Open Access

Inferring Time-Varying Network Topologies from Gene Expression Data

  • Arvind Rao1, 2Email author,
  • Alfred O HeroIII1, 2,
  • David J States2, 3 and
  • James Douglas Engel4
EURASIP Journal on Bioinformatics and Systems Biology20072007:51947

DOI: 10.1155/2007/51947

Received: 24 June 2006

Accepted: 17 February 2007

Published: 12 April 2007

Abstract

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster—to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

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Authors’ Affiliations

(1)
Department of Electrical Engineering and Computer Science, University of Michigan
(2)
Bioinformatics Graduate Program, Center for Computational Medicine and Biology, School of Medicine, University of Michigan
(3)
Department of Human Genetics, School of Medicine, University of Michigan
(4)
Department of Cell and Developmental Biology, School of Medicine, University of Michigan

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Copyright

© Arvind Rao et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.