Simulation of Biological Processes phần 9 ppsx

This paper discusses the development of comprehensive integrative mathematical models of human physiology based on patient-speci¢c quantitative descriptions of anatomical structures and

resolve the issue, then you have people queuing up to get further understanding.This comes back to the point I emphasized earlier on The ‘hands on’ is necessary.References

Jacob F, Monod J 1961 Genetic regulatory mechanisms in the synthesis of proteins J Mol Biol 3:318^356

Noble D 2002 Simulation of Na^Ca exchange activity during ischaemia Ann NY Acad Sci, in press

The IUPS Physiome Project

P.J Hunter, P.M.F Nielsen and D Bullivant

Bioengineering Institute, University of Auckland, Private Bag 92019, Auckland, New Zealand

Abstract Modern medicine is currently bene¢ting from the development of new genomic 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

of biophysical processes which reach down to the genetic level.

2002 ‘In silico’ simulation of biological processes Wiley, Chichester (Novartis Foundation Symposium 247) p 207^221

Physiology has always been concerned with the integrative function of cells,organs and whole organisms However, as reductionist biomedical sciencesucceeds in elucidating ever more detail at the molecular level, it is increasinglydi⁄cult for physiologists to relate integrated whole organ function to underlyingbiophysically detailed mechanisms Understanding a re-entrant arrhythmia in theheart, for example, depends on knowledge of not only numerous cellular ioniccurrent mechanisms and signal transduction pathways, but also larger scalemyocardial tissue structure and the spatial distribution of ion channel and gapjunction densities

The only means of coping with this explosion in complexity is mathematicalmodelling
a situation very familiar to engineers and physicists who have longbased their design and analysis of complex systems on computer models Biologicalsystems, however, are vastly more complex than human engineered systems andunderstanding them will require specially designed software and instrumentation

207

‘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

and an unprecedented degree of both international and interdisciplinarycollaboration.

Furthermore, modern medicine is currently bene¢ting both from thedevelopment of new genomic and proteomic techniques, based on our recentlydiscovered knowledge of protein-encoding sequences in the human genome, andfrom the development of ever more sophisticated clinical imaging devices (MRI,NMR, micro-CT, ultrasound imaging, electrical ¢eld imaging, opticaltomography, etc.) This will mean that the clinical assessment of a patient’smedical condition could, in the near future, include information from bothdiagnostic imaging and DNA pro¢le or protein expression data To relate thesetwo ends of the spectrum, however, will require very comprehensive integrativemathematical models of human physiology based on patient-speci¢c quantitativedescriptions of anatomical structures and models of biophysical processes whichreach down to the genetic level

The term ‘Physiome Project’ means, somewhat loosely, the combination ofworldwide e¡orts to develop databases and models which facilitate theunderstanding of the integrative function of cells, organs and organisms It waslaunched in 1997 by the International Union of Physiological Sciences (see http://www.physiome.org) The project aims both to reach down through subcellularmodelling to the molecular level and the database generated by the genomeproject, and to build up through whole organ and whole body modelling toclinical knowledge and applications The initial goals include both organ speci¢cmodelling such as the Cardiome Project (driven partly by a collaboration betweenOxford University, UK, the University of Auckland, NZ, the University ofCalifornia at San Diego and Physiome Sciences Inc, but also involvingcontributions by many other cardiac research groups around the world) anddistributed systems such as the Microcirculation Physiome Project (led byProfessor Popel at Johns Hopkins University; http://www.bme.jhu.edu/news/microphys/)

The Physiome markup languages

An important aspect of the Physiome Project is the development of standards andtools for handling web-accessible data and models The goal is to have all relevantmodels and their parameters available on the web in a way which allows the models

to be downloaded and run with easy user-editing of parameters and goodvisualization of results By storing models in a machine and applicationindependent form it will become possible to automatically generate computercode implementations of the models and to provide web facilities for validatingnew code The most appropriate choice for web based data storage would appear

to be the newly approved XML standard (eXtensible Markup Language
see

http://www.w3c.org/) XML ¢les contain tags identifying the names, values andother related information of model parameters whose type is declared inassociated DTD (Data Type De¢nition) ¢les XQL (XML Query Language) is aset of tools designed to issue queries to database search engines to extract relevantinformation from XML documents (which can reside anywhere on the world wideweb) The display of information in web browsers is controlled by XSL (XMLStyle Language) ¢les Two groups are currently developing an XML for cellmodelling One group, based at Caltech, is developing SBML (Systems BiologyMarkup Language) as a language for representing biochemical networks such ascell signalling pathways, metabolic pathways and biochemical reactions (http://www.cds.caltech.edu/erato/), and a joint e¡ort by the University of Auckland andPhysiome Sciences is developing CellML with an initial focus on models ofelectrophysiology, mechanics, energetics and signal transduction pathwaymodels (http://www.cellml.org) The CellML and SBML development teams arenow working together to achieve a single common standard.

