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An information theoretic method for reconstructing local regulatory network modules from polymorphic samples.

Manjunatha Jagalur1, David Kulp

  • 1Computational Biology Lab, University of Massachusetts Amherst, Amherst, MA 01002, USA. manju@cs.umass.edu

Computational Systems Bioinformatics. Computational Systems Bioinformatics Conference
|October 24, 2007
PubMed
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This study introduces a novel two-step method to construct gene regulatory networks by integrating gene expression and genotype data. The approach effectively identifies regulatory modules and causal relationships, offering insights into gene interactions.

Area of Science:

  • Genomics and Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Inferring gene regulatory relationships from genome-wide mRNA transcript levels is challenging, often requiring additional data and focusing on small gene subnetworks.
  • Previous work demonstrated a model integrating microarray expression and whole-genome genotype data to identify pairwise gene relationships.

Purpose of the Study:

  • To extend existing methodology for the principled construction of networks describing local gene regulatory modules.
  • To develop a method that infers causal relationships within gene regulatory networks.

Main Methods:

  • A two-step process was employed, starting with a seed gene.
  • A Markov Blanket was inferred using differential entropy estimation on genotype and gene expression data.

Related Experiment Videos

  • A Bayes Net was constructed from the inferred variables, incorporating biological constraints for causal accuracy.

    • Main Results:

    • Simulations showed that 45% of genes in a regulatory module could be identified with over 70% accuracy in recovering relationships.
    • Increasing sample size tenfold only doubled the recovery of true gene-gene relationships, indicating current experimental designs are viable.
    • Application to a real dataset of 111 back-crossed mice successfully recovered local gene regulatory networks supported by existing literature.

    Conclusions:

    • The developed method enables the construction of gene regulatory networks by integrating gene expression and genotype data.
    • The approach effectively identifies local regulatory modules and their causal relationships.
    • The findings suggest that useful gene networks can be achieved with current experimental sample sizes.