innovation: integration from proteins to organs: the physiome project

PERSPECTIVES

this so-called ‘Physiome Project’ (BOX 1, FIG. 1).One is in medical physics, in which our abil-ity to image structure and function in aclinical setting — with techniques such asNMR, magnetic resonance imaging, com-puter tomography and electrical imaging —is bringing great advances in diagnosticmedicine. It is probable that within a fewyears whole-body scans that take less thanone minute will be routine and cheap.Another development is in computationalscience. Advances in both computer hard-ware and computational algorithms havetransformed many traditional areas ofphysics and engineering (for example, air-craft can be designed and tested in silicowithout the need for expensive wind-tunneltesting), and now these advances areexpected to bring similar benefits to theunderstanding of biology and the practice

of medicine. When modern computationaltechniques are applied to anatomically andbiophysically based models of human phys-iology, which are partly derived from theimaging techniques mentioned above, theyprovide the means to integrate the vastamount of data that are available fromgenomics, and also now from proteomicsand glycomics (the corresponding attemptto describe all of the carbohydrates that areused by the body), into a framework thatcan be linked to whole-body physiology andclinical medicine.

Computational biology at the level ofion channels and biochemical pathways isusually referred to as ‘systems biology’, andprogress in this field has been well docu-mented in several recent reviews3–8. Here, wedescribe the challenges and progress, at pre-sent, of incorporating these subcellularmodels into tissue- and organ-level modelsthat are relevant to understanding humanphysiology, because this is the focus of thePhysiome Project.

The modelling hierarchyThe need for mathematical modellingshould hardly require justification —mathematics is the language for describingphysical and chemical processes, and math-ematical modelling is the only means of

The Physiome Project will provide aframework for modelling the human body,using computational methods thatincorporate biochemical, biophysical andanatomical information on cells, tissuesand organs. The main project goals are touse computational modelling to analyseintegrative biological function and toprovide a system for hypothesis testing.

The revolution in molecular biology, whichhas dominated biological research for the past20 years and will culminate this year in thecompletion of the final draft of the humangenome, is a testament to the power of inter-national and interdisciplinary science1,2. Thegenome turned out to be relatively simple —there are about 40,000 protein-encodinggenes in human DNA — but uncovering thefull set of gene regulatory mechanisms andthe full set of RNA transcripts (the ‘transcrip-tome’) is dauntingly complex, as one gene canapparently influence as many as 100 othersthrough transcription-factor interactions.Similarly, discovering the full set of pro-teins that are produced by the genome (the‘proteome’), as well as how these proteins areformed by both translation and post-transla-tional modifications, is a huge task. It has,however, created an urgent need to integratethe vast amount of sequence, protein struc-ture and signal-transduction-pathway datainto a mathematical modelling frameworkthat can deal with the complexity of cell, tissueand organ function.

Fortunately, there have been severalimportant developments that will facilitate

NATURE REVIEWS | MOLECULAR CELL BIOLOGY VOLUME 4 | MARCH 2003 | 237

Integration from proteins to organs:the Physiome Project

Peter J. Hunter and Thomas K. Borg

I N N O VAT I O N

Box 1 | A brief history of the Physiome Project

The concept of a ‘Physiome Project’ was presented in a report from the Commission onBioengineering in Physiology to the International Union of Physiological Sciences (IUPS)Council at the 32nd World Congress in Glasgow, UK, in 1993. The term ‘physiome’ comesfrom ‘physio’ (life) and ‘ome’ (as a whole), and is intended to provide a ‘…quantitativedescription of physiological dynamics and functional behaviour of the intact organism’26.A satellite workshop — On designing the Physiome Project — which was organized andchaired by the Chair of the IUPS Commission on Bioengineering in Physiology, JimBassingthwaighte, was held in Petrodvoretz, Russia, after the 33rd World Congress in StPetersburg in 1997. A meeting on the Physiome Project was held at the 34th World Congressof the IUPS in Christchurch, New Zealand, in August 2001, and the Physiome Project wasdesignated, by the IUPS executive, as a major focus for the IUPS during the next decade.P.J.H. was appointed Chair of the newly created Physiome Commission of the IUPS in 2000,and is now co-chair, with Aleksander Popel, of the recently combined IUPS Physiome andBioengineering Committee.

