From Cardiac Cells to Genetic Regulatory Networks.*

R. Grosu, G. Batt, F. Fenton, J. Glimm, C. Le Guernic, S.A. Smolka and E. Bartocci.

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 identication 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.

In Proc. of CAV'11, the 23rd International Conference on Computer Aided Verification, Cliff Lodge, Snowbird, Utah, USA, July, 2011, Springer, LNCS.

*This work was supported by the NSF Faculty Early Career Development Award CCR01-33583, the NSF Expeditions Award CNS-09-26190, the NSF CSR-AES05-09230 Award and the AFOSR FA-0550-09-1-0481 Award.