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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

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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

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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.)


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,

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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 (213,500).

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and by theoretical arguments that proteinsare difficult to evolve once they are partici-pating in many different interactions5.

Understanding the relatedness of mod-ules is useful because knowledge about onemember of a class can inform the study of theothers. Relatedness by descent is oftenapparent from the homology of chemicalcomponents. For instance, the mitogen-activated protein kinase cascades that occurin many intracellular signalling pathwaysdefine a common functional class of signaltransduction modules. Modules may also berelated by shared design or functional principles, even if they are not related bydescent. The pheromone-detection systemof budding yeast and the chemotacticmachinery of bacteria use unrelated compo-nents, but both pathways achieve a sensitiveresponse over a wide range of pheromone orchemoattractant concentrations by usingreactions that specifically turn off activeforms of the signalling receptors6–8.

Lessons from other sciencesWe have argued that most functional proper-ties of a module are collective properties,arising from the properties of the underlyingcomponents and their interactions. Collec-tive properties have long been studied in sta-tistical physics and share attributes that riseabove the details9. For example, the meltingof the surface of a solid can be induced in dif-ferent ways: by changing the pressure or tem-perature or by adding impurities. Similarly,different organisms induce the transitionbetween different patterns of microtubuleorganization that occurs during cell divisionby changing different members of the set ofkinetic parameters that govern microtubulepolymerization10,11. The concept of phase (orstate) transitions from physics may helpunify different observations and experi-ments. Moreover, many molecular detailsare simply not needed to describe phenom-ena on the desired functional level.

Biological systems are very different fromthe physical or chemical systems analysed bystatistical mechanics or hydrodynamics. Statistical mechanics typically deals with systems containing many copies of a fewinteracting components, whereas cells con-tain from millions to a few copies of each ofthousands of different components, eachwith very specific interactions. In addition,the components of physical systems are oftensimple entities, whereas in biology each ofthe components is often a microscopicdevice in itself, able to transduce energy andwork far from equilibrium12. As a result, themicroscopic description of the biologicalsystem is inevitably more lengthy than that ofa physical system, and must remain so, unlessone moves to a higher level of analysis.

Information flows bidirectionallybetween different levels of biological organi-zation. For instance, the macroscopic signals

that a cell receives from its environment caninfluence which genes it expresses — andthus which proteins it contains at any giventime — or even the rate of mutation of itsDNA13, which could lead to changes in themolecular structures of the proteins. This isin contrast to physical systems where, typical-ly, macroscopic perturbations or higher-levelstructures do not modify the structure of themolecular components. For example, theexistence of vortices in a fluid, althoughdetermined by the dynamics of molecules,does not usually change the nature of the con-stituents and their molecular interactions.

More importantly, what really distin-guishes biology from physics are survivaland reproduction, and the concomitantnotion of function. Therefore, in our opin-ion, the most effective language to describefunctional modules and their interactionswill be derived from the synthetic sciences,such as computer science or engineering, inwhich function appears naturally.

The essence of computational science isthe capacity to engineer circuits that trans-form information from one form into

another on the basis of a set of rules. Howmight the lessons learned here apply to biol-ogy? Evolution selects those members of agenetically diverse population whosedescendants proliferate rapidly and surviveover many generations. One way of ensuringlong-term survival is to use informationabout the current environment to predictpossible future environments and generateresponses that maximize the chance of survival and reproduction. This process is a computation, in which the inputs areenvironmental measurements, the outputsare signals that modulate behaviour, and therules generate the outputs from the environ-mental inputs. For example, signals from theenvironment entrain circadian biologicalclocks to produce responses to predictedfluctuations in light intensity and tempera-ture. Indeed, the history of life can bedescribed as the evolution of systems thatmanipulate one set of symbols representinginputs into another set of symbols that repre-sent outputs14.

