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From Cardiac Cells to Genetic Regulatory Networks

  • Radu Grosu
  • Gregory Batt
  • Flavio H. Fenton
  • James Glimm
  • Colas Le Guernic
  • Scott A. Smolka
  • Ezio Bartocci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6806)

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.

Keywords

Cardiac Cell Action Potential Duration Linear Temporal Logic Spiral Wave Cardiac Disorder 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Radu Grosu
    • 1
  • Gregory Batt
    • 2
  • Flavio H. Fenton
    • 3
  • James Glimm
    • 4
  • Colas Le Guernic
    • 5
  • Scott A. Smolka
    • 1
  • Ezio Bartocci
    • 1
    • 4
  1. 1.Dept. of Comp. Sci.Stony Brook UniversityStony BrookUSA
  2. 2.INRIALe Cesnay CedexFrance
  3. 3.Dept. of Biomed. Sci.Cornell UniversityIthacaUSA
  4. 4.Dept. of Appl. Math. and Sta.Stony Brook UniversityStony BrookUSA
  5. 5.Dept. of Comp. Sci.New York UniversityNew YorkUSA

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