title: Inferring combinatorial regulation of transcription in silico.
abstract: In this paper, we propose a functional view on the in silico prediction of transcriptional regulation. We present a method to predict biological functions regulated by a combinatorial interaction of transcription factors. Using a rigorous statistic, this approach intersects the presence of transcription factor binding sites in gene upstream sequences with Gene Ontology terms associated with these genes. We demonstrate that for the well-studied set of skeletal muscle-related transcription factors Myf-2, Mef and TEF, the correct functions are predicted. Furthermore, starting from the well-characterized promoter of a gene expressed upon lipopolysaccharide stimulation, we predict functional targets of this stimulus. These results are in excellent agreement with microarray data.
authors.affiliation: Humboldt University, Institute for Theoretical Biology Invalidenstrasse 43, 10115 Berlin, Germany.
authors.forename: Nils
authors.initials: N
authors.lastname: Blüthgen
authors.forename: Szymon M
authors.initials: SM
authors.lastname: Kiełbasa
authors.forename: Hanspeter
authors.initials: H
authors.lastname: Herzel
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D023281:Genomics / Q000379:methods*; D006801:Humans; D008070:Lipopolysaccharides / Q000494:pharmacology; D018482:Muscle, Skeletal / Q000378:metabolism; D018006:Myogenic Regulatory Factors / Q000378:metabolism; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*; D015533:Transcriptional Activation
chemical_list: D008070:Lipopolysaccharides; D018006:Myogenic Regulatory Factors; D014157:Transcription Factors