© 2003 Nature Publishing Group

238 | MARCH 2003 | VOLUME 4 www.nature.com/reviews/molcellbio

P E R S P E C T I V E S

the arrhythmogenic vulnerability to bedetermined in terms of physiologicallymeaningful parameters, such as specific ionconductances.

In the next section, we discuss theontologies (that is, taxonomies plusdomain-specific concepts and relation-ships), databases and tools that are neededto create a modelling framework at the vari-ous levels of biological organization. In thefollowing sections, we discuss the require-ments for models at the organ, tissue andcell levels.

Ontologies and software toolsTo access databases of information that arerelevant to modelling, ontologies for struc-ture (anatomy) and function are neededthat, from the highest level of biologicalorganization, begin with the organ systemsand progressively access tissues, cells, cellorganelles, proteins and protein domains(FIG. 3). There is reasonable agreementamong anatomists on how to classify organsystems and among histologists on how toclassify both the structure and function oftissues. An ontology for the functional andstructural components of cells has beendeveloped by the Gene OntologyConsortium (see Online links). A graphicaltool for displaying models at all spatialscales, and interacting with their spatiallyvarying material parameters, is being devel-oped13. In addition, several software packagesare being developed to carry out simulationstudies with the models at the cell level (seeOnline link to The National Resource forCell Analysis and Modeling) and at the tis-sue and organ levels (for example, the inter-active computer program CMISS (contin-uum mechanics, image analysis, signal

use of a coefficient of diffusion in the diffu-sion equation to represent the length scalesthat are associated with the Brownianmotion of diffusing particles. The coeffi-cient of diffusion is the black box that hidesthe detail at a more finely resolved spatiallevel behind an experimentally determinedrelationship between solute flux and con-centration gradient.

An important question for mathematicalmodellers is how much detail to include in amodel. If the added detail includes more freeparameters (that is, model parameters thatcan be altered to force the model to matchobserved behaviour at the integrative level),the answer — in keeping with the principleof Occam’s Razor (‘entities must not bemultiplied beyond what is necessary’) —has to be as little as possible. On the otherhand, detail that is added in the form ofanatomical structure and validated bio-physical relationships can often constrainpossible solutions and therefore enhance thephysiological relevance of a model. It is sur-prisingly easy, for example, to create a modelof ventricular fibrillation with over-simpli-fied representations of cell electrophysiology.Adding more biophysical detail, however, inthe form of membrane ion channels, allows

providing a quantifiable framework forintegrating numerous processes acrossmany spatial and temporal scales. Anyattempt to link molecular and cellularevents with physiological function mustdeal with length scales that range from the 1 nmthat is typical of a protein to the 1 m scaleof an intact body (FIG. 2a). Similarly, therange of timescales must encompass the 1 µsthat is characteristic of Brownian motionto the 109 s (70 years) that is characteristicof a human lifetime (FIG. 2b). It is clear thatno single model can cover a factor of 109 ina spatial scale and a factor of 1015 in atimescale. A more reasonable approach istherefore to develop models for a more lim-ited range of spatial and temporal scalesand to develop techniques to link the para-meters of this hierarchy of models (see, forexample, recent texts on computationalbiology9–12). This means that, at any onelevel, there is a ‘black box’ that groups all ofthe detail at the level below (in either a spa-tial or temporal sense) into a mathematicalexpression. The parameters of this expres-sion are determined directly from experi-ments, but can be related to another, moredetailed, model at the finer spatial or tem-poral level. A familiar example of this is the

Figure 1 | Illustration of the relationship between the physiome and other areas of biologicalorganization. The other areas of biological organization include the genome (the genes encoded in DNA),transcriptome (the messenger RNA produced by gene expression under particular conditions),metabolome (the metabolites that are present under particular conditions) and proteome (the proteins thatare actually produced and where they reside — that is, the translation of the transcriptome together withpost-translational modifications and protein trafficking). There is another level of organization above thephysiome, which deals with populations and interactions with the environment. It should be noted thatmany other processes such as the assembly of carbohydrates and lipids are omitted.

