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

Young Hwan Chang, Joe Gray, Claire Tomlin

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

The problem of identifying dynamics of biological networks is of critical importance in order to understand biological systems. In this article, we propose a data-driven inference scheme to identify temporally evolving network representations of genetic networks. In the formulation of the optimization problem, we use an adjacency map as a priori information and define a cost function that both drives the connectivity of the graph to match biological data as well as generates a sparse and robust network at corresponding time intervals. Through simulation studies of simple examples, it is shown that this optimization scheme can help capture the topological change of a biological signaling pathway, and furthermore, might help to understand the structure and dynamics of biological genetic networks.

Original languageEnglish (US)
Pages (from-to)1307-1323
Number of pages17
JournalJournal of Computational Biology
Volume19
Issue number12
DOIs
StatePublished - Dec 1 2012

Keywords

  • gene regulatory networks
  • inference of dynamic models
  • temporally evolving networks

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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