Abstract
Cardiac electrophysiology models are among the most mature and well‐studied mathematical models of biological systems. This maturity is bringing new challenges as models are being used increasingly to make quantitative rather than qualitative predictions. As such, calibrating the parameters within ion current and action potential (AP) models to experimental data sets is a crucial step in constructing a predictive model. This review highlights some of the fundamental concepts in cardiac model calibration and is intended to be readily understood by computational and mathematical modelers working in other fields of biology. We discuss the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales. We end with a discussion of the many challenges that work to date has raised and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies.This article is categorized under:Analytical and Computational Methods > Computational MethodsPhysiology > Mammalian Physiology in Health and DiseaseModels of Systems Properties and Processes > Cellular Models
Highlights
Mathematical and computational modeling of cardiac electrophysiology has been one of the great success stories of systems biology, showing how ionic currents interact to produce the action potential (AP) and emergent properties in Abbreviations: ABC, approximate Bayesian computation; AP, action potential; APD, action potential duration; CV, conduction velocity; ERP - effective refractory period; DAD, delayed after-depolarization; EAD, early after-depolarization; ERP, effective refractory period; hiPSC-CM, human induced pluripotent stem cell-derived cardiomyocyte; MCMC, Markov chain Monte Carlo; ODE, ordinary differential equation; Uncertainty quantification (UQ), uncertainty quantification.Dominic G
We focus on electrophysiology, similar principles apply in modeling cardiac mechanics—see Niederer, Campbell, and Campbell (2019) for a recent review of such models
Such models can be written in a form where each gate's kinetics are described by a voltage-dependent steady-state and time constant, and the fitting procedure involves extracting these quantities from current measurements independently for several voltages
Summary
Mathematical and computational modeling of cardiac electrophysiology has been one of the great success stories of systems biology, showing how ionic currents interact to produce the action potential (AP) and emergent properties in Abbreviations: ABC, approximate Bayesian computation; AP, action potential; APD, action potential duration; CV, conduction velocity; ERP - effective refractory period; DAD, delayed after-depolarization; EAD, early after-depolarization; ERP, effective refractory period; hiPSC-CM, human induced pluripotent stem cell-derived cardiomyocyte; MCMC, Markov chain Monte Carlo; ODE, ordinary differential equation; UQ, uncertainty quantification. One consequence of writing a data-generating model like this is that one can infer parameters for both the mechanistic model and the noise model at the same time, commonly by writing a likelihood function for the experimental observations given the parameters within both the mechanistic and statistical models, and maximizing this likelihood (using numerical optimization) to find all of the parameters at once Another advantage of the statistical approach is that it offers a natural way to incorporate prior knowledge about the parameter values into the final estimate: If a prior distribution on the parameters can be defined, it can be combined with the likelihood function through Bayes' rule, to obtain a function for the posterior distribution (which incorporates both prior knowledge and what was learned from this experiment). Assessing whether we have experimental data which allows us to identify all parameters is a task of primary importance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
