Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling Export

@article{citeulike:4271569, abstract = {Author Summary Cells use networks of interacting proteins to interpret intra-cellular state and extra-cellular cues and to execute cell-fate decisions. Even when individual proteins are well understood at a molecular level, the dynamics and behavior of networks as a whole are harder to understand. However, deciphering the operation of such networks is key to understanding disease processes and therapeutic opportunities. As a means to study signaling networks, we have modified and applied a fuzzy logic approach originally developed for industrial control. We use fuzzy logic to model the responses of colon cancer cells in culture to combinations of pro-survival and pro-death cytokines, making it possible to interpret quantitative data in the context of abstract information drawn from the literature. Our work establishes that fuzzy logic can be used to understand complex signaling pathways with respect to multi-factorial activity-based protein data and prior knowledge.}, author = {Aldridge, Bree B. and Saez-Rodriguez, Julio and Muhlich, Jeremy L. and Sorger, Peter K. and Lauffenburger, Douglas A.}, citeulike-article-id = {4271569}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000340}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19343194}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19343194}, doi = {10.1371/journal.pcbi.1000340}, journal = {PLoS Comput Biol}, keywords = {logic, systemsbiology}, month = {April}, number = {4}, pages = {e1000340+}, posted-at = {2009-07-04 17:14:19}, priority = {2}, publisher = {Public Library of Science}, title = {Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000340}, volume = {5}, year = {2009} }

TY - JOUR ID - citeulike:4271569 L3 - citeulike-article-id:4271569 N2 - Author Summary Cells use networks of interacting proteins to interpret intra-cellular state and extra-cellular cues and to execute cell-fate decisions. Even when individual proteins are well understood at a molecular level, the dynamics and behavior of networks as a whole are harder to understand. However, deciphering the operation of such networks is key to understanding disease processes and therapeutic opportunities. As a means to study signaling networks, we have modified and applied a fuzzy logic approach originally developed for industrial control. We use fuzzy logic to model the responses of colon cancer cells in culture to combinations of pro-survival and pro-death cytokines, making it possible to interpret quantitative data in the context of abstract information drawn from the literature. Our work establishes that fuzzy logic can be used to understand complex signaling pathways with respect to multi-factorial activity-based protein data and prior knowledge. IS - 4 JF - PLoS Comput Biol TI - Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling PB - Public Library of Science VL - 5 SP - e1000340 KW - logic KW - systemsbiology AU - Aldridge, Bree AU - Saez-Rodriguez, Julio AU - Muhlich, Jeremy AU - Sorger, Peter AU - Lauffenburger, Douglas PY - 2009/04/03/ UR - http://dx.doi.org/10.1371/journal.pcbi.1000340 ER -