title: Hybrid automata as a unifying framework for modeling excitable cells.
abstract: 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.
authors.affiliation: Comput. Sci. Dept., Stony Brook Univ., NY 11790, USA.
authors.forename: P
authors.initials: P
authors.lastname: Ye
authors.forename: E
authors.initials: E
authors.lastname: Entcheva
authors.forename: S A
authors.initials: SA
authors.lastname: Smolka
authors.forename: M R
authors.initials: MR
authors.lastname: True
authors.forename: R
authors.initials: R
authors.lastname: Grosu
mesh_terms: 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