From cardiac cells to genetic regulatory networks

Authors:
Radu Grosu;Gregory Batt;Flavio H. Fenton;James Glimm;Colas Le Guernic;Scott A. Smolka;Ezio Bartocci
Affiliations:
Dept. of Comp. Sci., Stony Brook University, Stony Brook, NY;INRIA, Le Cesnay Cedex, France;Dept. of Biomed. Sci., Cornell University, Ithaca, NY;Dept. of Appl. Math. and Sta., Stony Brook University, Stony Brook, NY;Dept. of Comp. Sci., New York University, New York, NY;Dept. of Comp. Sci., Stony Brook University, Stony Brook, NY;Dept. of Comp. Sci., Stony Brook University, Stony Brook, NY and Dept. of Appl. Math. and Sta.
Venue:
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Year:
2011

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Abstract

A fundamental question in the treatment of cardiac disorders, such as tachycardia and fibrillation, is under what circumstances does such a disorder arise? To answer to this question, we develop a multiaffine hybrid automaton (MHA) cardiac-cell model, and restate the original question as one of identification of the parameter ranges under which the MHA model accurately reproduces the disorder. The MHA model is obtained from the minimal cardiac model of one of the authors (Fenton) by first bringing it into the form of a canonical, genetic regulatory network, and then linearizing its sigmoidal switches, in an optimal way. By leveraging the Rovergene tool for genetic regulatory networks, we are then able to successfully identify the parameter ranges of interest.