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

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 appli...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Marc Bailly-Bechet, Alfredo Braunstein, Andrea Pagnani, Martin Weigt, Riccardo Zecchina
• Keywords: BMCBI 2010, Combinatorial Control, Transcription Factor, Transcriptional Gene