Computational Studies of Calcium Dynamics in Cardiac Cells Using GPGPU
Date
2014-08
Authors
Hoang-Trong, Tuan Minh
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Abstract
Calcium release in the heart plays a central role regulating and linking the electrical excitation of the heart with contraction. The molecular and cellular processes that govern calcium release are stochastic in nature involving over 1,000,000 stochastic ion channels. This creates a highly computationally expensive problem that necessitated the creation of novel algorithms that exploited advanced computational architectures. The studies presented in this dissertation started with introducing a fast, high-memory efficiency algorithm to simulate stochastic gating of ion channels. The method was then implemented using the novel computing platform GPU which utilizes Compute Unified Device Architecture (CUDA) programming model, and to apply it in developing experimentally-based models of the rat ventricular myocytes. There are two levels of modelling were developed in this dissertation: the stochastic compartmental model that incorporate a mechanistic representation of the calcium-induced calcium-release (CICR) mechanism, and a stochastic temporal-spatial model that incorporate the spatial placement of all the calcium release units (CRU) in the myocyte. The presented work was used to answer specific scientific questions to the morphologies of ionic currents under heart failure (HF) condition, calcium alternans and the critical number of cells in order to trigger a spontaneous ectopic heart beat. Then, the spatial model was used to study the dynamic changes in calcium sparks under atrial fibrillation (AF) conditions. The integrated whole-cell temporal spatial model for the rat was used to study calcium waves, which do not occur during normal conditions, but are important as they can cause membrane depolarizations under certain conditions as the result of the life threatening cardiac arrhythmias.
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Keywords
Bioinformatics, Biostatistics, Computer science, Calcium signaling, Cardiac myocyte, CUDA, GPGPU, Heart failure, Stochastic modeling