At the heart of computational modelling

Abstract  The link between experimental data and biophysically based mathematical models is key to computational simulation meeting its potential to provide physiological insight. However, despite the importance of this link, scrutiny and analysis of the processes by which models are parameterised from data are currently lacking. While this situation is common to many areas of physiological modelling, to provide a concrete context, we use examples drawn from detailed models of cardiac electro‐mechanics. Using this biophysically detailed cohort of models we highlight the specific issues of model parameterization and propose this process can be separated into three stages: observation, fitting and validation. Finally, future research challenges and directions in this area are discussed.

[1]  S. Roberds,et al.  Functional characterization of RK5, a voltage‐gated K+ channel cloned from the rat cardiovascular system , 1991, FEBS letters.

[2]  N. Trayanova,et al.  A Computational Model to Predict the Effects of Class I Anti-Arrhythmic Drugs on Ventricular Rhythms , 2011, Science Translational Medicine.

[3]  G. Plank,et al.  Length-dependent tension in the failing heart and the efficacy of cardiac resynchronization therapy. , 2011, Cardiovascular research.

[4]  N. Trayanova,et al.  Systems Approach to Understanding Electromechanical Activity in the Human Heart: A National Heart, Lung, and Blood Institute Workshop Summary , 2008, Circulation.

[5]  N P Smith,et al.  Coupling multi-physics models to cardiac mechanics. , 2011, Progress in biophysics and molecular biology.

[6]  Edmund J. Crampin,et al.  Computational biology of cardiac myocytes: proposed standards for the physiome , 2007, Journal of Experimental Biology.

[7]  Denis Noble,et al.  Markov models for ion channels: versatility versus identifiability and speed , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[8]  Denis Noble,et al.  Cellular Open Resource (COR): a Public CellML Based Environment for Modeling Biological Function , 2003, Int. J. Bifurc. Chaos.

[9]  H. Kitano.  Systems Biology: A Brief Overview , 2002, Science.

[10]  Steven Niederer,et al.  The Role of the Frank–Starling Law in the Transduction of Cellular Work to Whole Organ Pump Function: A Computational Modeling Analysis , 2009, PLoS Comput. Biol..

[11]  M P Nash,et al.  Electromechanical wavebreak in a model of the human left ventricle. , 2010, American journal of physiology. Heart and circulatory physiology.

[12]  J J Rice,et al.  Distribution of electromechanical delay in the heart: insights from a three-dimensional electromechanical model. , 2010, Biophysical journal.

[13]  F. Collins,et al.  A vision for the future of genomics research , 2003, Nature.

[14]  Heye Zhang,et al.  OpenCMISS: a multi-physics & multi-scale computational infrastructure for the VPH/Physiome project. , 2011, Progress in biophysics and molecular biology.

[15]  S. Niederer,et al.  A mathematical model of the slow force response to stretch in rat ventricular myocytes. , 2007, Biophysical journal.

[16]  D Noble,et al.  A meta‐analysis of cardiac electrophysiology computational models , 2009, Experimental physiology.

[17]  Henggui Zhang,et al.  Cardiac cell modelling: observations from the heart of the cardiac physiome project. , 2011, Progress in biophysics and molecular biology.

[18]  P. Hunter,et al.  New developments in a strongly coupled cardiac electromechanical model. , 2005, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[19]  D. P. Nickerson,et al.  A model of cardiac cellular electromechanics , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[20]  Alexander G. Fletcher,et al.  Chaste: A test-driven approach to software development for biological modelling , 2009, Comput. Phys. Commun..

[21]  Peter Buneman,et al.  Challenges in Integrating Biological Data Sources , 1995, J. Comput. Biol..

[22]  Viatcheslav Gurev,et al.  Models of cardiac electromechanics based on individual hearts imaging data , 2011, Biomechanics and modeling in mechanobiology.

[23]  Nathan A. Baker,et al.  A multiscale model linking ion-channel molecular dynamics and electrostatics to the cardiac action potential , 2009, Proceedings of the National Academy of Sciences.

[24]  N P Smith,et al.  Calcium dynamics in the ventricular myocytes of SERCA2 knockout mice: A modeling study. , 2011, Biophysical journal.

[25]  Roy C. P. Kerckhoffs,et al.  Patient-specific modeling of dyssynchronous heart failure: a case study. , 2011, Progress in biophysics and molecular biology.

[26]  Steven Niederer,et al.  Efficient Computational Methods for Strongly Coupled Cardiac Electromechanics , 2012, IEEE Transactions on Biomedical Engineering.

[27]  Gary R. Mirams,et al.  Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk , 2011, Cardiovascular research.

[28]  S. Omholt,et al.  Phenomics: the next challenge , 2010, Nature Reviews Genetics.

[29]  Andrew D McCulloch,et al.  Multi-scale computational models of familial hypertrophic cardiomyopathy: genotype to phenotype , 2011, Journal of The Royal Society Interface.

