A Hybrid Promoter Analysis Methodology for Prokaryotic Genomes

  • Oscar Harari
  • Luis Herrera
  • Igor Zwir
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 242)

Summary

One of the biggest challenges in genomics is the elucidation of the design principles controlling gene expression. Current approaches examine promoter sequences for particular features, such as the presence of binding sites for a transcriptional regulator, and identify recurrent relationships among these features termed network motifs. To define the expression dynamics of a group of genes, the strength of the connections in a network must be specified, and these are determined by the cis-promoter features participating in the regulation. Approaches that homogenize features among promoters (e.g., relying on consensuses to describe the various promoter features) and even across species hamper the discovery of the key differences that distinguish promoters that are co-regulated by the same transcriptional regulator. Thus, we have developed a model-based approach to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We applied our method to characterize network motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identify key features that enable the PhoP protein to produce distinct kinetic patterns in target genes, which could not have been uncovered just by inspecting network motifs.

Keywords

Transcription Factor Binding Site Network Motif Promoter Feature Feedforward Loop Position Weight Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Oscar Harari
    • 1
  • Luis Herrera
    • 1
  • Igor Zwir
    • 1
    • 2
  1. 1.Dept. Computer Science and Artificial IntelligenceUniversity of GranadaSpain
  2. 2.Howard Hughes Medical Institute, Department of Molecular MicrobiologyWashington University School of MedicineSt. LouisUSA

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