title: cDREM: inferring dynamic combinatorial gene regulation.
abstract: Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.
authors.affiliation: Lane Center for Computational Biology, Carnegie Mellon University , Pittsburgh, Pennsylvania.
authors.forename: Aaron
authors.initials: A
authors.lastname: Wise
authors.forename: Ziv
authors.initials: Z
authors.lastname: Bar-Joseph
mesh_terms: D047369:Chromatin Immunoprecipitation; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D006801:Humans; D007251:Influenza, Human / Q000276:immunology / Q000378:metabolism; D008390:Markov Chains; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D012984:Software; D013312:Stress, Physiological; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors
keywords: HMM; computational molecular biology; gene chips; gene expression; gene networks; machine learning; regulatory networks
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