The Auckland group is also developing ‘FieldML’ to encapsulate the spatial andtemporal variation of parameters in continuum (or ‘¢eld’) models, and ‘AnatML’

as a markup language for anatomical data (see http://www.physiome.org.nz) Whenall the pertinent issues for each area have been addressed it may be appropriate tocoalesce all three markup languages into one more general Physiome markuplanguage since the need for a standardized description of spatially varyingparameters at the organ level is equally important within the cell for models ofcellular processes

The hierarchy of models

A major objective of the Physiome Project is to develop mathematical modelswhich link gene, protein, cell, tissue, organ and whole body systems physiologyinto one comprehensive framework Models are currently being developed atmany levels in this hierarchy, including

whole body system models

whole body continuum models

tissue and whole organ continuum models

subcellular ordinary di¡erential equation (ODE) models

subcellular Markov models

molecular models

gene network models

An important issue is how to relate the parameters of a model at one spatial scale tothe biophysical detail captured in the model at the level below

The computational models used in the Physiome Project are largely

‘anatomically based’ That is, they attempt to capture the real geometry andstructure of an organ in a mathematical form which can be used together with thecell and tissue properties to solve the physical laws which govern the behaviour ofthe organ such as the electrical current £ow, oxygen transport, mechanicaldeformation and other physical processes underlying function Whereverpossible the models are also ‘biophysically based’, meaning that the equationsused to describe the material properties at both cell and tissue level either directlycontain descriptions of the biophysical processes governing those properties or arederived from such descriptions in a computationally tractable form One importantconsequence of an anatomically and biophysically based modelling approach is that

as more and more detail is added (such as the spatial distribution of ion channelexpression) the greater complexity often leads to fewer rather than more freeparameters in the models because the number of constraints increases Anotherimportant point is that the governing tissue-level equations represent physicalconservation laws that must be obeyed by any material
e.g conservation ofelectrical current (Faraday’s law) or conservation of mass and momentum(Newton’s laws) The models are therefore predictive and represent much morethan just a summary of experimental data

The question of how much detail to include in a model is one that allmathematical modellers have to deal with, irrespective of the ¢eld of application

If added detail includes more free parameters (model parameters which can bealtered to force the model to match observed behaviour at the integrative level)the answer
in keeping with the principle of Occam’s Razor
must be ‘as little

as possible’ On the other hand, detail added in the form of anatomical structureand validated biophysical relationships can often constrain possible solutions andtherefore enhance physiological relevance It is surprisingly easy, for example, tocreate a model of ventricular ¢brillation with over-simpli¢ed representations of cellelectrophysiology Adding more biophysical detail in the form of membrane ionchannels reduces the arrhythmogenic vulnerability to more realistic levels

A brief summary of the various types of model used in computationalphysiology is given here in order to highlight the major challenges and theimmediate requirements for the Physiome Project

Tissue mechanics

The equations come from the physical laws of mass conservation and momentumconservation in three dimensions and require a knowledge of the tissue structureand material (constitutive) properties, together with a mathematicalcharacterization of the anatomy and ¢brous structure of the organ (or bone, etc.).Solution of the equations gives the deformation, strain and stress distributions

throughout the organ Examples are the large deformation soft-tissue mechanics ofthe heart, lungs, skeletal muscles and cartilage, and the small strain mechanics ofbones The mathematical techniques required for these problems are now wellestablished and the main challenge is to de¢ne the geometry of all body parts andthe spatial variation of tissue structure and material properties The most urgentrequirements are to de¢ne the markup language (FieldML) which allows theanatomy and spatial property variations to be captured in a format for storageand exchange, and to develop the visualization tools for viewing the 3D anatomyand computed ¢elds such as stress and strain Another high priority is to enhancethe tools that allow a generic model to be customized to individual patient datafrom medical imaging devices such as MRI, CAT and ultrasound.

Reaction^di¡usion systems

There are many issues of transport by di¡usion and advection, coupled tobiochemical reactions, in physiological systems The transport equations arebased on well established laws of £ux conservation, and the numerical solutionstrategies are also well developed Examples are the electrical activation of theheart (equations based on conservation of current) and numerous problems indevelopmental biology The need for good anatomical descriptions usingFieldML is similar to the above two categories The main challenges lie indeveloping good models of the biochemical reactions and capturing these in theCellML format for storage and exchange

Electrophysiology

All cells make use of ion channels, pumps and exchangers The mathematicaldescription of the ion channel conductance and voltage (or ion) dependentgating rate parameters is usually based on the Hodgkin^Huxley formalism

(typically using voltage clamp data) or more molecularly-based stochastic models(with patch clamp data) Examples are the Hodgkin^Huxley models of actionpotential propagation in nerve axons, the Noble and Rudy models for cardiac cellelectrophysiology and pancreaticb-cell models of the metabolic dependence ofinsulin release The major challenge now is to relate the parameters of thesemodels to our rapidly increasing knowledge of gene sequence and 3D structurefor these membrane-bound proteins, together with tissue speci¢c ion channeldensities (and isoforms) and known mutations The CellML markup language iscurrently being extended to link into FieldML for handling the spatially varyingparameters such as channel density The most urgent requirements are authoringtools, application programming interfaces (APIs) and simulation tools.

Signal transduction and metabolic pathways

The governing equations here are based on mass balance relations Theinformation content is often based on signal dynamics rather than steady-stateproperties, so a system dynamics and control theoretical framework is important

An example is the eukaryotic mitogen-activated protein kinase (MAPK) signallingpathway which culminates with activation of extracellular signal-regulated kinases(ERKs) The signal transduction pathway de¢nitions can be encapsulated inCellML and a priority now is the development of tools which will allow theactivity of the pathways to be modelled in the context of a 3D cell and linked toion channel and pumps (e.g as sites of phosphorylation), and to tissue and organlevel models

Gene networks

This relates to the study of gene regulation, where proteins often regulate theirown production or that of other proteins in a complex web of interactions Thebiochemistry of the feedback loops in protein^DNA interactions often leads tonon-linear equations Techniques from non-linear dynamics, control theory andmolecular biology are used to develop dynamic models of gene regulatorynetworks

It should be emphasized that no one model could possibly cover the 109dynamicrange of spatial scales (from the 1 nm pore size of an ion channel to the 1 m scale ofthe human body) or 1015dynamic range of temporal scales (from the 1ms typical ofBrownian motion to the 70 years or 109s typical of a human lifetime) Rather, itrequires a hierarchy of models, such that the parameters of one model in thehierarchy can be understood in terms of the physics or chemistry of the modelappropriate to the spatial or temporal scale at the level below This hierarchy ofmodels must range from gene networks, signal transduction pathways and

stochastic models of single channels at the ¢ne scale, up to systems of ODEs,representing cell level function, and partial di¡erential equations, representingthe continuum properties of tissues and organs, at the coarse scale.

Modelling software and databases

There are now a number of cell and organ modelling programs freely available foracademic use:

PathwayPrism and CardioPrism (http://www.physiome.com) provide access todatabases as well as cell modelling and data analysis tools

E-Cell (http://www.e-cell.org/) is a modelling and simulation environment forbiochemical and genetic processes

VCell (http://www.nrcam.uchc.edu/) is a general framework for the spatialmodelling and simulation of cellular physiology

CMISS is the modelling software package developed by the BioengineeringResearch group at the University of Auckland (see http://www.bioeng.auckland.ac.nz/cmiss/cmiss.php)

CONTINUITY from the Cardiac Bioengineering group at UCSD is a ¢niteelement based package targeted primarily at the heart (see http://cmrg.ucsd.edu) BioPSE from the Scienti¢c and Computing Institute (SCI) deals primarily withbioelectric problems (http://www.sci.utah.edu)

CardioWave from the Biomedical Engineering Department at Duke University

is designed for electrical activation of myocardial tissue www.egr.duke.edu/)

(http://bme- XSIM models the transport and exchange of solutes and water in themicrovasculature (http://nsr.bioeng.washington.edu)

Physiome projects

Several Physiome projects are mentioned brie£y here Figure 1 illustrates thesequence of measuring geometric data for the femur and ¢tting a ¢nite elementmodel (Fig 1A,B), incorporating the femur model into a whole skeleton model(Fig 1C) and then combining with the muscles of the leg (Fig 1D) for analysis

of loads in the knee Figure 2 illustrates a model of the torso (Bradley et al 1997),including the heart and lungs and the layers of skin, fat and skeletal muscle, which isbeing used for studying the forward and inverse problems of electrocardiology andfor developing the lung physiome Figure 3 illustrates the ¢brous structure,coronary network and epicardial textures in a model of the heart (LeGrice et al

1997, Smith et al 2000, Kohl et al 2000)

214 HUNTER ET AL

FIG 1 (A) A ¢nite element mesh of the femur prior to ¢tting, together with a cloud of data points measured from a bone with a laser scanner, and (B) the same (bicubic Hermite) mesh after

¢tting the nodal parameters (C) Anatomically detailed model of the skeleton (D) Rendered

¢nite element mesh shown for the bones of the leg and a subset of the muscles (sartorius, rectus femoris and biceps femoris in upper leg and gastrocnemius and soleus in lower leg) The musculo-skeletal models contain descriptions of 3D geometry and material properties and are used in computing stress distributions under mechanical loads.

IUPS PHYSIOME PROJECT 215

FIG 2 Computational model of the skull and torso (A) The layer of skeletal muscle is highlighted (B) The heart and lungs shown within the torso.