Hybrid automata as a unifying framework for modeling excitable cells.
We propose hybrid automata (HA) as a unifying framework for computational models of excitable cells. HA, which combine discrete transition graphs with continuous dynamics, can be naturally used to obtain a piecewise, possibly linear, approximation of a nonlinear excitable-cell model. We first show how HA can be used to efficiently capture the action-potential morphology--as well as reproduce typical excitable-cell characteristics such as refractoriness and restitution--of the dynamic Luo-Rudy model of a guinea-pig ventricular myocyte. We then recast two well-known computational models, Biktashev's and Fenton-Karma, as HA without any loss of expressiveness. Given that HA possess an intuitive graphical representation and are supported by a rich mathematical theory and numerous analysis tools, we argue that they are well positioned as a computational model for biological processes.
Ye
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Comput. Sci. Dept., Stony Brook Univ., NY 11790, USA.
Entcheva
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Smolka
S A
SA
True
M R
MR
Grosu
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R
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001331:Automation; D006168:Guinea Pigs; D006352:Heart Ventricles; D008954:Models, Biological; D008955:Models, Cardiovascular; D032383:Myocytes, Cardiac / Q000166:cytology* / Q000502:physiology*; D017711:Nonlinear Dynamics; D009991:Oscillometry