizes the nonlinear differential equations

into algebraic difference equations that are linear with re-


Figure 7. Illustration of parameter estimation from time series data: Each parameter is determined from the value to which the estimatesconverge (shown by the horizontal line). (Note that any experimental noise can be further eliminated by regression techniques if multiple

experimental replicates at each time point are available.)


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spect to theparameters andthen solve the transformed lin-

ear algebraic difference equations to obtain the parameter

values at each sampling time point. We can then estimate

the required parameter values by employing curve fitting,

calculation of steady state values, and regression tech-

niques. Forthis purpose of parameterestimation, theprevi-

ous equations are transformed into

k g mm

( ) ( ),( )

t td t

dt=

,

and this can be further transformed into a set of algebraic

difference equationsby approximatingthe differential oper-

ator vector g via a difference operator vector h as

k h m m m( ) ( ( ), ( ), , ( ))t t t t r 1 K

where r depends on the order of approximation. Without

lossof generality, k( )t canbe approximated by k since most

of the signal transduction systems can be regarded as

slowly time varying systems compared with the measure-

ment windows in time scale. Hence we have

k h m m m ( ( ), ( ), , ( ))t t t r 1 K ,

which implies the parameter estimates based on time

course measurements.

The entire model, as shown in Figure 6, is constructed in

this way, leading to what usually becomes a relatively large

set of differential equations for which parameter values

have to be identified. As illustrated inFigure 7, in theestima-

tion ofparameters fromwestern blot data, theparameteres-

timates usually appearas a time dependentprofilesince the

time course data include various uncertainties. However,

since the signal transduction system itself can be consid-

ered as time invariant, the estimated parameter profile

shouldconverge to a constant valueat steadystate. Figure 7

illustrates this estimation procedure.


Figure 8. Thesimulationresults forfixed initial conditions: (a)shows the bindingof RKIP to Raf-1*,(b) shows thebindingof MEK-PPtoERK-P, (c) shows the binding of ERK-PP to Raf-1*/RKIP, and (d) shows the binding of RP to RKIP-P.


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If a reasonable model is constructed, this can then be

used in a variety of ways to validate and generate hypothe-

ses, or to help experimental design [22], [24]. Based on the

mathematical model illustrated in Figure 6 and the esti-

mated parameter values as, for example, obtained using a

discretization of the nonlinear ordinary differential equa-

tions (as illustrated in Figure 7), we can perform simulation

studies to validate the signal transduction mechanism as il-lustrated in Figure 5 and also to analyze the signal

transduction system with respect to the sensitivity for the

ligand (via simulation of variable initial conditions) as illus-

trated in Figure 9.

Current Research:An International PerspectiveThe interest in systems biology is documented by the in-

creasing number of conferences, research groups, and insti-

tutes dedicatedto thisarea. Funding initiatives in theUnited

States, Japan, South Korea, and Germany provide new op-

portunities to the engineering sciences. Systems biologyprovidesevidenceof the growinginvolvement of control en-

gineers, notjustat thetechnological level,but also playing a

vital role in the development of novel methodological ap-

proaches in mathematical modeling, simulation, and data

analysis [27]. To allow large scalesimulations, international

projectsaredevelopinga systems biology markup language

(SBML) [28] and Systems Biology Workbench to allow the

integration of models and simulation tools (visit

http://www.sbw-sbml.org for further information).

The principal challenge for the biomedical sciences is to

answer the following questions [6]:

How do cells actandinteract withinthe context of the

organism to generatecoherent andfunctional wholes?

How do genes act and interact within the context of

the cell to bring about structure and function?

Forsystems biology we cansummarize thechallenges as

follows:

methodologies for parameter estimation

experimental and formal methods for model valida-

tion

identification of causal relationships, feedback, and

circularity from experimental data

modular representationsand simulation of large scaledynamic systems

investigations into the stabilityand robustness of cel-

lular systems


Figure 9. The simulation results for variable initial conditions: (a) shows the variation of Raf-1*, (b) shows the variation of ERK, (c)shows the variation of RKIP, and (d) shows the variation of RKIP-P.


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visualization and fusion of information, integration of

models and simulators

scalingmodels acrosstimescales anddescription lev-

els (from genes, transcripts, and proteins to cells and

organisms).

Gene expression takes place within the context of a cell

and between cel ls , organs, and organisms. The

reductionist approach is to isolate a system, conceptu-

ally closingit off from its environment through the defini-

tionof inputs and outputs. We inevitably loose information

using this approach since conceptual closure amounts to

the assumption of constancy for the external factors and

the fact that external forces are described as a function of

something inside the system. Different levels of cellular

systems may require different modeling strategies, and ul-

timately we require a common conceptual framework that

integrates different models, forexample,differential (mass

actionor rate) equations providea realistic modelingpara-

digm for a single-gene or single-cell representation, but

cell-to-cell and large-scale gene interaction networkscould, for example, be represented by finite-state models

or using agent-based simulation.

One should not jump to the conclusion, however, that

systems and control theory could provide all answers to

the challenges given by dynamic systems in molecular bi-

ology. In dynamic systems theory, one can often ignore

spatial aspects. However, time andspace are essential for

explaining the physical reality of gene expression. The

same component of a pathway may have a different func-

tional role depending on its location within the cell. Al-

though components of cells have specific locations, these

locations lack exact coordinates. Not only signals are being

transmitted but components also move around in a non-random fashion. Without spatial entailment there canbe no

livingcell,andfor systems biology it is thus necessary to in-

tegrate a topological representation of this organization

with models of the dynamic behavior.

