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

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  • Bree B Aldridge
  • Julio Saez-Rodriguez
  • Jeremy L Muhlich
  • Peter K Sorger
  • Douglas A Lauffenburger

Abstract

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. 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.

Suggested Citation

  • Bree B Aldridge & Julio Saez-Rodriguez & Jeremy L Muhlich & Peter K Sorger & Douglas A Lauffenburger, 2009. "Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling," PLOS Computational Biology, Public Library of Science, vol. 5(4), pages 1-13, April.
    • Handle: RePEc:plo:pcbi00:1000340
    DOI: 10.1371/journal.pcbi.1000340
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    References listed on IDEAS

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    1. Jeremiah J Faith & Boris Hayete & Joshua T Thaden & Ilaria Mogno & Jamey Wierzbowski & Guillaume Cottarel & Simon Kasif & James J Collins & Timothy S Gardner, 2007. "Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles," PLOS Biology, Public Library of Science, vol. 5(1), pages 1-13, January.
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    1. Marti Bernardo-Faura & Stefan Massen & Christine S Falk & Nathan R Brady & Roland Eils, 2014. "Data-Derived Modeling Characterizes Plasticity of MAPK Signaling in Melanoma," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-18, September.
    2. Laurence Calzone & Laurent Tournier & Simon Fourquet & Denis Thieffry & Boris Zhivotovsky & Emmanuel Barillot & Andrei Zinovyev, 2010. "Mathematical Modelling of Cell-Fate Decision in Response to Death Receptor Engagement," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-15, March.
    3. KyungOh Choi & Bassel Ghaddar & Colby Moya & Hai Shi & Gautham V Sridharan & Kyongbum Lee & Arul Jayaraman, 2014. "Analysis of Transcription Factor Network Underlying 3T3-L1 Adipocyte Differentiation," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-17, July.
    4. Andres Kriete & William J Bosl & Glenn Booker, 2010. "Rule-Based Cell Systems Model of Aging using Feedback Loop Motifs Mediated by Stress Responses," PLOS Computational Biology, Public Library of Science, vol. 6(6), pages 1-13, June.

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