title: Computational biology in the study of cardiac ion channels and cell electrophysiology.
abstract: The cardiac cell is a complex biological system where various processes interact to generate electrical excitation (the action potential, AP) and contraction. During AP generation, membrane ion channels interact nonlinearly with dynamically changing ionic concentrations and varying transmembrane voltage, and are subject to regulatory processes. In recent years, a large body of knowledge has accumulated on the molecular structure of cardiac ion channels, their function, and their modification by genetic mutations that are associated with cardiac arrhythmias and sudden death. However, ion channels are typically studied in isolation (in expression systems or isolated membrane patches), away from the physiological environment of the cell where they interact to generate the AP. A major challenge remains the integration of ion-channel properties into the functioning, complex and highly interactive cell system, with the objective to relate molecular-level processes and their modification by disease to whole-cell function and clinical phenotype. In this article we describe how computational biology can be used to achieve such integration. We explain how mathematical (Markov) models of ion-channel kinetics are incorporated into integrated models of cardiac cells to compute the AP. We provide examples of mathematical (computer) simulations of physiological and pathological phenomena, including AP adaptation to changes in heart rate, genetic mutations in SCN5A and HERG genes that are associated with fatal cardiac arrhythmias, and effects of the CaMKII regulatory pathway and beta-adrenergic cascade on the cell electrophysiological function.
authors.affiliation: Cardiac Bioelectricity & Arrhythmia Center, Department of Biomedical Engineering, Washington University, St. Louis, MO 63130-489, USA. rudy@wustl.edu
authors.forename: Yoram
authors.initials: Y
authors.lastname: Rudy
authors.forename: Jonathan R
authors.initials: JR
authors.lastname: Silva
mesh_terms: D000200:Action Potentials; D055592:Biophysical Phenomena; D001703:Biophysics; D020013:Calcium Signaling; D054732:Calcium-Calmodulin-Dependent Protein Kinase Type 2; D017871:Calcium-Calmodulin-Dependent Protein Kinases / Q000378:metabolism; D019295:Computational Biology; D003198:Computer Simulation; D000072237:ERG1 Potassium Channel; D004594:Electrophysiology; D051638:Ether-A-Go-Go Potassium Channels / Q000235:genetics / Q000378:metabolism; D006801:Humans; D007473:Ion Channels / Q000235:genetics / Q000378:metabolism*; D007700:Kinetics; D008390:Markov Chains; D008955:Models, Cardiovascular; D009124:Muscle Proteins / Q000235:genetics / Q000378:metabolism; D009154:Mutation; D032383:Myocytes, Cardiac / Q000378:metabolism*; D062554:NAV1.5 Voltage-Gated Sodium Channel; D011943:Receptors, Adrenergic, beta / Q000378:metabolism; D015398:Signal Transduction; D015222:Sodium Channels / Q000235:genetics / Q000378:metabolism
chemical_list: D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels; D007473:Ion Channels; C000606913:KCNH2 protein, human; D009124:Muscle Proteins; D062554:NAV1.5 Voltage-Gated Sodium Channel; D011943:Receptors, Adrenergic, beta; C568320:SCN5A protein, human; D015222:Sodium Channels; D054732:Calcium-Calmodulin-Dependent Protein Kinase Type 2; D017871:Calcium-Calmodulin-Dependent Protein Kinases