title: Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach.
abstract: 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.
authors.affiliation: ISI Foundation Viale Settimio Severo 65, Villa Gualino, I-10133 Torino, Italy.
authors.forename: Marc
authors.initials: M
authors.lastname: Bailly-Bechet
authors.forename: Alfredo
authors.initials: A
authors.lastname: Braunstein
authors.forename: Andrea
authors.initials: A
authors.lastname: Pagnani
authors.forename: Martin
authors.initials: M
authors.lastname: Weigt
authors.forename: Riccardo
authors.initials: R
authors.lastname: Zecchina
mesh_terms: D000465:Algorithms; D002453:Cell Cycle / Q000235:genetics; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics* / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism; D014158:Transcription, Genetic
chemical_list: D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors