Biological and biomedical researchers must now contend with an unprecedented accumulation of genomic, proteomic, and biological data. This information, which has created the ability to investigate biochemical pathways in the context of large networks of molecules, will profoundly influence the direction of 21st century biomedicine. For example, although thousands of possible therapeutic targets will be identified, conventional pharmaceutical approaches will encounter serious problems in the search for effective new drugs. One important problem is that human reasoning alone is often unable to predict the consequences of perturbing the function of one component in a complex biological network. Therefore, a compelling need exists for powerful computational tools for use in effectively and efficiently reasoning about these networks and for generating experimentally testable hypotheses. Such tools will greatly augment the ability of researchers to understand biological systems. Researchers here at SRI International have developed unique computational tools based on formal methods a collection of techniques derived from mathematical logic that are widely applied to evaluate complex computer systems. Here, we describe the application of these tools to the creation of an executable, predictive model of a medically important biological signaling system: the transmission of signals by the epidermal growth factor receptor (EGFR) in a cultured cell type. Our computational approach, called Pathway Logic, is designed to automatically generate and display the network states of interactions possible when the EGFR is activated and to permit researchers to easily ask logical questions about this complex network. Pathway Logic will not create simulations of biological processes, but will generate large formal models derived entirely from experimental findings that can be edited and queried. In the Phase I or R21 part of the project, we will establish the conventions (symbols and rules) required to construct and run this early model. Moreover, we will develop and refine a graphical viewer that allows the results of in silico experiments involving the EGFR network to be usefully and intuitively displayed. In the Phase II or R33 part, we will increase the complexity and detail of the model so it includes conventions for other signaling systems embedded in the EGFR network, and we will perform laboratory studies to test predictions generated by the model about EGFR signaling processes in cultured cells. In addition, we will develop an intuitive graphical user interface that can be used to directly create, edit, and query Pathway Logic models as well as display the results of in silico experiments in a variety of representations.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Exploratory/Developmental Grants Phase II (R33)
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Special Emphasis Panel (ZRG1-SSS-H (90))
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Lyster, Peter
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Sri International
Menlo Park
United States
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Riesco, Adrián; Santos-Buitrago, Beatriz; De Las Rivas, Javier et al. (2017) Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search. Biomed Res Int 2017:1809513