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Reconciling gene expression data with known genome-scale regulatory network structures.

Markus J Herrgård1, Markus W Covert, Bernhard Ø Palsson

  • 1Department of Bioengineering, Bioinformatics Graduate Program, University of California, San Diego, La Jolla, California 92093-0412, USA.

Genome Research
|October 16, 2003
PubMed
Summary

This study reconciles gene expression data with known transcriptional regulatory networks in E. coli and yeast. Findings show network element consistency depends on structure and gene function, enabling refinement of regulatory networks.

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Area of Science:

  • Systems Biology
  • Genomics
  • Bioinformatics

Background:

  • Genome-scale gene expression data enables inference of transcriptional regulatory networks.
  • Regulatory network structures can also be reconstructed from genomic information and literature.

Purpose of the Study:

  • To reconcile gene expression data with known genome-wide regulatory network structures.
  • To examine the consistency between these two data types in Escherichia coli and Saccharomyces cerevisiae.

Main Methods:

  • Decomposition of regulatory networks into basic network elements.
  • Computation of local consistency for each network element instance.
  • Analysis of structural features and functional gene classes influencing consistency.

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Main Results:

  • Network element consistency is influenced by the number of regulators and gene functional classes.
  • A method was developed to define regulatory network subcomponents with high consistency.
  • Identified specific network subcomponents with high consistency between structure and expression data.

Conclusions:

  • Gene expression data can refine and expand known regulatory network subcomponents.
  • The approach allows for a more accurate understanding of transcriptional regulation.
  • Highlights the interplay between network topology and gene function in regulatory processes.