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

@article{citeulike:973077, abstract = {10.1073/pnas.0609152103 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 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.}, address = {Department of Physics, 104 Davey Laboratory, and Department of Biology and Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802; Santa Fe Institute, Santa Fe, NM 87501; Institute of Physics, Polish Academy of Science, Aleja Lotnikow 32/48, 02-668 Warsaw, Poland.}, author = {Lezon, Timothy R. and Banavar, Jayanth R. and Cieplak, Marek and Maritan, Amos and Fedoroff, Nina V.}, citeulike-article-id = {973077}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0609152103}, citeulike-linkout-1 = {http://www.pnas.org/content/103/50/19033.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/103/50/19033.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/17138668}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=17138668}, day = {12}, doi = {10.1073/pnas.0609152103}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences}, keywords = {analysis, grn}, month = {December}, number = {50}, pages = {19033--19038}, posted-at = {2009-10-13 13:01:34}, priority = {2}, title = {Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns}, url = {http://dx.doi.org/10.1073/pnas.0609152103}, volume = {103}, year = {2006} }

TY - JOUR ID - citeulike:973077 L3 - citeulike-article-id:973077 N2 - 10.1073/pnas.0609152103 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 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. IS - 50 JF - Proceedings of the National Academy of Sciences SN - 0027-8424 AD - Department of Physics, 104 Davey Laboratory, and Department of Biology and Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802; Santa Fe Institute, Santa Fe, NM 87501; Institute of Physics, Polish Academy of Science, Aleja Lotnikow 32/48, 02-668 Warsaw, Poland. EP - 19038 TI - Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns VL - 103 SP - 19033 KW - analysis KW - grn AU - Lezon, Timothy AU - Banavar, Jayanth AU - Cieplak, Marek AU - Maritan, Amos AU - Fedoroff, Nina PY - 2006/12/12/ UR - http://dx.doi.org/10.1073/pnas.0609152103 ER -