In his visionary commentary, Glen Evans1

describes the emergence of new post-genomic disciplines like proteomics, epige-nomics, and phenomics. He proposes thatintegrated “omic” databases in the futurewill allow the in silico re-enacting of theentire organism once the totality of theinformation at the various levels, fromgenome to proteome and functions, isavailable. This is an attractive idea: justthink of virtual organisms that could servefor virtual clinical trials. However, the“omic” strategy is based on the same linear,monocausal, deterministic thinking thathas reigned in molecular biology over thepast decades2; it just extrapolates it to theentire genome. This is brute-force, geno-centric reductionism in the guise of entire-ness, rather than a novel integrativeapproach devoted to wholeness, or as NatureBiotechnology put it in an editorial:“Complicated is not complex.”3

In fact, the present efforts in the drugdevelopment industry to simulate organis-mal function still ignore the very elemen-tary parts, and instead employ the tradi-tional “top-down” modeling approach ofphysiologists and systems engineers, inwhich organs and cells are black boxes.Such approaches vastly dominated a recentworkshop on computer modeling in biolo-gy, despite the promising conference title:“From gene to organ.”* With their dream ofsimulating organisms “from bottom-up,”genomics scientists touch upon the old rid-dle of genome-to-phenome mapping. Theywill have to overcome the mentality of datacollecting, clustering, and classification,and develop a qualitatively new conceptualunderstanding of the complexity of livingorganisms. Help in tackling this dauntingtask is already on its way: not only are phys-ical scientists increasingly interested instudying complex systems in general, butone of the first institutions devoted to suchstudies, the New England Complex SystemsInstitute (NECSI) in Boston, MA, haslaunched collaborations with molecularbiologists to harness the data flood trig-gered by the success of genomic technology.

To counter, but not stifle Evans’ enthusi-asm, let me give an overview of some of the

major obstacles and challenges that we willhave to confront in a post-genomic endeav-or that will ultimately lead to the design ofcyber-guinea pigs based on our genomicdatabases:

Ontological questions concerning data-base structure and representation of “genefunction”. A major goal of making genomedatabases more user-friendly is the system-atic annotation of genes with functionalproperties, such as the biochemical func-tion, interaction partners, and physiologi-cal role. This has worked pretty well forsimple organisms, as demonstrated in theSaccharomyces Genome Database4.However, in more complex organisms, thesame task represents an ontological chal-lenge, as most proteins are embedded in aregulatory network and don’t just code formetabolic enzymes. For instance, genessuch as ras, myc, rho, and NF-κB eitherstimulate growth and survival, or theyinduce apoptosis. The “function” of a geneproduct appears to depend on its “cellularcontext.”

This raises fundamental questions. Forexample, what is a “gene function?” What isits “context?” And what is the modular enti-ty to which the concept of context applies: aprotein domain, a single protein, a stablecomplex, or a whole pathway? Recently, theprinciple of “modularity” in cellularprocesses has been proposed to facilitate thetask of dealing with biological functions5.For example, a specific signal transductionpathway could be viewed as a functionalmodule. However, it remains to be seenwhether modules are “natural kinds” or justlogical constructs of our mind and howtheir boundaries have to be delineatedgiven the extensive “crosstalk” between thehistorically defined pathways. These ques-tions must be addressed first before usefulhigher level “omic” databases can be con-structed.

Limitations of computational power. Inproposing the in silico re-enacting oforganismal function, Evans seems to ignorethe immensity of computational capacityrequired. Take the human genome with100,000 genes and let every gene be simplyeither “on” (expressed) or “off ” (silent).This minimal, idealized, and discrete set-ting alone would lead to the astronomicalnumber of 1030,000 possible gene expressionprofiles! The computing and testing of allthese patterns with the existing serial com-puters would take more time than the age of

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the universe. The cell, in contrast, in whichzillions of molecular events occur at a time,computes in a parallel fashion. Moreover,the cell has evolved a wiring architecture forgenomic interactions that enables it tospontaneously choose stable, physiologicalexpression profiles6,7.

Even with the envisaged knowledge ofthe genomic wiring architecture and possi-ble novel algorithms that could facilitateour computing approaches, many genericproblems in genomic-scale computation,such as analysis of genome-wide interactionmaps, are thought to belong to the class ofproblems known as ”hard NP” to computerscientists. On traditional serial computerssuch problems can have computationalrunning times that grow exponentially withthe number of variables. Feeding “omic”databases into computers and runningthem to simulate life would require parallelsupercomputers. However, in contrast totraditional computers, parallel computersmust be built to the specifications of thegiven task, which for simulating phenomesout of genomes still awaits formalization.With the need for ever-increasing simula-tion capacity, biology will join the physicalsciences as a discipline where simulationcapacity is a notorious rate-limiting factor.Ironically, in an opposite development,computer scientists facing the limits oftheir machines have started to explore thepossibility of moving from in silico to invitro computing to exploit the inherentparallelism of mixtures of specific DNAmolecules8.

Lack of formal understanding of thedesign principle of large biochemical net-works. Unexpected results in metabolicengineering have taught us some illuminat-ing lessons. For instance, an Escherichia colistrain with a mutated pyruvate kinase geneshows the same central carbon flux ratio asthe parental wild-type strain, and knockoutmice lacking a critical gene that fail toexhibit the expected phenotype are no sen-sation anymore. These examples point to an“emergent property” of biochemical net-works at a higher level of integration thatdefies traditional genetic determinism; theyillustrate our inability to predict suchaggregate behavior from the knowledge ofthe individual pathways.