Just as electrical engineers design circuitsto perform specific functions, modules have

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Building a computer in the 1950s relied on understanding barium oxide, the material of choice for emittingelectrons from the cathodes of vacuum tubes. A vacuum-tube module could then be designed whosefunction was the amplification of a signal, but whose functional description had no reference to barium oxide.These amplifiers were assembled into logical circuits, whose logical operation could be described withoutreference to vacuum tubes. The connecting wires in these logical circuits had insulation of many colours sothat the circuits could be accurately hand-manufactured. Mathematical programs were then designed on thebasis of these logic circuits. In the computer of today, there is no barium oxide or coloured wires. Instead, theproperties of silicon and silicon dioxide are of primary importance in designing an amplifier, and the transistorreplaces the vacuum tube. Both old and new computers have logic circuits based on the same elementaryprinciples, but arranged rather differently as computers have become more sophisticated. Yet all this isunimportant to most users, whose computer program runs on either machine. At one level, barium oxide andcoloured wires were the soul of the old machine, while at another level, they are irrelevant to understandingthe essence of how a computer functions.

Box 3From atoms to modules in computers

An early stored-program computer (left), built around 1950, used vacuum tubes in logic circuits,whereas modern computers use transistors and silicon wafers (right), but both are based on thesame principles.

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evolved to perform biological functions. Theproperties of a module’s components andmolecular connections between them areanalogous to the circuit diagram of an elec-trical device. As biologists we often try todeduce the circuitry of modules by listingtheir component parts and determining howchanging the input of the module affects itsoutput15. This reverse engineering isextremely difficult. Although an electricalengineer could design many different circuits that would amplify signals, he wouldfind it difficult to deduce the circuit diagramof an unknown amplifier by correlating itsoutputs with its inputs. It is thus unlikely thatwe can deduce the circuity or a higher-leveldescription of a module solely from genome-wide information about gene expression and physical interactions between proteins.Solving this problem is likely to require additional types of information and findinggeneral principles that govern the structureand function of modules.

A number of the design principles of biological systems are familiar to engineers.Positive feedback loops can drive rapid tran-sitions between two different stable states of asystem, and negative feedback loops canmaintain an output parameter within anarrow range, despite widely fluctuatinginput. Coincidence detection systems requiretwo or more events to occur simultaneously

in order to activate an output. Amplifiers arebuilt to minimize noise relative to signal, forinstance by choosing appropriate time constants for the circuits. Parallel circuits(fail-safe systems) allow an electronic deviceto survive failures in one of the circuits.

Designs such as these are common inbiology. For example, one set of positivefeedback loops drives cells rapidly into mito-sis, and another makes the exit from mitosis arapid and irreversible event16. Negative feed-back in bacterial chemotaxis allows the sen-sory system to detect subtle variations in aninput signal whose absolute size can vary byseveral orders of magnitude17. Coincidencedetection lies at the heart of much of the control of gene transcription in eukaryotes,in which the promoters that regulate genetranscription must commonly be occupiedby several different protein transcriptionfactors before a messenger RNA can be pro-duced. Signal transduction systems would beexpected to have their characteristic rateconstants set so as to reject chance fluctua-tions, or noise, in the input signal. DNAreplication involves a fail-safe system of errorcorrection, with proofreading by the DNApolymerase backed up by a mismatch repairprocess that removes incorrect bases after thepolymerase has moved on. A failure in eitherprocess still allows cells to make viable proge-ny, but simultaneous failure of both is lethal.

In both biological and man-made systems,reducing the frequency of failure oftenrequires an enormous increase in the com-plexity of circuits. Reducing the frequency atwhich individual cells give rise to cancer toabout 10–15 has required human cells toevolve multiple systems for preventingmutations that could generate cancer cells,and for killing cells that have an increasedtendency to proliferate.

Biological systems can both resist andexploit random fluctuations, or noise. Thus,evolutionary adaptation depends on DNAbeing mutable, but because most mutationsare neutral or deleterious, the rate of muta-tion is under rigorous genetic control. Manysystems for specifying the polarity of cells orgroups of cells rely on a mechanism known as‘lateral inhibition’, which causes adjacentcells to follow different fates. This process canamplify a small, often stochastic, initialasymmetry causing adjacent cells or adjacentareas within cells to follow different fates.

Other aspects of functional modules areless familiar to engineers. Several can be subsumed under the idea that the rules for amodule’s function are rigidly encoded in thestructures of its proteins, but produce messy,probabilistic intermediates that are thenrefined to give unique solutions. This princi-ple seems to hold across an enormous range of scales, from the folding of protein

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molecules to the evolution of organisms. Theprinciple arises from a combination of threemechanisms: exploration with selection(trial and error), error-correction mecha-nisms, and error-detection modules thatdelay subsequent events until a process hasbeen successfully completed. These are pre-sent to different extents in different examples.

