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. 2007 Apr 12;2007(1):51947. doi: 10.1155/2007/51947

Inferring Time-Varying Network Topologies from Gene Expression Data

Arvind Rao 1,2,✉, Alfred O Hero III 1,2, David J States 2,3, James Douglas Engel 4
PMCID: PMC3171343  PMID: 18309363

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.

[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]

Contributor Information

Arvind Rao, Email: ukarvind@umich.edu.

Alfred O Hero, III, Email: hero@umich.edu.

David J States, Email: dstates@umich.edu.

James Douglas Engel, Email: engel@umich.edu.

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