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Ye/Entcheva/Smolka/True/Grosu: Hybrid Automata as a Unifying Framework for Modeling Cardiac Cells

Hybrid Automata as a Unifying Framework for Modeling Cardiac Cells*

P. Ye, E. Entcheva, S.A. Smolka, M.R. True and R. Grosu

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.

In Proc. of EMBC'06, the IEEE International Conference of the Engineering in Medicine and Biology Society, New York City, USA, 2006.

*This work was partially supported by the NSF Faculty Early Career Development Award CCR01-33583 and the NSF CCF05-23863 Award.