Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling.

TitleFuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling.
Publication TypeJournal Article
Year of Publication2009
AuthorsAldridge, BB, Saez-Rodriguez, J, Muhlich, JL, Sorger, PK, Lauffenburger, DA
JournalPLoS Comput Biol
Date Published2009 Apr
KeywordsCell Line, Tumor, Colonic Neoplasms, Computer Simulation, Epidermal Growth Factor, Fuzzy Logic, Humans, Models, Biological, Phosphotransferases, Receptor, Insulin, Signal Transduction, Tumor Necrosis Factor-alpha

When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors in human colon carcinoma cells. Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation. We uncover a relationship between MK2 and ERK pathways that might account for the previously identified pro-survival influence of MK2. We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling. More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks.

Alternate JournalPLoS Comput. Biol.
PubMed ID19343194
PubMed Central IDPMC2663056
Grant ListCA112967 / CA / NCI NIH HHS / United States
GM68762 / GM / NIGMS NIH HHS / United States
P50 GM068762-06 / GM / NIGMS NIH HHS / United States

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