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This article is part of the supplement: IEEE 7th International Conference on Bioinformatics and Bioengineering at Harvard Medical School

Open Access Research

Inferring gene regulatory networks by thermodynamic modeling

Chieh-Chun Chen1 and Sheng Zhong123*

Author Affiliations

1 Department of Bioengineering, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA

2 Department of Computer Science, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA

3 Department of Statistics, University of Illinois at Urbana Champaign, Champaign, IL 61820, USA

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BMC Genomics 2008, 9(Suppl 2):S19  doi:10.1186/1471-2164-9-S2-S19

Published: 16 September 2008

Abstract

Background

To date, the reconstruction of gene regulatory networks from gene expression data has primarily relied on the correlation between the expression of transcription regulators and that of target genes.

Results

We developed a network reconstruction method based on quantities that are closely related to the biophysical properties of TF-TF interaction, TF-DNA binding and transcriptional activation and repression. The Network-Identifier method utilized a thermodynamic model for gene regulation to infer regulatory relationships from multiple time course gene expression datasets. Applied to five datasets of differentiating embryonic stem cells, Network-Identifier identified a gene regulatory network among 87 transcription regulator genes. This network suggests that Oct4, Sox2 and Klf4 indirectly repress lineage specific differentiation genes by activating transcriptional repressors of Ctbp2, Rest and Mtf2.