Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

Proc Natl Acad Sci U S A. 2006 Dec 12;103(50):19033-8. doi: 10.1073/pnas.0609152103. Epub 2006 Nov 30.

Abstract

We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Computational Biology
  • Computer Simulation
  • Entropy*
  • Gene Expression / genetics*
  • Oligonucleotide Array Sequence Analysis
  • Saccharomyces cerevisiae / genetics