Environment and populations

Organism

Tissues

Transcriptome, metabolome

Genome

OrgansPhysiome

Cells

Signallingpathways

Metabolicpathways

Cell cycle, motility, contraction, adhesion,secretion, sensory, transport…

Ligand binding,phosphorylation ATP

Proteins

Proteome

Growthfactors,

cytokines

Carbohydratesand lipids

“The complexity ofbiological systems, and thevast amount of informationnow available at the level ofgenes, proteins, cells, tissuesand organs, requires thedevelopment ofmathematical models thatcan define the relationshipbetween structure andfunction at all levels ofbiological organization.”



quite uniform across the wall thickness.When the heart contracts in systole, theshearing action of the myocardial sheetsresults in a large degree of wall thickening(especially in the subendocardial region) toachieve a high ejection fraction. Theseinsights into cardiac tissue have been gainedusing models of structure–function relationsthat are measured directly by experimentand are incorporated into the whole-heartcontinuum models. They do not require anunderstanding of the molecular detail.Understanding a disease process, however,such as the change in collagen and ion-channel expression that is associated withheart failure, often does require anothermodel at a finer spatial scale to represent thesignal-transduction pathways that couplemechanical events at the tissue level to geneexpression at the subcellular level (BOX 2).

Another important aspect of the contin-uum modelling of an organ is the need todeal with many types of physical problem inthe same spatial scale. In the heart, forexample, myocardial contraction is gov-erned by the laws of mechanics, but it is ini-tiated by an electrical wave, the propagation

processing and system identification),BioPSE (Bioelectric Problem SolvingEnvironment) and Continuity; see Onlinelinks). Typically, these latter models involvethe solution of large numbers of partialdifferential equations and require high-performance computers.

Models at the organ levelThe principles that govern the integrativebehaviour of an organ or organ systemderive from physical conservation laws suchas the conservation of mass, momentum(linear and angular) and charge. Applyingthese conservation laws to an organ such asthe heart, or to an organ system such as theblood-circulation system, requires either anextra relationship between stress and strain(for a deformable solid such as hard or softtissue) or a strain rate (for a fluid such asblood or air). The ability to define such arelationship on the basis of experimentaltests, without having to specify the detailedbehaviour of all the components of the mate-rial, is the foundation of ‘continuum models’and is the basis of modern engineering prac-tice. For example, the stresses and strains that

are generated by bending a beam can be cal-culated from equations that are based onphysical principles and that do not requireknowledge of the detailed atomic structure ofthe material from which the beam is made.Similarly, conductivity coefficients that areused in the solution of a reaction-diffusionequation, such as the equation that governselectrical-current flow in excitable tissues, arebased on a continuum representation ofcharge movement through the tissue. Thekey idea is to find relationships betweenstress and strain that are based on measuringthe properties of the material experimentally,rather than on determining them from firstprinciples. Finding such relationships is the‘art’ of modelling.

An important point is that physicalprinciples and global behaviour can giveinsights at a local level without the need toinclude every detail of the material in themodel. For example, these principles havebeen used to understand the mechanics ofthe heart14–16. When the heart dilates duringdiastole, the fibrous structure of the heartallows it to twist in a way that results in thesarcomere length of the muscle fibres being


Figure 2 | Linking molecular and cellular events with physiological function must deal with wide ranges of length scales and timescales. a | Levels ofbiological organization from genes to proteins, cells, tissues, organs and finally the whole organism. The range of spatial scales — from ~1 nm for proteins to ~1 mfor the whole body — requires a hierarchy of models. Different types of model are appropriate to each level, and relationships must be established betweenmodels at one level and the more detailed, but spatially or temporally limited, models at the level below. The organ-level and whole-body-level models shown arethe Auckland heart and torso models, respectively32,38. The tissue figure is a reconstructed three-dimensional confocal image of a transmural section of ratmyocardium, which is also from the Auckland Bioengineering Institute, New Zealand28. b | The range of temporal scales as shown here is even more daunting andagain calls for a hierarchy of models. HGP, human genome project. Modified with permission from REF. 39 © Springer-Verlag (2002).

