A Probability Density Approach to Modeling Local Control of Calcium-Induced Calcium Release in Cardiac Myocytes

Abstract

We present a probability density approach to modeling localized Ca 2+ influx via L-type Ca 2+ channels and Ca 2+-induced Ca 2+ release mediated by clusters of ryanodine receptors during excitation-contraction coupling in cardiac myocytes. Coupled advection-reaction equations are derived relating the time-dependent probability density of subsarcolemmal subspace and junctional sarcoplasmic reticulum [Ca 2+] conditioned on “Ca 2+ release unit” state. When these equations are solved numerically using a high-resolution finite difference scheme and the resulting probability densities are coupled to ordinary differential equations for the bulk myoplasmic and sarcoplasmic reticulum [Ca 2+], a realistic but minimal model of cardiac excitation-contraction coupling is produced. Modeling Ca 2+ release unit activity using this probability density approach avoids the computationally demanding task of resolving spatial aspects of global Ca 2+ signaling, while accurately representing heterogeneous local Ca 2+ signals in a population of diadic subspaces and junctional sarcoplasmic reticulum depletion domains. The probability density approach is validated for a physiologically realistic number of Ca 2+ release units and benchmarked for computational efficiency by comparison to traditional Monte Carlo simulations. In simulated voltage-clamp protocols, both the probability density and Monte Carlo approaches to modeling local control of excitation-contraction coupling produce high-gain Ca 2+ release that is graded with changes in membrane potential, a phenomenon not exhibited by so-called “common pool” models. However, a probability density calculation can be significantly faster than the corresponding Monte Carlo simulation, especially when cellular parameters are such that diadic subspace [Ca 2+] is in quasistatic equilibrium with junctional sarcoplasmic reticulum [Ca 2+] and, consequently, univariate rather than multivariate probability densities may be employed.