Population-Based Computational Approaches to Investigate Cardiac Arrhythmia Risk

Mechanistic modeling and simulations have become fundamental techniques for studying cardiac excitation-contraction coupling, to dissect the complex mechanisms that interact nonlinearly to link cardiomyocyte electrical excitation to Ca2+ signaling to contractile response. By complementing the classical wet experimentation, modeling studies have led to many important advances in understanding cardiomyocyte physiopathological mechanisms, including those underlying cardiac arrhythmias. One limitation of the classic modeling paradigm is the lack of inclusion of intercell variability. In fact, most models have been traditionally built with and validated using average data from voltage- and current-clamp experiments. Thus, while they can faithfully recapitulate the average characteristics of several cell subsystems, they may fail to capture the average integrated behavior, or the properties of a given individual in a population. In this chapter, we illustrate the “population-based” modeling approaches recently developed to overcome this limitation and demonstrate how their adoption can strengthen computational investigations. We describe methods to create populations of model variants by randomly varying parameters in an “average” cardiomyocyte model and to analyze the simulated data to characterize the mechanisms associated with regulation of cardiac cellular function in physiological and pathological conditions.