Several attractive concepts have beenproposed in the past for understanding theintegrated behavior of metabolic networksand predicting the outcome of metabolic

COMMENTARY

COMPLEXITY

The practical problems of post-genomic biology

Sui Huang

Sui Huang is research fellow at thedepartment of surgery, Children’s Hospitaland Harvard Medical School, Boston, MA02115 (huang_su@a1.tch.harvard.edu).

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COMMENTARY

engineering9–11. However, we are still farfrom a unifying, formal understanding ofthe link between functional macroscopicproperties and the architecture of large reg-ulatory networks of intertwined, nonlinear,stochastic interactions exhibiting quasi-sta-tionary states far from thermodynamicequilibrium. Analytical approaches to smallbiochemical networks using mathematicaltools for describing nonlinear dynamic sys-tems has brought some insights into designprinciples of biochemical circuitries12.Importantly, they have addressed genericphenomena such as feedback regulation,hysteresis, multistability, robustness, whichwere counterintuitive to the biologist usedto thinking within the linear framework ofthe central dogma of molecular biology.However, it remains unclear whether livingorganisms with larger networks of interact-ing parts can be reduced to explicit differ-ential equations. Recent studies combiningcomputer modeling with recombinantDNA technology approaches to real, well-documented, small networks, such as theone involved in bacterial chemotaxis, areillustrative examples of the depth of under-standing to be targeted in the near future13.The extension of such approaches to largernetworks of higher organisms will have toincorporate multiple layers of interactions,including those establ;ished by physical,humora and neutral intercellular commu-nications. This will be the next big chal-lenge.

Uncharacterized consequences of a dis-tinct physicochemical reality at the microdi-mensions of the cell. Most quantitative mod-eling approaches rely on our chemical intu-ition gained from the study of ideal, dilute,homogeneous solutions where the laws ofmass action allow the simple formalizationof molecular reactions. The fact that condi-tions in the cell are far from ideal has manyimportant kinetic and thermodynamicconsequences. First, the small number ofindividual molecules, ranging from two(specific promoter elements) to a few thou-sand (regulatory proteins), precludes theuse of concepts like concentrations or equi-librium constants. Such characteristic vari-ables are derived from the statisticalmechanics treatment of large numbers ofmolecules. Second, the local crowding ofmacromolecules in the cell affects struc-ture, function, and diffusion of biopoly-mers14. Third, cellular biochemistry, such assignal transduction cascades, occurs not infree three-dimensional space, but on thesurface of membranes and cytoskeletalstructures. This leads to fractal and non-Michaelis-Menten kinetics15. Finally, thesmall number of specific reaction partnersgives rise to relatively large, random fluctu-ations16.

It is still debated whether and when thisnoise is a disturbance (necessitating thedesign of robust regulatory networks) or iseven exploited by the organism. For exam-ple, randomness can be used to generatevariability in pattern formation, for thenoise-driven transport of macromoleculesbased on a ratchet mechanism or for signalprocessing based on stochastic resonance17.Taken together, these microscale reaction

conditions within the cell preclude a mod-eling approach that relies on traditionalchemokinetics using “macrovariables” suchas concentrations and rate constants. Thedevelopment of novel visualization toolsand the advent of cellular nanotechnologywill pave the way toward an understandingof the real “device physics” of a cell, whichin turn will provide access to the relevant“microvariables” for a precise bottom-upmodeling.

Structural and mechanical complexity ofcells. Genomic science is concerned withthe abstract level of information storageand processing in cells while neglectingtheir mechanical structure. Borrowingterms from the world of computers,genomics and its “omic” grandchildren canbe said to solely deal with the software, andto neglect the hardware of living systems.Yet the organism is more than an extendedbiochemical network obeying logical rulesand chemical laws. Genomics scientistsshould be reminded that the organism isalso a physical entity with geometricdimensions and thus is subjected to thelaws of macroscopic mechanics.Understanding gene function must includeknowledge about the hardware aspect. Cellsare compartmentalized, and localization ofproteins affects their function. Informationtransfer takes place not only through thespecificity of protein binding. The cellresponds to topological clues and mechani-cal forces that play a central role duringmorphogenesis and yet do not encodegenetic information. For example, withinthe same biochemical milieu, a normalmammalian cell will divide, differentiate,or apoptose depending just on its externally

imposed shape18. With the advent of micro-fabrication in cell biology, bioengineers aredesigning powerful tools, like the micro-electromechanic systems (MEMS), whichwill become available for biologists whowish to study the microscopic hardware ofthe living organism.

Biology and the new science of complexsystems. The fundamental question of howsimple parts (self-) assemble into a com-plex whole with novel properties is attract-ing an increasing number of physical scien-tists who are now consolidating the newscience of complexity. As physicist andNobel Prize laureate Phil Andersonacknowledged, psychology is not appliedbiology is not applied chemistry is notapplied physics. At each level, novel lawscan be found whose necessary, separatestudy has defined each discipline. Featuresof the very same system depend on thescale of observation. This precludes theextrapolation of knowledge at one level tohigher levels where the “complexity”increases (and vice versa). Understandingwhy this is so, and determining how to for-malize the problem of emergent featuresand multiscale description is one of thegoals of the science of complex systems.Biology, whose object of study extendswithin one same entity across many scales,from molecule to animated organism,should thankfully embrace the efforts andwatch their progress carefully19,20.

*“From Gene to Organ,” February 23–27, 2000,Georgia Institute of Technology, Hilton Head, SC.

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A.W. Nature 402 (Suppl.), C47-C52 (1999).6. Kauffman, S.A. The origins of order. (Oxford

University Press, New York, NY; 1993).7. Huang, S. J. Mol. Med. 77, 469-480 (1999).8. Chen. J. & Wood, D.H. Proc. Natl. Acad. Sci. USA

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Features of the very samesystem depend on thescale of observation. This precludes the extrapolation of knowledge at one level tohigher levels where the“complexity” increases.

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