Exploration with selection (trial anderror) is a fundamental principle of biologyand acts on timescales from milliseconds toaeons and at organizational levels from sin-gle molecules to populations of organisms.In single molecules, kinetic funnels directdifferent molecules of the same proteinthrough multiple, different paths from thedenatured state to a unique folded struc-ture18. Within cells, the shape of the mitoticspindle is partly due to selective stabilizationof microtubules whose ends are close to achromosome19. At the organismal level, thepatterning of the nervous system is refinedby the death of nerve cells and the decay ofsynapses that fail to connect to an appropri-ate target. Within populations, differentialreproductive success alters the structure ofgene pools, giving rise to evolution.

The use of exploration with selection onshort timescales as a design principle inintracellular modules may make them espe-cially easy to modify on evolutionarytimescales. The lack of a rigidly programmed

sequence of intermediates should allow suchmodules to survive incremental modifica-tions and incorporate evolutionary addi-tions such as error detection and correction.Similar messy and probabilistic intermedi-ates appear in engineering systems based onartificial neural networks — mathematicalcharacterizations of information processingthat are directly inspired by biology. A neuralnetwork can usefully describe complicateddeterministic input–output relationships,even though the intermediate calculationsthrough which it proceeds lack any obviousmeaning and their choice depends on ran-dom noise in a training process20.

Constraints from evolutionOne approach to uncovering biologicaldesign principles is to ask what constraintsthey must obey. Apart from the laws ofphysics and chemistry, most constraints arisefrom evolution, which has selected particularsolutions from a vast range of possible ones.

Today’s organisms have an unbrokenchain of ancestors stretching back to the ori-gin of life. This constraint has been success-fully used to understand protein functions,by comparing existing protein sequencesfrom related species, finding conserved partsand inferring their roles. Comparing mod-ules of common function from differentorganisms should be a similarly useful tool

for understanding their operation. One hasalso to remember that today’s modules werebuilt by tinkering with already functionalmodules, rather than by starting fromscratch, and may not be the optimal way of solving a particular problem21. This evolutionary history is similar to that ofman-made devices. Particular solutions incomputing, or for any engineered object, arethe result of an elaborate historical process ofselection by technological, economical andsociological constraints. A familiar exampleis the less than optimal QWERTY keyboard,originally invented to prevent jammed keyson early manual typewriters. It can be viewedas a living fossil.

The survival of living systems implies that the critical parameters of essential modules, such as the accuracy of chromo-some segregation or the periodicity of a circadian clock, are robust: they are insensi-tive to many environmental and genetic perturbations. Evolvability22, on the otherhand, requires that other parameters of modules are sensitive to genetic changes.They can then be modified over many gener-ations to alter the function of a module, or itsconnections to other modules, in a way thatallows organisms to adapt to new challenges.It is important to understand how robustnessand flexibility can be reconciled for eachfunctional module.

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Organisms have been selected for twoproperties: rapid reproduction in optimalconditions and the ability to survive rarelyencountered extreme conditions. Becauseenvironments tend to fluctuate over time,most modules are likely to have been selectedfor their ability to contribute to both repro-duction and survival. These considerationsimply that understanding the full function ofmodules may require us to measure smalldifferences in reproductive ability, as well asstudying the performance of modules underextreme perturbations. Some componentsof an in vivo module that are ‘nonessential’in normal laboratory conditions are likely tohave important roles in the assembly, fideli-ty, robustness and dynamic characteristics ofmodules that produce small advantages inlong-term survival probability. It may bevery difficult, however, to measure such contributions directly.

Survival of a gene pool, as opposed to anindividual organism, is favoured by diversifi-cation, as the simultaneous presence of mul-tiple phenotypes in a population increasesthe possibility that some individuals will sur-vive and reproduce in a heterogeneous andchanging environment. Diversification canbe achieved by epigenetic mechanisms thatenable a single genotype to produce morethan one phenotype, by genetic mechanismsthat maintain multiple genotypes in a popu-lation, and by speciation, which splits a singlegene pool into two independently evolvingpools. It is thus important to consider thefunction of modules not only in the contextof an organism, but also from the point ofview of the population of organisms, and toask how modular construction facilitates themaintenance and selection of diversity.

