Simulation of Biological Processes phần 9

206 GENERAL DISCUSSION IV resolve the issue, then you have people queuing up to get further understanding. ThiscomesbacktothepointIemphasizedearlieron.The‘handson’isnecessary. References JacobF,MonodJ1961Geneticregulatorymechanismsinthesynthesisofproteins.JMolBiol 3:318^356 Noble D 2002 Simulation of Na^Ca exchange activity during ischaemia. Ann NY Acad Sci, in press ‘In Silico’ Simulation of Biological Processes: Novartis Foundation Symposium, Volume 247 Edited by Gregory Bock and Jamie A. Goode Copyright ¶ Novartis Foundation 2002. ISBN: 0-470-84480-9 TheIUPSPhysiomeProject P.J. Hunter, P.M.F. Nielsen and D. Bullivant BioengineeringInstitute,UniversityofAuckland,PrivateBag92019,Auckland,NewZealand Abstract.Modernmedicineiscurrentlybene¢tingfromthedevelopmentofnewgenomic and proteomic techniques, and also from the development of ever more sophisticated clinical imaging devices. This will mean that the clinical assessment of a patient’s medical condition could, in the near future, include information from both diagnostic imaging and DNA pro¢le or protein expression data. The Physiome Project of the International Union of Physiological Sciences (IUPS) is attempting to provide a comprehensive framework for modelling the human body using computational methods which can incorporate the biochemistry, biophysics and anatomy of cells, tissues and organs. A major goal of the project is to use computational modelling to analyse integrative biological function in terms of underlying structure and molecular mechanisms. To support that goal the project is establishing web-accessible physiological databases dealing with model-related data, including bibliographic information, at the cell, tissue, organ and organ system levels. This paper discusses the development of comprehensive integrative mathematical models of human physiology based on patient-speci¢c quantitative descriptions of anatomical structures and models ofbiophysicalprocesseswhich reachdown tothegeneticlevel. 2002 ‘In silico’ simulation of biological processes. Wiley, Chichester (Novartis Foundation Symposium247)p207^221 Physiology has always been concerned with the integrative function of cells, organs and whole organisms. However, as reductionist biomedical science succeeds in elucidating ever more detail at the molecular level, it is increasingly di⁄cult forphysiologists to relate integratedwhole organ functionto underlying biophysically detailed mechanisms. Understanding a re-entrant arrhythmia in the heart, for example, depends on knowledge of not only numerous cellular ionic current mechanisms and signal transduction pathways, but also larger scale myocardial tissue structure and the spatial distribution of ion channel and gap junctiondensities. The only means of coping with this explosion in complexity is mathematical modellingöa situation very familiar to engineers and physicists who have long basedtheirdesignandanalysisofcomplexsystemsoncomputermodels.Biological systems, however, are vastly more complex than human engineered systems and understanding them will require specially designed software and instrumentation 207 208 HUNTER ET AL and an unprecedented degree of both international and interdisciplinary collaboration. Furthermore, modern medicine is currently bene¢ting both from the development of new genomic and proteomic techniques, based on our recently discovered knowledge of protein-encoding sequences in the human genome, and from the development of ever more sophisticated clinical imaging devices (MRI, NMR, micro-CT, ultrasound imaging, electrical ¢eld imaging, optical tomography, etc.). This will mean that the clinical assessment of a patient’s medical condition could, in the near future, include information from both diagnostic imaging and DNA pro¢le or protein expression data. To relate these two ends of the spectrum, however, will require very comprehensive integrative mathematical models of human physiology based on patient-speci¢c quantitative descriptions of anatomical structures and models of biophysical processes which reachdowntothegeneticlevel. The term ‘Physiome Project’ means, somewhat loosely, the combination of worldwide e¡orts to develop databases and models which facilitate the understanding of the integrative function of cells, organs and organisms. It was launched in1997 by theInternational Union ofPhysiological Sciences(see http:// www.physiome.org). The project aims both to reach down through subcellular modelling to the molecular level and the database generated by the genome project, and to build up through whole organ and whole body modelling to clinical knowledge and applications. The initial goals include both organ speci¢c modellingsuchastheCardiomeProject(drivenpartlybyacollaborationbetween Oxford University, UK, the University of Auckland, NZ, the University of California at San Diego and Physiome Sciences Inc, but also involving contributions by many other cardiac research groups around the world) and distributed systems such as the Microcirculation Physiome Project (led by Professor Popel at Johns Hopkins University; http://www.bme.jhu.edu/news/ microphys/). ThePhysiomemarkuplanguages AnimportantaspectofthePhysiomeProjectisthedevelopmentofstandardsand toolsforhandlingweb-accessibledataandmodels.Thegoalistohaveallrelevant modelsandtheirparametersavailableonthewebinawaywhichallowsthemodels to be downloaded and run with easy user-editing of parameters and good visualization of results. By