Abstract
In cardiac electrophysiology, there exist many sources of inter- and intra-personal variability. These include variability in conditions and environment, and genotypic and molecular diversity, including differences in expression and behavior of ion channels and transporters, which lead to phenotypic diversity (e.g., variable integrated responses at the cell, tissue, and organ levels). These variabilities play an important role in progression of heart disease and arrhythmia syndromes and outcomes of therapeutic interventions. Yet, the traditional in silico framework for investigating cardiac arrhythmias is built upon a parameter/property-averaging approach that typically overlooks the physiological diversity. Inspired by work done in genetics and neuroscience, new modeling frameworks of cardiac electrophysiology have been recently developed that take advantage of modern computational capabilities and approaches, and account for the variance in the biological data they are intended to illuminate. In this review, we outline the recent advances in statistical and computational techniques that take into account physiological variability, and move beyond the traditional cardiac model-building scheme that involves averaging over samples from many individuals in the construction of a highly tuned composite model. We discuss how these advanced methods have harnessed the power of big (simulated) data to study the mechanisms of cardiac arrhythmias, with a special emphasis on atrial fibrillation, and improve the assessment of proarrhythmic risk and drug response. The challenges of using in silico approaches with variability are also addressed and future directions are proposed.
Highlights
Beginning with the seminal paper by Hodgkin and Huxley, 1952, mathematical models of electrophysiology have proven to be valuable tools for better understanding many physiological processes, especially in cardiac arrhythmia research (Noble et al, 2012; Dibb et al, 2015)
Fifty-six years after publication of the first cardiac model (Noble, 1962), there is currently a computational model for almost every cell type of the heart, including nodal, atrial, ventricular, and Purkinje cells (Beeler and Reuter, 1977; Difrancesco and Noble, 1985; Luo and Rudy, 1991; Inada et al, 2009; Maltsev and Lakatta, 2009; Sampson et al, 2010; Grandi et al, 2011; O’Hara et al, 2011), for Simulating Variability in Cardiac Arrhythmia Research numerous species, and for various levels of complexity across multiple spatial scales (Fink et al, 2011). Most of these models use average data from voltage-clamp experiments of individual ionic membrane currents, and while they have led to many important advances in studies of cardiac electrophysiology and pathology, especially cardiac arrhythmias (Sepulveda et al, 1989; Courtemanche and Winfree, 1991; Panfilov and Holden, 1991; Gray et al, 1995; Krogh-Madsen and Christini, 2012; Roberts et al, 2012; Bueno-Orovio et al, 2014), they typically represent the average behavior of a particular cell type
Lee et al compared the impact of ionic processes on AP duration (APD) in control and atrial fibrillation (AF)-remodeled cells and found that the Na+/Ca2+ exchanger (NCX) current has little influence on APD in control cells but more markedly impacts AF cells; the analysis revealed that IK1 upregulation plays a dominant role in APD shortening in AF, and that the L-type Ca2+ current (ICaL) significantly contributes to rate-dependent APD changes in both control and AF myocytes
Summary
We outline the recent advances in statistical and computational techniques that take into account physiological variability, and move beyond the traditional cardiac model-building scheme that involves averaging over samples from many individuals in the construction of a highly tuned composite model. We discuss how these advanced methods have harnessed the power of big (simulated) data to study the mechanisms of cardiac arrhythmias, with a special emphasis on atrial fibrillation, and improve the assessment of proarrhythmic risk and drug response.
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