The biologist Frank Harold presented an excellent dis-

cussion of the complexity of cellular processes and pro-

vided a compelling argument for the need for more

research in complexity studies: From the chemistry of

macromolecules and the reactions that they catalyze, lit-

tle can be inferred regarding their articulation into physi-

ological functions at the cellular level, and nothing

whatever can be said regarding the form of developmentof these cells. It therefore seems to me self-evident that

the quest for the nature of life cannot be conducted exclu-

sively on the biochemists horizon. We must also inquire

how molecules are organized into larger structures, how

direction and function and form arise, and how parts are

integrated into wholes [9].

In general, causation is a principle of explanation of

change in the realm of matter. In systems biology causation

is defined as a (mathematical) relationship, not between

materialobjects(molecules),butbetweenchanges of states

within and between elements of a system. Instead of trying

to identify genes as causalagents forsome function,role,or

change in phenotype we relate these observations to se-

quences of events. In other words, instead of looking for a

gene that is the reason, explanation, or cause of some phe-

nomenon we seek an explanation in the dynamics (se-

quences of events ordered by time) that led to it. It is

systems dynamics, not a genetic program that gives rise tobiological forms and function [9, p. 199].

In analyzing experimental data, we usually rely on as-

sumptions made about the ensemble of samples. A statisti-

cal or average perspective, however, may hide short-term

effects that are thecausefor a whole sequence of eventsin a

genetic pathway. What in statistical terms is considered an

outlier may just be the phenomenon the biologist is looking

for. It is verydifficult to obtainsufficiently large andreliable

data sets for pathway modeling; it is therefore important to

compare different methodologies, their implicit assump-

tions, and the consequences of the biological questions

asked.To allow reasoning in thepresenceof uncertainty, wehave to be precise about uncertainty, and if we cannot be

precise about uncertainty, modeling (generating hypothe-

ses), and model validation (for hypothesis testing) become

complementary aspects of an iterative process. It is thus of

paramount importance that we strive to bridge the gap be-

tween data and models. In the words of Bertalanffy: Thus

even supposedly unadulterated facts of observation al-

ready are interfused with all sorts of conceptual pictures,

model concepts, theories, or whatever expression you

choose. The choice is not whether to remain in the field of

data or to theorize; the choice is only between models that

are more or less abstract, generalized, near or more remote

from direct observation, more or less suitable to representobserved phenomena [29].

ConclusionsSystems biology marks a shift away from an often obses-

sively reductionist (molecular) approach to providing a

causal and dynamic account of cellular form and function.

The biggest if not the principal hurdle for the systems ap-

proach is Zadehs uncertainty principle, which states

that as the complexity of a system increases, our ability to

make precise and yet significant statements about its be-

havior diminishes until a threshold is reached beyond

which precision and significance (or relevance) become al-most exclusive characteristics.

The cell is a self-controlled andself-regulatingdynamicsys-

tem consisting of components that are interacting in space

and time. The relationships that prevail between structure,

function, and regulation in cellular networks are still largely

unknown. Systems biology aims to identify and explain these

relationships through an integrated effort of both experimen-

tal and theoretical methodologies. For this we require scien-

tists who are preparedto investtimeand effort intomore than

one discipline and scientific culture. This will necessitate a



11/11

change in the education, training, and career prospects of in-

terdisciplinaryscientists. Forthose whopersist, the rewardis

a better understanding of life itself.

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Olaf Wolkenhauerreceived the Dipl.-Ing.and BEng. degree

incontrol engineering in 1994 fromtheUniversity ofApplied

Sciences, Hamburg, Germany and the University of

Portsmouth, U.K., respectively. He received the Ph.D. from

the University of Manchester Institute of Science and Tech-

nology (UMIST). From 1997 to 2002 he held a research lec-

tureship at the Control Systems Centre, UMIST, and since

2002 he has held a joint senior lectureship between the De-

partment of Biomolecular Sciences and the Department of

Electrical Engineering and Electronics. In 1999 and 2000 he

was a visiting research fellow at Delft University of Technol-ogy, The Netherlands. He has authored two books, Possibil-

ity Theory with Applications to Data Analysis (RSP, 1998) and

Data Engineering(Wiley, 2001). Hisresearch interestis in the

application of systems andcontrolmethodologiesto molec-

ular and cell biology. Since July 2003 he has held the chair in

Bioinformatics and Systems Biology at the University of

Rostock. He can be contacted at the Department of

Biomolecular Sciences and Department of Electrical Engi-

neeringandElectronics,ControlSystems Centre,University

of Manchester Instituteof ScienceandTechnology , P.O. Box

88,Manchester M601QD,U.K.,[email protected].

Hiroaki Kitano is a director at Sony Computer Science

Laboratories, Inc., director of ERATOKitano Symbiotic Sys-

tems Project, JST, and president of The Systems BiologyIn-

stitute. He received his B.A. in physics from International

Christian University, Tokyo, in 1984 and a Ph.D. in com-

puter science from Kyoto University in 1991. He received

theComputersand ThoughtAwardin 1993 and thePrixArs

Electronica in 2000.

Kwang-Hyun Cho receivedhisB.S., M.S., andPh.D. degrees

in electrical engineering from the Korea Advanced Institute

of Science and Technology in 1993, 1995, and 1998, respec-

tively. He joined the School of Electrical Engineering at theUniversity of Ulsan,Korea, in 1999 as an assistantprofessor.

From 2002 to 2003, he was a research fellow at the Control

Systems Centre, Department of Electrical Engineering and

Electronics at the University of Manchester Institute of Sci-

ence and Technology, U.K. His research interests cover the

areas of systems science and control engineering including

analysis andsupervisory control of discreteevent systems,

nonlinear dynamics, hybrid systems, and applications to

complex systems suchas communicationnetworksand bio-

logical systems.


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