Cell-specific cardiac electrophysiology models.
The traditional cardiac model-building paradigm involves constructing a composite model using data collected from many cells. Equations are derived for each relevant cellular component (e.g., ion channel, exchanger) independently. After the equations for all components are combined to form the composite model, a subset of parameters is tuned, often arbitrarily and by hand, until the model output matches a target objective, such as an action potential. Unfortunately, such models often fail to accurately simulate behavior that is dynamically dissimilar (e.g., arrhythmia) to the simple target objective to which the model was fit. In this study, we develop a new approach in which data are collected via a series of complex electrophysiology protocols from single cardiac myocytes and then used to tune model parameters via a parallel fitting method known as a genetic algorithm (GA). The dynamical complexity of the electrophysiological data, which can only be fit by an automated method such as a GA, leads to more accurately parameterized models that can simulate rich cardiac dynamics. The feasibility of the method is first validated computationally, after which it is used to develop models of isolated guinea pig ventricular myocytes that simulate the electrophysiological dynamics significantly better than does a standard guinea pig model. In addition to improving model fidelity generally, this approach can be used to generate a cell-specific model. By so doing, the approach may be useful in applications ranging from studying the implications of cell-to-cell variability to the prediction of intersubject differences in response to pharmacological treatment.
Greenberg Division of Cardiology, Weill Cornell Medical College, New York, New York, United States of America.
Willemijn
W
Groenendaal
Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, United States of America.
Francis A
FA
Ortega
Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, United States of America.
Armen R
AR
Kherlopian
Consultant, New Hartford, New York, United States of America.
Andrew C
AC
Zygmunt
Greenberg Division of Cardiology, Weill Cornell Medical College, New York, New York, United States of America; Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, United States of America.
Trine
T
Krogh-Madsen
Greenberg Division of Cardiology, Weill Cornell Medical College, New York, New York, United States of America; Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, United States of America; Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, United States of America.
David J
DJ
Christini
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D002478:Cells, Cultured; D003198:Computer Simulation; D006329:Heart Conduction System / Q000502:physiology*; D015640:Ion Channel Gating / Q000502:physiology; D007473:Ion Channels / Q000502:physiology*; D008564:Membrane Potentials / Q000502:physiology*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D032383:Myocytes, Cardiac / Q000502:physiology*; D013552:Swine
D007473:Ion Channels