TY - JOUR T1 - Protein Networks as Logic Functions in Development and Cancer A1 - Dutkowski, Janusz A1 - Ideker, Trey Y1 - 2011/09/29 N2 - Author Summary Biological outcomes are often determined by modules of proteins working in combination. In classic biological studies, these modules have been shown to encode a diverse repertoire of logic functions which provide the means to express complex regulatory programs using a limited number of proteins. Here, we integrate gene expression profiles and physical protein interaction maps to provide a systematic and global view of combinatorial network modules underlying representative developmental and cancer programs. We develop a new method that associates decision trees with concise network regions to identify network decision modules predictive of biological or clinical outcome. The resulting network signatures prove robust across different sample cohorts and capture causal mechanisms of development or disease. Furthermore, we find that the most predictive network decision functions rely on both coherent and opposing gene activities. Notably, in cancer progression the predictive gene associations often map to physical interactions between known oncogenes and tumor suppressors, where the combined activity of these genes determines disease outcome. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 7 IS - 9 UR - https://doi.org/10.1371/journal.pcbi.1002180 SP - e1002180 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002180 ER -