[30]  Jan Vijg,et al.  Increased cell-to-cell variation in gene expression in ageing mouse heart , 2006, Nature.

[31]  Roy C. P. Kerckhoffs,et al.  Effect of transmurally heterogeneous myocyte excitation–contraction coupling on canine left ventricular electromechanics , 2009, Experimental physiology.

[32]  David Nordsletten,et al.  Coupling contraction, excitation, ventricular and coronary blood flow across scale and physics in the heart , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[33]  J. Lin,et al.  Species similarities and differences in pharmacokinetics. , 1995, Drug metabolism and disposition: the biological fate of chemicals.

[34]  G. Bett,et al.  Computer model of action potential of mouse ventricular myocytes. , 2004, American journal of physiology. Heart and circulatory physiology.

[35]  W. Kannel,et al.  Patterns of coronary heart disease morbidity and mortality in the sexes: a 26-year follow-up of the Framingham population. , 1986, American heart journal.

[36]  P. Benfey,et al.  From Genotype to Phenotype: Systems Biology Meets Natural Variation , 2008, Science.

[37]  J. Rice,et al.  Approximate model of cooperative activation and crossbridge cycling in cardiac muscle using ordinary differential equations. , 2008, Biophysical journal.

[38]  Thomas S Deisboeck,et al.  In silico cancer modeling: is it ready for prime time? , 2009, Nature Clinical Practice Oncology.

[39]  Viatcheslav Gurev,et al.  Mechanisms of Mechanically Induced Spontaneous Arrhythmias in Acute Regional Ischemia , 2010, Circulation research.

[40]  N P Smith,et al.  A mathematical model of the murine ventricular myocyte: a data-driven biophysically based approach applied to mice overexpressing the canine NCX isoform. , 2010, American journal of physiology. Heart and circulatory physiology.

[41]  Eric A Sobie,et al.  Mathematical model of the neonatal mouse ventricular action potential. , 2008, American journal of physiology. Heart and circulatory physiology.

[42]  N. Trayanova,et al.  Mapping of cardiac electrical activation with electromechanical wave imaging: an in silico-in vivo reciprocity study. , 2011, Heart rhythm.

[43]  Andrew D. McCulloch,et al.  Mechanisms of transmurally varying myocyte electromechanics in an integrated computational model , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[44]  S. Niederer,et al.  An improved numerical method for strong coupling of excitation and contraction models in the heart. , 2008, Progress in biophysics and molecular biology.

[45]  Natalia A Trayanova,et al.  The role of photon scattering in optical signal distortion during arrhythmia and defibrillation. , 2007, Biophysical journal.

[46]  G Plank,et al.  Biophysical Modeling to Simulate the Response to Multisite Left Ventricular Stimulation Using a Quadripolar Pacing Lead , 2012, Pacing and clinical electrophysiology : PACE.

[47]  Eric A. Sobie,et al.  Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Cardiac Cells , 2009, PLoS Comput. Biol..

[48]  D. Noble.  Claude Bernard, the first systems biologist, and the future of physiology , 2008, Experimental physiology.

[49]  Glen A. Evans,et al.  Designer science and the “omic” revolution , 2000, Nature Biotechnology.

[50]  Peter Kohl,et al.  Force-length relations in isolated intact cardiomyocytes subjected to dynamic changes in mechanical load. , 2007, American journal of physiology. Heart and circulatory physiology.

[51]  Subbarao Kambhampati,et al.  Integration of biological sources: current systems and challenges ahead , 2004, SGMD.

[52]  H. van de Waterbeemd,et al.  ADMET in silico modelling: towards prediction paradise? , 2003, Nature reviews. Drug discovery.

[53]  P. Hunter,et al.  A quantitative analysis of cardiac myocyte relaxation: a simulation study. , 2006, Biophysical journal.

[54]  Roy C. P. Kerckhoffs,et al.  Ventricular Dilation and Electrical Dyssynchrony Synergistically Increase Regional Mechanical Nonuniformity But Not Mechanical Dyssynchrony: A Computational Model , 2010, Circulation. Heart failure.

[55]  E. Kunkel.  Systems biology in drug discovery , 2004, Nature Biotechnology.

[56]  M. Tanouye,et al.  Molecular cloning and functional expression of a potassium channel cDNA isolated from a rat cardiac library , 1990, FEBS letters.

[57]  Natalia A. Trayanova,et al.  Reversible Cardiac Conduction Block and Defibrillation with High-Frequency Electric Field , 2011, Science Translational Medicine.

[58]  Yoram Rudy,et al.  Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation , 2011, PLoS Comput. Biol..

[59]  Alain Pumir,et al.  Low-energy Control of Electrical Turbulence in the Heart , 2011, Nature.

[60]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[61]  D. Clapham,et al.  Cloning and expression of a rat cardiac delayed rectifier potassium channel. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[62]  Rodrigo Weber dos Santos,et al.  CellML and associated tools and techniques , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.