Towards modular biologyA major challenge for science in the twenty-first century is to develop an integratedunderstanding of how cells and organismssurvive and reproduce. Cell biology is intransition from a science that was preoccu-pied with assigning functions to individualproteins or genes, to one that is now trying tocope with the complex sets of molecules thatinteract to form functional modules23,24.There are several questions that we want toanswer. What are the parts of modules, howdoes their interaction produce a given func-tion, and which of their properties are robustand which are evolvable? How are modulesconstructed during evolution and how can their functions change under selectivepressure? How do connections betweenmodules change during evolution to alter thebehaviour of cells and organisms?

The number of modules that have beenanalysed in detail is very small, and each ofthese efforts has required intensive study.Biologists need to study more functions atthe modular level and develop methods thatmake it easier to determine the relationship

of inputs to outputs of modules, their biochemical connectivity, and the states ofkey intermediates within them. Three com-plementary approaches can help in this task:better methods for perturbing and monitor-ing dynamic processes in cells and organ-isms; reconstituting functional modulesfrom their constituent parts, or designingand building new ones; and new frameworksfor quantitative description and modellingof modules.

The first approach is illustrated by effortsto find small organic molecules that can per-turb and report on the activity of modules.Calcium-binding dyes have been used to fol-low the activities of neurons with high spatialand temporal resolution. Light-activatedchemicals can perturb function on thetimescales that characterize changes withinthe modules, thus giving them an importantadvantage over the slower perturbationsproduced by classical and molecular genet-ics25. Another example of a new method formonitoring cellular processes is genome-wide analysis of gene expression26,27. Buttechniques for collecting information aboutthe entire genome will be only as powerful asthe tools available to analyse it, just as ourability to infer protein structure and func-tion from protein sequence data hasincreased with the sophistication of tools forsequence analysis. We need better methodsof finding patterns that identify networksand their components, of identifying possi-ble connections among the components,and of reconstructing the evolution of modules by comparing information frommany different organisms.

Another approach to discerning modulefunction is that of ‘synthetic biology’. Just aschemists have tested their understanding ofsynthetic pathways by making molecules,biologists can test their ideas about modulesby attempting to reconstitute or build func-tional modules. This approach has alreadybeen used to construct and analyse artificialchromosomes made by assembling definedDNA elements28, and cellular oscillatorsmade from networks of transcriptional regulatory proteins (M. Elowitz and S. L.,unpublished results). Seeing how well thebehaviour of such modules matches ourexpectations is a critical test of how well weunderstand biological design principles.

The main difficulty in reconstructing theevolution of modules is our ignorance aboutpast events. One solution to this problem is to examine the evolution of module func-tion in the laboratory. Analysing multiplerepetitions of such experiments may tell ushow much the path of future change isrestricted by the current structure and function of modules, and should help us tounderstand how evolutionary pressures constrain biological design principles.

Finally, we emphasize the importance ofintegrating experimental approaches with

modelling and conceptual frameworks. Thebest test of our understanding of cells will beto make quantitative predictions about theirbehaviour and test them. This will requiredetailed simulations of the biochemicalprocesses taking place within the modules.But making predictions is not synonymouswith understanding. We need to developsimplifying, higher-level models and findgeneral principles that will allow us to graspand manipulate the functions of biologicalmodules. The next generation of studentsshould learn how to look for amplifiers andlogic circuits, as well as to describe and lookfor molecules and genes (Box 3). Connectingdifferent levels of analysis — from molec-ules, through modules, to organisms — isessential for an understanding of biologythat will satisfy human curiosity.Leland H. Hartwell is at the Fred HutchinsonCancer Center, Seattle, Washington 98109, USA.John J. Hopfield and Stanislas Leibler are in theDepartment of Molecular Biology (J.H.) andDepartment of Physics and Molecular Biology(S.L.), Princeton University, Princeton, New Jersey08542, USA. Andrew W. Murray is in theDepartment of Physiology, University of Californiaat San Francisco, San Francisco, California 94143,USA.1. Stock, J. B. & Surette, M. G. in Escherichia coli and Salmonella:

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