Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns. Journal   Proceedings of the National Academy of Sciences of the United States of America. Citation   Proc Natl Acad Sci U S A. 103(50):19033-8 Publication date   2006 Dec 12 Authors   Lezon TR Banavar JR Cieplak M Maritan A Fedoroff NV Investigators   Jayanth Banavar Nina Fedoroff MeSH headings   Entropy Gene Expression MeSH qualifiers   genetics 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.