Personalisation of Cellular Electrophysiology Models: Utopia?

As cell-level differences from person to person are gaining more attention, the idea of having personalised models of cell electrophysiology is growing ever more attractive. In this paper for the special session “Personalized medicine through integration of imaging and cardiac modeling”, I briefly review the different pathways to personalisation and the challenges they present.

[1]  Jamie I. Vandenberg,et al.  Sinusoidal voltage protocols for rapid characterization of ion channel kinetics , 2017, bioRxiv.

[2]  Amrita X. Sarkar,et al.  Exploiting mathematical models to illuminate electrophysiological variability between individuals , 2012, The Journal of physiology.

[3]  P. Pathmanathan,et al.  Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges , 2018, Journal of Cardiovascular Translational Research.

[4]  Jørgen K. Kanters,et al.  In silico cardiac risk assessment in patients with long QT syndrome: type 1: clinical predictability of cardiac models. , 2012, Journal of the American College of Cardiology.

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

[6]  Michael Clerx,et al.  Reproducible model development in the cardiac electrophysiology Web Lab , 2018, bioRxiv.

[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]  S. Severi,et al.  Human induced pluripotent stem cell‐derived versus adult cardiomyocytes: an in silico electrophysiological study on effects of ionic current block , 2015, British journal of pharmacology.

[9]  Chon Lok Lei,et al.  Tailoring Mathematical Models to Stem-Cell Derived Cardiomyocyte Lines Can Improve Predictions of Drug-Induced Changes to Their Electrophysiology , 2017, Front. Physiol..

[10]  Katherine C. Wu,et al.  Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models , 2016, Nature Communications.

[11]  Gary R. Mirams,et al.  Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics , 2018, The Journal of physiology.

[12]  Leslie Tung,et al.  Cardiomyocytes derived from human induced pluripotent stem cells as models for normal and diseased cardiac electrophysiology and contractility. , 2012, Progress in biophysics and molecular biology.

[13]  Peter J. Hunter,et al.  Bioinformatics Applications Note Databases and Ontologies the Physiome Model Repository 2 , 2022 .

[14]  Colleen E. Clancy,et al.  In silico Prediction of Sex-Based Differences in Human Susceptibility to Cardiac Ventricular Tachyarrhythmias , 2012, Front. Physio..

[15]  Jussi T. Koivumäki,et al.  Structural Immaturity of Human iPSC-Derived Cardiomyocytes: In Silico Investigation of Effects on Function and Disease Modeling , 2018, Front. Physiol..

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

[17]  Gordana Vunjak-Novakovic,et al.  Advanced maturation of human cardiac tissue grown from pluripotent stem cells , 2018, Nature.

[18]  Kevin Burrage,et al.  Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm , 2016, Progress in biophysics and molecular biology.

[19]  Richard A. Gray,et al.  A Parsimonious Model of the Rabbit Action Potential Elucidates the Minimal Physiological Requirements for Alternans and Spiral Wave Breakup , 2016, PLoS Comput. Biol..

[20]  Zhong Jian,et al.  Sequential dissection of multiple ionic currents in single cardiac myocytes under action potential-clamp. , 2011, Journal of molecular and cellular cardiology.

[21]  Y. Rudy,et al.  Linking a genetic defect to its cellular phenotype in a cardiac arrhythmia , 1999, Nature.

[22]  Gary R. Mirams,et al.  Recent developments in using mechanistic cardiac modelling for drug safety evaluation , 2016, Drug discovery today.

[23]  V. Maltsev,et al.  Molecular identity of the late sodium current in adult dog cardiomyocytes identified by Nav1.5 antisense inhibition. , 2008, American journal of physiology. Heart and circulatory physiology.

[24]  Michael Clerx,et al.  Applying novel identification protocols to Markov models of INa , 2015, 2015 Computing in Cardiology Conference (CinC).

[25]  Pras Pathmanathan,et al.  Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models? , 2016, Journal of molecular and cellular cardiology.

[26]  Teng Hong Tan,et al.  Modeling type 3 long QT syndrome with cardiomyocytes derived from patient-specific induced pluripotent stem cells. , 2013, International journal of cardiology.

[27]  D. Noble A modification of the Hodgkin—Huxley equations applicable to Purkinje fibre action and pacemaker potentials , 1962, The Journal of physiology.

[28]  Carlos Sánchez,et al.  Na/K pump regulation of cardiac repolarization: insights from a systems biology approach , 2013, Pflügers Archiv - European Journal of Physiology.

[29]  Trine Krogh-Madsen,et al.  Cell-Specific Cardiac Electrophysiology Models , 2015, PLoS Comput. Biol..

[30]  Gary R. Mirams,et al.  The Cardiac Electrophysiology Web Lab , 2016, Biophysical journal.

[31]  W. J. Hedley,et al.  A short introduction to CellML , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[32]  Stefan A. Mann,et al.  hERG K(+) channels: structure, function, and clinical significance. , 2012, Physiological reviews.

[33]  Haibo Ni,et al.  A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research , 2018, Front. Physiol..