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12 organsystems

1 body

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Proteinturnover

a

b

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Mitosis Humanlifetime




provides an average description of themechanical properties that are relevant tothe analysis of stresses and strains in the tis-sue. However, these load-bearing propertiesof cartilage can only be linked to the proper-ties of the underlying collagen–proteoglycanmatrix if the parameters of the ‘lumped-parameter’ constitutive law are derivedfrom a tissue structural model that repre-sents features such as the collagen types,their orientation, the degree of crosslinkingand the state of proteases. This need to linkmodels across different spatial scales is thegreatest challenge for tissue-level model-ling. For a recent summary of mathematicalapproaches to this problem in an engineer-ing context, see REF. 17.

The spatial variation of material properties— such as the density of collagen, gap junc-tions or ion channels — is a key aspect ofanatomically based models. For example, thevariation across the heart wall in the density ofthe transient outward K+ channel is probablyresponsible for the transmural repolarizationgradient that gives rise to the dispersion of theT-wave of the electrocardiogram and, withcertain familial mutations, provides the cellularbasis for the Brugada and long-QT syndromesthat can lead to potentially fatal reentrantarrhythmias18,19. In addition, a physiome pro-ject on microcirculation is being undertaken asa collaboration between several groups (seeOnline link to The Microcirculation PhysiomeProject).

Models at the cell levelTwo ontologies are required at the cell level —one that deals with cell structure, whichincludes the cytoskeleton and the three-dimensional (3D) configuration oforganelles, and one that deals with cell func-tion. A preliminary classification of mostknown cell types can be found under ‘Cells’on The IUPS Physiome Project web site (seeOnline links) and a coordinated effort isunderway to link to the Gene Ontology

using a markup language (for example,AnatML; see Online links), together withapplication programming interfaces thatallow software developers to write code thatreads the parameterized models into theirsimulation packages. Examples of suchmodels are shown in FIG. 4.

Models at the tissue levelIn many biological processes, the key tosuccessful modelling is the ability to under-stand and represent structure–functionrelationships. This is true at the level ofproteins, cells, tissues and whole organs andit is especially important for relating mod-els across several spatial scales. For example,the load-bearing properties of a soft tissue,such as cartilage, can be modelled by a con-tinuum mechanics constitutive law that

of which is governed by the conservation ofelectrical current. Furthermore, the heart issupplied with energy by a bloodstream thatalso obeys the equations of mechanics, butwith entirely different constitutive laws.Similarly, the function of the lungs is deter-mined by three fundamental physicalprocesses — gas flow in the airways, large-deformation tissue mechanics and bloodflow in the pulmonary circulation. The cou-pling of these physical processes that occurswithin an organ — electromechanics in theheart, or gas exchange between the airwaysand blood vessels in the lungs — is a keyaspect of the organ models.

Parametric descriptions (which are usu-ally based on finite-element or boundary-element computational methods) of allorgans and organ systems can be established

Figure 3 | Accessing information at the various spatial scales using ontologies and webdatabases that contain models encoded in markup languages. The markup languages ensure thatmodels are encoded in a consistent form and allow simulation packages to import the models in astandard format (for information on CellML, see Online links). The Physiome Project database will allowmodels at, for example, the tissue level to be obtained from the TissueML database with parameters thatare appropriate to the relevant organ (and to the spatial location in the organ). This is indicated here by thered arrow that illustrates the tissue structure at a particular location in the heart. dbEST, ExpressedSequence Tags database; dbSTS, Sequence Tagged Sites database; DDBJ, DNA Database of Japan;EMBL, European Molecular Biology Laboratory; OMIM, Online Mendelian Inheritance in Man; PIR, ProteinInformation Resource; SCOPs, Structural Classification of Proteins; SNPbase, Single NucleotidePolymorphisms database; TIGR, The Institute for Genome Research. Modified with permission from REF. 39

© Springer-Verlag (2002).

