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An Efficient Method for Modeling Kinetic Behavior of Channel Proteins in Cardiomyocytes

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Wang, Chong, Beyerlein, Peter, Pospisil, Heike, Krause, Antje, Nugent, Chris D and Dubitzky, W (2012) An Efficient Method for Modeling Kinetic Behavior of Channel Proteins in Cardiomyocytes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,, 9 (1). pp. 40-51. [Journal article]

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DOI: 10.1109/TCBB.2011.84

Abstract

Characterization of the kinetic and conformational properties of channel proteins is a crucial element in the integrative studyof congenital cardiac diseases. The proteins of the ion channels of cardiomyocytes represent an important family of biologicalcomponents determining the physiology of the heart. Some computational studies aiming to understand the mechanisms of the ionchannels of cardiomyocytes have concentrated on Markovian stochastic approaches. Mathematically, these approaches employChapman-Kolmogorov equations coupled with partial differential equations. As the scale and complexity of such subcellular andcellular models increases, the balance between efficiency and accuracy of algorithms becomes critical. We have developed a novel two-stage splitting algorithm to address efficiency and accuracy issues arising in such modeling and simulation scenarios. Numerical experiments were performed based on the incorporation of our newly developed conformational kinetic model for the rapid delayed rectifier potassium channel into the dynamic models of human ventricular myocytes. Our results show that the new algorithm significantly outperforms commonly adopted adaptive Runge-Kutta methods. Furthermore, our parallel simulations with coupled algorithms for multicellular cardiac tissue demonstrate a high linearity in the speedup of large-scale cardiac simulations.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Life and Health Sciences
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Life and Health Sciences > School of Biomedical Sciences
Research Institutes and Groups:Biomedical Sciences Research Institute
Computer Science Research Institute
Biomedical Sciences Research Institute > Molecular Medicine
Computer Science Research Institute > Smart Environments
ID Code:20726
Deposited By:Professor Werner Dubitzky
Deposited On:10 Jan 2012 14:49
Last Modified:10 Jan 2012 14:49

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