conference review attractive models: how to make the

Comparative and Functional GenomicsComp Funct Genom 2003; 4: 155–158.Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.205

Conference Review

Attractive models: how to make the siliconcell relevant and dynamic

Hans V. Westerhoff1,2*, Frank Bruggeman2, Jan-Hendrik S. Hofmeyr3 and Jacky L. Snoep2,3

1Stellenbosch Institute for Advanced Study, Stellenbosch 7600, South Africa2BioCentrum Amsterdam, De Boelelaan 1087, NL-1081 HV Amsterdam, The Netherlands3Department of Biochemistry, University of Stellenbosch, Matieland 7602, South Africa

*Correspondence to:Hans V. Westerhoff, Departmentof Molecular Cell Physiology,BioCentrum Amsterdam, Facultyof biology, Vrije Universiteit, DeBoelelaan 1087, 1081 HVAmsterdam, The Netherlands.E-mail: [email protected]

Received: 10 July 2002Accepted: 2 August 2002

Is biology just a complicated special caseof physics? Are its networks special?

The completion of the DNA inventory of a numberof living organisms has invalidated the excuse thatbiology cannot be exact because it is necessarilyincomplete. No longer can the failure of a model toexplain experimental data be attributed to the inter-ference of as yet unknown factors. Any such factorcan become known given sufficient experimentaleffort. In addition, the concentrations of the tran-scripts of all the genes, of all proteins and soon ofa number of organisms can now be measured. Thisbrings cell biology irreversibly into the realm ofthe physical chemical sciences, where models andhypotheses are scrutinized for consistency with allknown principles and experimental data, and thentested by specially designed critical experiments.

Does this mean that biology has become abranch of physics (and chemistry)? As functionalgenomics unfolds, it becomes clearer and clearerthat it has not and will not. At least not when‘physics’ and ‘chemistry’ are taken to refer tothe archetypical sciences that were so successfulin the last century. Physics and chemistry were

successful in the selection or dissection of theirobjects of study. They only considered small, moreor less autonomous parts, which were then studiedindependently. For quite a while (molecular) biol-ogy followed suit, also dissecting and then char-acterizing and understanding the individual macro-molecules of living cells in isolation. And indeed,that (molecular) biology came close to physicsand chemistry.

Biology is unique among the natural sciences inthat it is ultimately bound to a single object ofstudy, which is life itself, or at least to a singleclass of objects, i.e. living systems. And ‘life’cannot be reduced indefinitely. There is a smallestunit of life: the autonomously replicating unit. Thisunit cannot be dissected further without losing thatcharacteristic of biology, i.e. life. That is not to saythat biology should not also dissect living systemsand engage in molecular biology, but it shouldultimately return to its object of study, i.e. livingsystems and life itself.

How large and complex is this minimum unit ofbiology? We shall address this question in threeways, from the insight of what may be needed forlife, from genome sequence data and from gene

Copyright 2003 John Wiley & Sons, Ltd.

156 H. V. Westerhoff et al.

expression data. The smallest autonomously repli-cating unit is the living cell. Biochemistry hasalready explained why this unit is indivisible. Bio-logical information being stored in a sequence ofreliably pairing bases requires the biosynthesis ofthose bases, the sugar phosphate backbone, as wellas a supply of free energy (ATP). In order to drivethe re-phosphorylation of ADP, catabolic pathwayssuch as glycolysis are needed. For the rates of theseprocesses to be competitive, protein catalysts (oftenwith co-factors and prosthetic groups) are required.This necessitates a protein synthesis machinery.To keep everything together, a lipid bilayer is anappropriate device, provided that extra transportproteins are inserted [14].

The sequencing of entire genomes has confirmedthis view that there is a substantial minimum sizeof the unit of life, where size is here measuredin terms of the number of processes. The genomeof Mycoplasma genitalium was shown to containabout 400 genes, of which only 100 or so may bedispensable. Following the ‘first law’ of biochem-istry, that every process in living cells is catalysedby something that involves a protein, the ‘firstlaw’ of genetics, that each process corresponds toa gene, and the ‘first law’ of molecular biology,that each gene corresponds to a stretch of nucleicacid, this suggests that a few hundred processesconstitute the minimum unit of life. The experi-mental observation that deletion of any of the 300processes interferes with the living state [4] consti-tutes the experimental proof that 300 processes isindeed the minimum required for life.

The second phase of genomics brought an addi-tional suggestion of a minimum unit of life. Exter-nal perturbations were seen to cause the mRNAexpression levels of many genes to change [e.g. 2,12]. In most studies of the transcriptome, a func-tional change appeared not to be accompanied bythe altered expression of a single, ‘key’, gene, butrather by that of a multitude of genes.

