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Estimating large-scale signaling networks through nested effect models with intervention effects from microarray

Holger Fröhlich1, Mark Fellmann, Holger Sültmann

  • 1German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de

Bioinformatics (Oxford, England)
|January 30, 2008
PubMed
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This study introduces module networks to infer large-scale gene signaling networks, extending previous work with a new statistical framework for RNA interference data analysis.

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • RNA interference (RNAi) and DNA microarrays enable reverse engineering of signaling cascades.
  • Previous statistical frameworks exist for scoring network hypotheses.

Purpose of the Study:

  • To extend existing statistical frameworks for inferring gene regulatory networks.
  • To incorporate prior knowledge into network inference.
  • To scale network inference to larger gene sets.

Main Methods:

  • Developed a module networks approach to infer large-scale gene networks (>30 genes).
  • Incorporated prior assumptions on network structure using prior distributions.
  • Utilized a beta-uniform mixture distribution on P-value profiles, avoiding data discretization.

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Main Results:

  • Successfully inferred a 13-gene signaling network in the ER-alpha pathway of MCF-7 breast cancer cells.
  • Module networks approach scales beyond the limitations of previous methods.
  • Results were validated through simulations and statistical stability assessments (bootstrapping, jackknife).

Conclusions:

  • The module networks approach provides a statistically stable and scalable method for inferring gene signaling networks.
  • The method allows for the incorporation of prior biological knowledge.
  • The approach is applicable to complex biological systems and available as an R package.