Local identification of piecewise deterministic models of genetic networks

Eugenio Cinquemani*, Andreas Milias-Argeitis, Sean Summers, John Lygeros

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

14 Citations (Scopus)

Abstract

We address the identification of genetic networks under stationary conditions. A stochastic hybrid description of the genetic interactions is considered and an approximation of it in stationary conditions is derived. Contrary to traditional structure identification methods based on fitting deterministic models to several perturbed equilibria of the system, we set up an identification strategy which exploits randomness as an inherent perturbation of the system. Estimation of the dynamics of the system from sampled data under stability constraints is then formulated as a convex optimization problem. Numerical results are shown on an artificial genetic network model. While our methods are conceived for the identification of interaction networks, they can as well be applied in the study of general piecewise deterministic systems with randomly switching inputs.

Original languageEnglish
Title of host publicationHybrid Systems: Computation and Control - 12th International Conference, HSCC 2009, Proceedings
Pages105-119
Number of pages15
Volume5469
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event12th International Conference on Hybrid Systems: Computation and Control, HSCC 2009 - San Francisco, CA, United States
Duration: 13-Apr-200915-Apr-2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5469
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference12th International Conference on Hybrid Systems: Computation and Control, HSCC 2009
Country/TerritoryUnited States
CitySan Francisco, CA
Period13/04/200915/04/2009

Keywords

  • Convex optimization
  • Markov processes
  • Piecewise deterministic systems
  • Sampled systems
  • State-space identification

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