Inferring Time-Varying Network Topologies from Gene Expression Data. (12th April 2007)

• Record Type: Journal Article
• Authors: Rao, Arvind; Hero III, Alfred O.; States, David J.; Engel, James Douglas
• Other Names: Dougherty Edward R. Academic Editor.
• Is Part Of: EURASIP journal on bioinformatics and systems biology. Volume 2007(2007)
• Publication Date: 2007-04-12
• Subjects: Bioinformatics -- Periodicals; Systems biology -- Periodicals; Systems Biology; Signal Processing, Computer-Assisted; Bio-informatique; Biologie systémique; Bioinformatics; Systems biology; Systems Biology; Bioinformatics; Electronic journals; Periodical; Fulltext; Internet Resources; Periodicals; Periodicals; 570.285
• DOI: 10.1155/2007/51947
• Languages: English
• ISSNs: 1687-4145

Abstract

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.