Fusion of Domain Knowledge for Dynamic Learning in Transcriptional Networks
A critical challenge of the postgenomic era is to understand how genes are differentially regulated even when they belong to a given network. Because the fundamental mechanism controlling gene expression operates at the level of transcription initiation, computational techniques have been developed that identify cis-regulatory features and map such features into differential expression patterns. The fact that such co-regulated genes may be differentially regulated suggests that subtle differences in the shared cis-acting regulatory elements are likely significant. Thus, we carry out an exhaustive description of cis-acting regulatory features including the orientation, location and number of binding sites for a regulatory protein, the presence of binding site submotifs, the class and number of RNA polymerase sites, as well as gene expression data, which is treated as one feature among many. These features, derived from different domain sources, are analyzed concurrently, and dynamic relations are recognized to generate profiles, which are groups of promoterssharing common features. We apply this method to probe the regulatory networks governed by the PhoP/PhoQ two-component system in the enteric bacteria Escherichia coli and Salmonella enterica. Our analysis uncovered novel members of the PhoP regulon as and the resulting profiles group genes that share underlying biological that characterize the system kinetics. The predictions were experimentally validated to establish that the PhoP protein uses multiple mechanisms to control gene transcription and is a central element in a highly connected network.
KeywordsTranscriptional Network Dynamic Learn Howard Hughes Medical Institute Position Weight Matrice Control Gene Transcription
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