Open Access Methodology article

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

Marc Bailly-Bechet12, Alfredo Braunstein23, Andrea Pagnani4*, Martin Weigt4 and Riccardo Zecchina23

Author Affiliations

1 Université Lyon 1; CNRS UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622, Villeurbanne, France

2 Politecnico di Torino, Duca degli Abruzzi 24, I-10129 Torino, Italy

3 Human Genetics Foundation, Via Nizza 230, I-10126 Torino, Italy

4 ISI Foundation Viale Settimio Severo 65, Villa Gualino, I-10133 Torino, Italy

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BMC Bioinformatics 2010, 11:355  doi:10.1186/1471-2105-11-355

Published: 29 June 2010



Transcriptional 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.


We 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.


The 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.