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Molecule

Cell

Tissue

Genome

Organ

Organ system

Organism Skin (integument)Respiratory systemCirculatory systemCentral nervous systemEndocrine systemMale reproductive systemFemale reproductive systemLymphoid systemMusculoskeletal systemUrinary systemDigestive systemSpecial sense organs

OMIM, MedlinePubMedSNPbase…

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PDB,SCOPs

PIR,SwissProt,

Prosite

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GenBank,EMBL, DDBJ,

TIGR

Online resources

Markup languages

Muscle tissueNerve tissueConnective tissueEpithelial tissue

Web databases

Structure

Protein

RNA

DNA

“…the Physiome Projectuses a hierarchy of modelsin which the (spatially ortemporally) groupedparameters of a modelcomponent at one level canbe interpreted in terms ofthe finer resolution of themodels at the level below.”



Conclusion and future directionsThe complexity of biological systems, andthe vast amount of information now avail-able at the level of genes, proteins, cells, tis-sues and organs, requires the developmentof mathematical models that can define therelationship between structure and func-tion at all levels of biological organization.The range of spatial scales (~1 nm for aprotein to ~1 m for a person) and temporal

Consortium through the global open biologi-cal ontologies (GOBO) site (see Online links).It has become increasingly apparent thatmany aspects of cell function, as well as beingconfined to certain organelles, are also con-fined to microdomains in the organelles. 3Dmodels of cell structure will therefore probablybecome increasingly important in modellingcell function, including signal-transductionpathways.

The primary functions of cells are trans-port, metabolism, signalling, motility, orga-nizing the cytoskeletal structure and carry-ing out the cell cycle. In contrast to modelsat the organ and tissue levels, which aredominated by the physics of the governingcontinuum equations, models at this levelare dominated by the complex biochemistryof cell proteins. Protein structure, however,does not need to be considered at this level,because models of protein–protein or pro-tein–ligand interactions are based on exper-imentally measured kinetic parameters.However, at some stage, these parameters doneed to be linked to models of the structureof a protein and, in particular, its protein-binding domains.

At present, the greatest challenge forcomputational cell biology is to developstandards for defining cell models so thatthey can, for example, communicate withone another in a consistent format, beanalysed for consistency of units and con-straint checking, and be read into simula-tion software in a standard format. As a firststep towards this goal, a markup language(CellML20; see Online links) has been devel-oped to encapsulate models of cell func-tion, and the database of CellML modelsnow contains over 100 models, whichinclude electrophysiological models, meta-bolic-pathway models and signal-transduc-tion-pathway models. Mathematical equa-tions are represented using MathML, andmetadata, which includes bibliographicinformation, uses a syntax that is based onthe Resource Description Format (seeOnline link to The World Wide WebConsortium). All CellML models arederived from peer-reviewed publications.The next goal is to develop open-sourcesoftware tools for model simulation andgraphical rendering of model componentsand simulation results.

Another notable markup-languagedevelopment for biochemical networks isthe systems biology markup language(SBML), which is a standard for exchanginginformation about pathway and reactionmodels between existing applications (fordetails, see Online link to the Systems

Biology Workbench). Other projects formodelling subcellular processes are: the E-Cell project (see Online links), for mod-elling biochemical and genetic processes;the Virtual Cell project (see Online links),which provides a general framework for thespatial modelling and simulation of cellularphysiology; and the Gepasi software pack-age, for modelling biochemical systems (seeOnline links).


Box 2 | The Cardiome Project

An example of a Physiome Project organ model is the integrative model of the heart that wasdeveloped as a collaborative effort by groups at the University of Auckland, New Zealand, OxfordUniversity, UK, and the University of California at San Diego, USA. The model (see FIG. 4c) isbased on a finite-element model of the geometry and fibrous-sheet structure of myocardialtissue27,28. Membrane currents through protein ion channels, pumps and exchangers are modelledat the subcellular level29–31 to reproduce the voltage changes (the ‘action potential’) in the cardiacmuscle cell membrane, which support the propagation of the wave of electrical excitation in themyocardium that precedes each heart beat32 (see figure, part a). The reaction-diffusion equationsthat model current flow in the tissue are solved with an orthotropic diffusivity tensor, which isbased on the fibrous-sheet structure of the tissue33. The active properties of the muscle cells arebased on models of troponin-C calcium binding, thin filament kinetics and myosin cross-bridgekinetics34. Solving the equations of finite deformation elasticity provides the mechanicaldeformation in the myocardium throughout the cardiac cycle35,36. The flow of blood and deliveryof oxygen to the muscle cells is modelled by solving the equations of fluid flow in the coronaryvessels and coupling the behaviour of the compliant vessel wall to the compressive stress thatarises from the muscle contraction16,37 (see figure, part b).

