Fusion of domain knowledge for dynamic learning in transcriptional networks

Oscar Harari, R. Romero-Zaliz, C. Rubio-Escudero, I. Zwir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 m-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 promoters sharing 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.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings
PublisherSpringer Verlag
Pages1067-1078
Number of pages12
ISBN (Print)3540454853, 9783540454854
DOIs
StatePublished - Jan 1 2006
Event7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 - Burgos, Spain
Duration: Sep 20 2006Sep 23 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4224 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006
CountrySpain
CityBurgos
Period09/20/0609/23/06

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    Harari, O., Romero-Zaliz, R., Rubio-Escudero, C., & Zwir, I. (2006). Fusion of domain knowledge for dynamic learning in transcriptional networks. In Intelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings (pp. 1067-1078). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4224 LNCS). Springer Verlag. https://doi.org/10.1007/11875581_127