Optimization-based inference for temporally evolving Boolean networks with applications in biology

Young Hwan Chang, Joe Gray, Claire Tomlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Modeling of biological genetic networks forms the basis of systems biology. In this paper, we present an optimization-based inference scheme to identify temporally evolving Boolean network representations of genetic networks from data. In the formulation of the optimization problem, we use an adjacency map as a priori information, and define a cost function which both drives the connectivity of the graph to match biological data as well as generates a sparse and robust network at corresponding time intervals. Throughout simulation studies on simple examples, it is shown that this optimization scheme can help to understand the structure and dynamics of biological genetic networks.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Pages4129-4134
Number of pages6
StatePublished - Sep 29 2011
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2011 American Control Conference, ACC 2011
CountryUnited States
CitySan Francisco, CA
Period6/29/117/1/11

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Optimization-based inference for temporally evolving Boolean networks with applications in biology'. Together they form a unique fingerprint.

  • Cite this

    Chang, Y. H., Gray, J., & Tomlin, C. (2011). Optimization-based inference for temporally evolving Boolean networks with applications in biology. In Proceedings of the 2011 American Control Conference, ACC 2011 (pp. 4129-4134). [5991347] (Proceedings of the American Control Conference).