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Related Experiment Videos

Inferring subnetworks from perturbed expression profiles.

D Pe'er1, A Regev, G Elidan

  • 1School of Computer Science & Engineering, Hebrew University, Jerusalem, 91904, Israel. danab@cs.huji.ac.il

Bioinformatics (Oxford, England)
|July 27, 2001
PubMed
Summary
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This study reveals gene interactions like causality and inhibition using Bayesian networks. The method identifies complex gene networks in yeast, uncovering metabolic and signaling pathways.

Area of Science:

  • Genomics and Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Genome-wide expression profiling measures cellular responses to genetic perturbations.
  • Standard analysis identifies affected genes and clusters them by function.
  • Finer-scale gene interactions remain largely uncharacterized.

Purpose of the Study:

  • To develop a Bayesian network framework for analyzing gene expression data.
  • To identify causal, mediating, activating, and inhibiting relationships between genes.
  • To uncover significant subnetworks and pathways in cellular systems.

Main Methods:

  • Utilized a Bayesian network framework to model gene interactions.
  • Extended the framework to accommodate perturbation data.

Related Experiment Videos

  • Applied the method to genome-wide expression profiles of S. cerevisiae mutants.

    • Main Results:

    • Discovered a detailed structure of gene interactions, including causality and inhibition.
    • Identified significant subnetworks of interacting genes.
    • Uncovered structured metabolic, signaling, and regulatory pathways in yeast.

    Conclusions:

    • The Bayesian network approach provides a powerful tool for dissecting complex gene interactions.
    • This method enhances the understanding of cellular responses to perturbations.
    • Revealed intricate pathway structures in S. cerevisiae.