Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach

BMC Bioinformatics

Methodology Article

Marc Bailly-Bechet¹  Martin Weigt²  Andrea Pagnani²  Riccardo Zecchina³  Alfredo Braunstein³ 

[1] CNRS UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, F-69622, Villeurbanne, France;Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129, Torino, Italy;ISI Foundation Viale Settimio Severo 65, Villa Gualino, I-10133, Torino, Italy;Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129, Torino, Italy;Human Genetics Foundation, Via Nizza 230, I-10126, Torino, Italy;

关键词: Belief Propagation;    Coupling Vector;    Pleiotropic Drug Resistance;    Transcriptional Gene Regulation;    Marginal Probability Distribution;   

DOI  :  10.1186/1471-2105-11-355

 received in 2009-10-30, accepted in 2010-06-29,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundTranscriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle control, and the cellular response to variations in environmental conditions. Genes are regulated by transcription factors and other genes/proteins via a complex interconnection network. Such regulatory links may be predicted using microarray expression data, but most regulation models suppose transcription factor independence, which leads to spurious links when many genes have highly correlated expression levels.ResultsWe propose a new algorithm to infer combinatorial control networks from gene-expression data. Based on a simple model of combinatorial gene regulation, it includes a message-passing approach which avoids explicit sampling over putative gene-regulatory networks. This algorithm is shown to recover the structure of a simple artificial cell-cycle network model for baker's yeast. It is then applied to a large-scale yeast gene expression dataset in order to identify combinatorial regulations, and to a data set of direct medical interest, namely the Pleiotropic Drug Resistance (PDR) network.ConclusionsThe algorithm we designed is able to recover biologically meaningful interactions, as shown by recent experimental results [1]. Moreover, new cases of combinatorial control are predicted, showing how simple models taking this phenomenon into account can lead to informative predictions and allow to extract more putative regulatory interactions from microarray databases.

【 授权许可】

Unknown   
© Bailly-Bechet et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.