Abstract:

Markovian models of ion channels have proven useful in the reconstruction of experimental data and prediction of cellular electrophysiology. We present the stochastic Galerkin method as an alternative to Monte Carlo and other stochastic methods for assessing the impact of uncertain rate coefficients on the predictions of Markovian ion channel models. We extend and study two different ion channel models: a simple model with only a single open and a closed state and a detailed model of the cardiac rapidly activating delayed rectifier potassium current. We demonstrate the efficacy of stochastic Galerkin methods for computing solutions to systems with random model parameters. Our studies illustrate the characteristic changes in distributions of state transitions and electrical currents through ion channels due to random rate coefficients. Furthermore, the studies indicate the applicability of the stochastic Galerkin technique for uncertainty and sensitivity analysis of bio-mathematical models.

Abstract:

Based on the Weiner-Hermite polynomial chaos expansion, the stochastic Galerkin method efficiently computes nu- merical solutions for stochastic systems. Unlike such tech- niques as sensitivity analysis, perturbation methods, and second moment-analysis, this method is applicable to a large number of systems while requiring less computational effort than sampling based stochastic methods like Monte Carlo. We utilize the stochastic Galerkin method to assess the impact of stochastic rate coefficients on the predictions of Markovian cardiac ion channel models