title: A model-based approach to transcription regulatory network reconstruction from time-course gene expression data.
abstract: Time-course gene expression profiling provides valuable data on dynamic behavior of cellular responses to external stimulation. Investigation of transcription factors (TFs) that regulate co-expressed genes in a dynamic process can reveal insights on the underlying molecular mechanisms. As the ChIP-seq technology is only suitable for a fraction of TFs in mammalian organisms, the computational identification of relevant TFs remains to be critical. We propose a regression-based model to infer the functional binding sites of TFs from time-course gene expression profiles. Our approach incorporates an association strength for each potential TF and target gene pair based on computational analysis of binding sites in promoter sequences of co-expressed genes. Our model further uses the Lasso-penalized technique to search for the most informative TF-target pairs. The application of our method to a gene expression study on E2-induced apoptosis in a variant of MCF-7 cells revealed that the findings are biologically meaningful.
authors.forename: Hong
authors.initials: H
authors.lastname: Hu
authors.forename: Yang
authors.initials: Y
authors.lastname: Dai
mesh_terms: D000465:Algorithms; D000818:Animals; D017209:Apoptosis / Q000187:drug effects; D001665:Binding Sites; D030541:Databases, Genetic; D004958:Estradiol / Q000494:pharmacology; D004967:Estrogens / Q000494:pharmacology; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects; D053263:Gene Regulatory Networks; D006801:Humans; D016015:Logistic Models; D061986:MCF-7 Cells; D008957:Models, Genetic; D011401:Promoter Regions, Genetic; D013997:Time Factors; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism
chemical_list: D004967:Estrogens; D014157:Transcription Factors; D004958:Estradiol