A few hundred different processes involve hun-dreds of independent variables. If indeed this unitof hundreds of processes were not reducible, thenthis should make life unsuitable for the traditionalparadigms of physics and chemistry. Life shouldbe yet another one of those cases where (tradi-tional) physics ‘either stands mute, or gives thewrong answers’ [8]. Yet the opinion of many physi-cists, chemists and molecular biologists has beenthat this schism between physics and biology was

not essential: life was conceived as a collection ofprocesses that were so involved in each other thatno general principles ruled. Life should just be acomplicated special case of physical chemistry. Itshould therefore be good enough to examine eachof the processes individually. The grand total ofthe understanding of the 300 processes should thenconstitute the understanding of ‘life’.

This point of view admits that biology as ascience reaches beyond physics and chemistry, butthen holds that the extension is trivial. Is theextension indeed trivial? Any non-trivial differencebetween the unit of 300 processes and the sum ofthe 300 corresponding individual processes mustreside in, or derive from, the interactions betweenthe processes. Any non-triviality must arise in,or from, ‘the network’. The question therefore is:if we do take those interactions into account, donew properties arise? Is the network special? Oris the behaviour of the entire system not muchdifferent from the sum of the behaviour of itsmacromolecules in isolation?

Traditional theoretical biology, virtualand e-cells: what may be missing?

There have been quite a few demonstrations ofcomplex properties arising in the interactions of anumber of processes. Examples are the oscillationscalculated for models of glycolysis. The oscilla-tions did not, and could not, arise in the individ-ual macromolecules; they really required the net-work [7]. However, this type of calculation did notconstitute proof, as they were performed for ‘coremodels’. Such core models contain what might becharacteristic properties of the real intracellular net-works, but in highly simplified form, and for ratherarbitrary parameter values. Hence they show whatmight happen in the intracellular networks, but notwhat does happen. Most theoretical biology showswhat might happen, but not what does happen orwhy it happens.

User-friendly modelling programs and even en-tire modelling environments, such as the ‘virtualcell’, have been created for biochemical networks[e.g. 6, 3, 11]. However, contrary to what its name

suggests, the ‘virtual cell’ does not correspond to animage of the cell; it is a valuable collection of soft-ware allowing one to model intracellular processes.These collections of software do not suffice to

Copyright 2003 John Wiley & Sons, Ltd. Comp Funct Genom 2003; 4: 155–158.

Attractive models 157

address the properties of the real intracellular net-works. The flux balance analysis of the Escherichiacoli cell generated by Palsson and co-workers [1]serves the important purpose of analysing whathappens in the living E. coli cell (in terms of fluxanalysis). However, the modelling methods used inthis approach do not contain any other properties ofthe gene products than that they have the capabilityto catalyse certain reactions. They do not explainthe observed fluxes. Elementary mode analysis [9]only uses network stoichiometry and demonstrateswhich steady-state fluxes might run through theliving cell, given some required kinetic propertiesof the intracellular macromolecules, but does notspecify the required properties.

These methods show what does or may happen,but not why it happens. They lack a characteris-tic of physics that we have not mentioned yet: toexplain, not just to analyse. Physics would wantto understand, on the basis of the kinetic and ther-modynamic properties of the intracellular macro-molecules, why these fluxes are as high as theyare, and why the phenomena of life occur.

Attractive silicon models

What makes networks ‘live’? From much work intheoretical and mathematical biology we know thatself-organization, homeostasis, adaptation, ultra-sensitivity, chemotaxis and cell cycling can all arisefrom biochemical reaction networks, provided thatthe macromolecules interact in certain ways. Tak-ing an (ideal) metabolic pathway as an example,one may summarize the ways enzymes interact as‘talking’ and ‘listening’. The ‘talking’ correspondsto carrying out a chemical conversion at a cer-tain rate. The ‘listening’ corresponds to enzymesadjusting the rates at which they catalyse their reac-tions in response to the variations in metaboliteconcentrations.

The recently initiated Silicon Cell Project [10]differs from the above-mentioned developmentsin that it has a computer replica in which themacromolecules ‘listen’ and ‘talk’ (or whateverelse important macromolecules do) in silico. It thencalculates the behaviour implied by the knowledgewe have about the listening and talking. Theprocedure uses software, not hardware; ‘siliconcells’ do not correspond to analogue computers butto software replicas of the living cell.