storing models in a machine and application independent form it will become possible to automatically generate computer code implementations of the models and to provide web facilities for validating new code. The most appropriate choice for web based data storage would appear to be the newly approved XML standard (eXtensible Markup Languageösee IUPS PHYSIOME PROJECT 209 http://www.w3c.org/). XML ¢les contain tags identifying the names, values and other related information of model parameters whose type is declared in associated DTD (Data Type De¢nition) ¢les. XQL (XML Query Language) is a setoftoolsdesignedtoissuequeriestodatabasesearchenginestoextractrelevant informationfromXMLdocuments(whichcanresideanywhereontheworldwide web). The display of information in web browsers is controlled by XSL (XML Style Language) ¢les. Two groups are currently developing an XML for cell modelling. One group, based at Caltech, is developing SBML (Systems Biology Markup Language) as a language for representing biochemical networks such as cell signalling pathways, metabolic pathways and biochemical reactions (http:// www.cds.caltech.edu/erato/), and a joint e¡ort by the University of Auckland and Physiome Sciences is developing CellML with an initial focus on models of electrophysiology, mechanics, energetics and signal transduction pathway models (http://www.cellml.org). The CellML and SBML development teams are nowworkingtogethertoachieveasinglecommonstandard. TheAucklandgroupisalsodeveloping‘FieldML’toencapsulatethespatialand temporal variation of parameters in continuum (or ‘¢eld’) models, and ‘AnatML’ as a markup language for anatomical data (see http://www.physiome.org.nz). When all the pertinent issues for each area have been addressed it may be appropriate to coalesce all three markup languages into one more general Physiome markup language since the need for a standardized description of spatially varying parameters at the organ level is equally important within the cell for models of cellularprocesses. Thehierarchyofmodels A major objective of the Physiome Project is to develop mathematical models which link gene, protein, cell, tissue, organ and whole body systems physiology into one comprehensive framework. Models are currently being developed at manylevelsinthis hierarchy,including . wholebodysystemmodels . wholebodycontinuummodels . tissueandwholeorgancontinuummodels . subcellularordinary di¡erentialequation(ODE)models . subcellularMarkovmodels . molecularmodels . genenetworkmodels Animportantissueishowtorelatetheparametersofamodelatonespatialscaleto thebiophysicaldetailcapturedinthemodelatthelevelbelow. 210 HUNTER ET AL The computational models used in the Physiome Project are largely ‘anatomically based’. That is, they attempt to capture the real geometry and structure ofanorganinamathematicalformwhichcan beusedtogetherwiththe cellandtissuepropertiestosolvethephysicallawswhichgovernthebehaviourof the organ such as the electrical current £ow, oxygen transport, mechanical deformation and other physical processes underlying function. Wherever possible the models are also ‘biophysically based’, meaning that the equations used to describe the material properties at both cell and tissue level either directly containdescriptionsofthebiophysicalprocessesgoverningthosepropertiesorare derivedfromsuchdescriptionsinacomputationallytractableform.Oneimportant consequenceofananatomicallyandbiophysicallybasedmodellingapproachisthat as more and more detail is added (such as the spatial distribution of ion channel expression) the greater complexity often leads to fewer rather than more free parameters in the models because the number of constraints increases. Another important point is that the governing tissue-level equations represent physical conservation laws that must be obeyed by any materialöe.g. conservation of electrical current (Faraday’s law) or conservation of mass and momentum (Newton’s laws). The models are therefore predictive and represent much more thanjustasummaryofexperimentaldata. The question of how much detail to include in a model is one that all mathematical modellers have to deal with, irrespective of the ¢eld of application. If added detail includes more free parameters (model parameters which can be altered to force the model to match observed behaviour at the integrative level) the answeröin keeping with the principle of Occam’s Razorömust be ‘as little as possible’. On the other hand, detail added in the form of anatomical structure and validated biophysical relationships can often constrain possible solutions and therefore enhance physiological relevance. It is surprisingly easy, for example, to createamodelofventricular¢brillationwithover-simpli¢edrepresentationsofcell electrophysiology. Adding more biophysical detail in the form of membrane ion channelsreducesthearrhythmogenicvulnerability tomorerealisticlevels. A brief summary of the various types of model used in computational physiology is given here in order to highlight the major challenges and the immediaterequirementsforthePhysiomeProject. Tissuemechanics The equationscomefromthe physicallaws of massconservation and momentum conservation in three dimensions and require a knowledge of the tissue structure and material (constitutive) properties, together with a mathematical characterization of the anatomy and ¢brous structure of the organ (or bone, etc.). Solution of the equations gives the deformation, strain and stress distributions ... - tailieumienphi.vn