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Bioinformatics Advance Access published online on September 22, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm465
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Inferring Transcriptional Regulatory Networks from High-throughput Data

Rui-Sheng Wang a,b,*, Yong Wang c, Xiang-Sun Zhang c and Luonan Chen b

aSchool of Information, Renmin University of China, Beijing 100872, China, bDepartment of Electronics, Information and Communication Engineering, Osaka Sangyo University, Osaka 574-8530, Japan, cAcademy of Mathematics and Systems Science, CAS, Beijing 100080, China

*To whom correspondence should be addressed. Dr. Ruisheng Wang, E-mail: wangrsh{at}amss.ac.cn


  Abstract

Motivation: Inferring the relationships between transcription factors (TFs) and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular systems. However, the transcription factor activities (TFAs) cannot be measured directly by standard microarray experiment owing to various posttranslational modifications. In particular, cooperative mechanism and combinatorial control are common in gene regulation, e.g. TFs usually recruit other proteins cooperatively to facilitate transcriptional reaction processes.

Results: In this paper, we propose a novel method for inferring transcriptional regulatory networks (TRN) from gene expression data based on protein transcription complexes and mass action law. With gene expression data and TFAs estimated from transcription complex information, the inference of TRN is formulated as a linear programming problem which has a globally optimal solution in terms ofL1 norm error. The proposed method not only can easily incorporate ChIP-Chip data as prior knowledge but also can integrate multiple gene expression datasets from different experiments simultaneously. A unique feature of our method is to take into account protein cooperation in transcription process. We tested our method by using both synthetic data and several experimental datasets in yeast. The extensive results illustrate the effectiveness of the proposed method for predicting transcription regulatory relationships between TFs with co-regulators and target genes.

Availability: The software TRNinfer is available from http://intelligent.eic.osaka-sandai.ac.jp/chenen/TRNinfer.htm.

Contact: chen{at}eic.osaka-sandai.ac.jp, zxs{at}amt.ac.cn

Associate Editor: Dr. Olga Troyanskaya

Received on June 25, 2007; revised on September 2, 2007; accepted on September 8, 2007

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