The properties of the macromolecules used inthe calculations should be real and based on exper-imental determinations of those properties them-selves. The corresponding experiments typicallycorrespond to in vitro enzyme kinetics, or to in vivodeterminations of the kinetic properties of theindividual macromolecules. The kinetic parametersshould not be obtained by fitting the parameter val-ues such that the silicon cell behaves just like thereal cell. It is in this crucial aspect that siliconcells differ from many existing, ‘phenomenologi-cal’ models. This property of silicon cells is also theone that makes them an important tool to addressthe issue of whether there is essential new ‘physics’in biology.

Twenty-first century biology: beyondchemistry and physics

Silicon cells should allow one to address thisquestion. Some of the existing silicon cells havesuggested that such non-trivial properties indeedarise in realistic networks [e.g. 5, 15]. Althoughnon-trivial, these properties can be down-to-earth,such as the property that the system reaches ametabolic steady state rather than engaging in ametabolic explosion [13]. Whether these propertiesarise in silico depends on the actual magnitudeof the kinetic parameters, stressing the importanceof silicon cells vis-a-vis the already existing ‘coremodels’. What comes from the networks is largelynon-trivial. In consequence, biology is not just acomplicated special case of physics. Accordinglybiology need not, and should not, conform tothe paradigm of traditional physics and chemistry.Biology is the science of excellence concerningnatural networks and what they bring, and biologymay need to grow beyond physics and chemistry.

Silicon cells have only data as input and under-standing as output. Hence they classify, as bioin-formatics integrates various types of information,we sometimes refer to this approach as ‘integrativebioinformatics’. Relative to systems biology, sili-con cells have the property that they relate directlyto molecular and experimentally determined prop-erties. They may therefore correspond to molecularsystems biology.

The fact that silicon cells require precise magni-tudes of kinetic parameters as input brings us to asevere limitation to the approach. The processes of


158 H. V. Westerhoff et al.

living cells for which the kinetic properties of allparticipating macromolecules are known, are rare.Consequently, there are only very few true siliconcells in existence. Indeed most silicon cells do notquite fulfil the above requirements and should beseen as alpha versions of what should constitutethe real silicon cell. For their development, siliconcells require close association with precise exper-imentation, preferably in ‘entering the living cell’contexts. We think that the philosophy behind sili-con cells and the possibility to create such cells inthe wake of functional genomics, will make mod-els of living cells more realistic and dynamic, andtherefore attractive and relevant.

References

1. Covert MW, Schilling CH, Famili I, et al. 2001. Metabolicmodeling of microbial strains in silico. Trends Biochem Sci26: 179–186.

2. Gasch AP, Spellman PT, Kao CM, et al. 2000. Genomicexpression programs in the response of yeast cells toenvironmental changes. Mol Biol Cell 11: 4241–4257.

3. Gepasi: http://www.gepasi.org/.4. Hutchison CA, Peterson SN, Gill SR, et al. 1999. Global

transposon mutagenesis and a minimal Mycoplasma genome.Science 286: 2165–2169.

5. Kholodenko BN, Demin OV, Moehren G, Hoek JB. 1999.Quantification of short term signaling by the epidermal growthfactor receptor. J Biol Chem 274: 30 169–30 181.

6. Mendes P. 1997. Biochemistry by numbers: simulation ofbiochemical pathways with Gepasi 3. Trends Biochem Sci 22:361–363.

7. Prigogine I, Nicolis G. 1971. Biological order, structure andinstabilities. Q Rev Biophys 4: 107–148.

8. Rosen R. 1991. Life Itself. Columbia University Press: NewYork.

9. Schuster S, Fell DA, Dandekar T. 2000. A general definitionof metabolic pathways useful for systematic organization andanalysis of complex metabolic networks. Nature Biotechnol18: 326–332.

10. Silicon Cell Project: http://www.siliconcell.net.11. Slepchenko BM, Schaff JC, Carson JH, Loew LM. 2002.

Computational cell biology: spatiotemporal simulation ofcellular events. Ann Rev Biophys Biomol Struct. 31: 423–441.

12. Spellman PT, Sherlock G, Zhang MQ, et al. 1998. Compre-hensive identification of cell cycle-regulated genes of the yeastSaccharomyces cerevisiae by microarray hybridization. MolBiol Cell 9: 3273–3297.

13. Teusink B, Walsh MC, van Dam K, Westerhoff HV. 1998.The danger of metabolic pathways with turbo design. TrendsBiochem Sci 23: 162–169.

14. Wallace DC, Morowitz HJ. 1973. Genome size and evolution.Chromosoma 40: 121–126.

15. Westerhoff HV, Getz WM, van Verseveld HW, Hofmeyr JHS,Snoep JL. 2002. Bioinformatics, cellular flows, and calcula-tion. Ernst Scher Found Symp 38: 221–243.


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