Identification of genetic regulatory networks: A stochastic hybrid approach

Eugenio Cinquemani, Andreas Milias-Argeitis, John Lygeros

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

4 Citations (Scopus)

Abstract

Genetic regulatory networks are families of biochemically interacting genes that regulate most functions of a living cell via the synthesis of proteins and other essential molecules. In this paper we introduce a piecewise deterministic model of genetic network and devise a systematic procedure for the identification of the model parameters from experimental observations of the protein concentration dynamics. Numerical results on simulated data are presented to show the effectiveness of our method.

Original languageEnglish
Title of host publicationProceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
PublisherElsevier
ISBN (Print)9783902661005
Publication statusPublished - 1-Dec-2008
Externally publishedYes
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: 6-Jul-200811-Jul-2008

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1 PART 1
Volume17
ISSN (Print)1474-6670

Conference

Conference17th World Congress, International Federation of Automatic Control, IFAC
Country/TerritoryKorea, Republic of
CitySeoul
Period06/07/200811/07/2008

Keywords

  • Application of nonlinear analysis and design
  • Complex systems
  • Uncertainty descriptions

Fingerprint

Dive into the research topics of 'Identification of genetic regulatory networks: A stochastic hybrid approach'. Together they form a unique fingerprint.

Cite this