Part a of the figure shows a model of myocardial activation. Wavefront locations are shownusing an eikonal equation to simulate propagation from the distal ends of the Purkinje tree. Foreach sample time, anterior (top) and posterior (bottom) views are given. The endocardial surfacesof the left and right ventricles are coloured red. The regions of electrically activated myocardiumat each time step are coloured yellow. Part b of the figure shows a model of cardiac mechanics,which includes the coronary arteries, at four stages in the cardiac cycle (from left to right: early indiastole, end-diastole, pre-ejection systole and end-systole). The colours (blue minimum to redmaximum) indicate the flow reduction caused by compressive wall stresses acting on thecoronary vessels. LV, left ventricle; RV, right ventricle. Part a is modified with permission fromREF. 33 © Society for Industrial and Applied Mathematics (2002). Part b is reproduced withpermission from REF. 16 © Society for Industrial and Applied Mathematics (2002).

RVLV

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highly complex 3D cell cytoskeletal andorganelle structure, it is less clear how toapply continuum modelling. There arevery significant mathematical challengesin building models that explain whole-cellfunction in terms of the kinetics of pro-tein–ligand and protein–protein interactionsand the spatial dimensions of subcellularmicrodomains. Recent symposia21–25 havebegun to address these important issues.Progress here will link the organ, tissueand cell-level modelling that have beendescribed in this article to the immensedatabases that are now emerging in thispost-genomics era and will result in amore rational basis for medical diagnosticsand drug discovery.

and simulation tools must be in the publicdomain and they must be accessiblethrough the internet.

At the level of tissues, organs and organsystems, the equations that govern biologi-cal function are dominated by continuumphysics, and the computational techniquesthat have been developed for solving engi-neering problems can be applied.However, it is very important that theanatomical and material complexity arenot over-simplified, if the physiologicalfunction is to be understood in relation tothe underlying structure and materialproperties. For the level below this, inwhich signalling pathways and otheraspects of cellular function operate in the

scales (1 µs for Brownian motion to 109 sfor a human lifetime) are clearly too greatfor a single all-encompassing model.Rather, the Physiome Project uses a hierar-chy of models in which the (spatially ortemporally) grouped parameters of a modelcomponent at one level can be interpretedin terms of the finer resolution of the mod-els at the level below. This hierarchy ofmodels must be linked to databases thathold parameters that are relevant to the celland tissue types at the appropriate spatiallocation in an organ. XML (extensiblemarkup language) standards must be devel-oped to encapsulate the models (bothstructurally and functionally) at all levels.These model databases and the visualization

Figure 4 | The 12 organ systems of the body with illustrations of some of the anatomical models. The measurement and modelling of organ-systemanatomy is well advanced and a database of finite-element models has been established (see Online link to The IUPS Physiome Project for further details,including the ontology of each organ system). It should be noted that in all cases, the models include a mathematical description of the anatomy and tissuestructure (for example, the fibrous-sheet structure of the soft tissue in the heart). The organ models are as follows: a | the lungs, including the airways and softparenchymal tissue, that are used for studying gas dynamics40; b | a facial animation model; c | the circulation system and heart, including the coronaryvasculature that is shown with the computed pressure distribution down the coronary arteries, which is used to study the regional distribution of blood flowand oxygen in the myocardium16; d | the musculoskeletal system, showing, in particular, the models of leg muscles that are used to study the biomechanics ofgait; e | a model of the eye that is used to study stress distributions in the cornea during radial keratotomy (a surgical procedure that is used to eliminatemyopia)41; and f | a model of the digestive system in the body that is used for transport studies in the small intestines and stomach and for applications invirtual surgery of the colon.

