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

TitleInference of sparse combinatorial-control networks from gene-expression data: a message passing approach.
Publication TypeJournal Article
Year of Publication2010
AuthorsBailly-Bechet, M, Braunstein, A, Pagnani, A, Weigt, M, Zecchina, R
JournalBMC Bioinformatics
Date Published2010
KeywordsAlgorithms, Cell Cycle, Gene Expression Profiling, Gene Expression Regulation, Fungal, Gene Regulatory Networks, Models, Genetic, Oligonucleotide Array Sequence Analysis, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Transcription Factors, Transcription, Genetic

BACKGROUND: 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.RESULTS: 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.CONCLUSIONS: 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.

Alternate JournalBMC Bioinformatics
PubMed ID20587029
PubMed Central IDPMC2909222