The 12 organ systems of the body:Skin (integument)Respiratory systemCirculatory systemCentral nervous systemEndocrine systemMale reproductive systemFemale reproductive systemLymphoid systemMusculoskeletal systemUrinary systemDigestive systemSpecial sense organs

b c

d

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f

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41. Sagar, M. A., Bullivant, D. P., Mallinson, G. D., Hunter, P. J. & Hunter, I. W. A virtual environment andmodel of the eye for surgical simulation. Compute.Graph. (ACM) 205–212 (Siggraph, ACM, AddisonWesley, Ontario, 1994).

AcknowledgementsThe authors gratefully acknowledge the contributions from mem-bers of the Bioengineering Institute at the University of Auckland,New Zealand. P.J.H. acknowledges the support of the NewZealand Foundation for Research, Science and Technology, theNew Zealand Health Research Council and the Wellcome Trust.He is also grateful for the discussions on the Physiome Project,over many years, with D. Noble (Oxford University, UK), J. Bassingthwaighte (University of Washington in Seattle, USA)and A. McCulloch (University of California at San Diego, USA).

Online links

FURTHER INFORMATIONAnatML: http://www.physiome.org.nz/sites/physiome/anatml/pages/index.htmlBioPSE: http://software.sci.utah.edu/biopse.htmlCellML: http://www.cellml.orgCMISS:http://www.bioeng.auckland.ac.nz/cmiss/cmiss.phpContinuity: http://cmrg.ucsd.edu/modelling/softwareE-Cell project: http://www.e-cell.orgGene Ontology Consortium: http://www.geneontology.orgGepasi: http://www.gepasi.orgGlobal open biological ontologies (GOBO):http://www.geneontology.org/doc/gobo.htmlThe Bioengineering Institute:http://www.bioeng.auckland.ac.nzThe IUPS Physiome Project:http://www.bioeng.auckland.ac.nz/physiome/physiome.phpThe Microcirculation Physiome Project:http://www.bme.jhu.edu/news/microphysThe National Resource for Cell Analysis and Modeling:http://www.nrcam.uchc.eduThe World Wide Web Consortium: http://www.w3c.orgSystems Biology Workbench: http://www.sbw-sbml.orgVirtual Cell:http://www.nrcam.uchc.edu/vcell_development/vcell_dev.htmlAccess to this interactive links box is free online.

Peter J. Hunter is at the Bioengineering Institute,University of Auckland, New Zealand.

Thomas K. Borg is at the Department ofDevelopmental Biology and Anatomy,

University of South Carolina, USA.

Correspondence to P.J.H.e-mail: [email protected]

doi:10.1038/nrm1054

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Towards an e-biology of ageing:integrating theory and dataThomas B. L. Kirkwood, Richard J. Boys, Colin S. Gillespie,Carole J. Proctor, Daryl P. Shanley and Darren J. Wilkinson

I N N O VAT I O N

Ageing is a highly complex process; it involvesinteractions between numerous biochemicaland cellular mechanisms that affect manytissues in an organism. Although work on thebiology of ageing is now advancing quickly,this inherent complexity means thatinformation remains highly fragmented. Wedescribe how a new web-based modellinginitiative is seeking to integrate data andhypotheses from diverse biological sources.

Recent years have seen rapid progress inunderstanding the science of ageing. A keyfactor has been the interaction between evo-lutionary (why?) and mechanistic (how?)

lines of research — this has helped to shapethe probable genetic basis of ageing and themechanisms that might be involved1. It hasalso helped to overcome a situation in whichthe field was dominated by a plethora ofrival theories with little effective dialoguebetween them. In particular, the ‘disposablesoma theory’1,2 suggests that ageing is causedby evolved limitations in organisms’ invest-ments in somatic maintenance and repair,rather than by active gene programming.This predicts that ageing is due to the grad-ual accumulation of unrepaired randommolecular faults, which leads to an increasedfraction of damaged cells and eventually to


innovation: integration from proteins to organs: the physiome project

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