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      Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search

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          Abstract

          In biological systems, pathways define complex interaction networks where multiple molecular elements are involved in a series of controlled reactions producing responses to specific biomolecular signals. These biosystems are dynamic and there is a need for mathematical and computational methods able to analyze the symbolic elements and the interactions between them and produce adequate readouts of such systems. In this work, we use rewriting logic to analyze the cellular signaling of epidermal growth factor (EGF) and its cell surface receptor (EGFR) in order to induce cellular proliferation. Signaling is initiated by binding the ligand protein EGF to the membrane-bound receptor EGFR so as to trigger a reactions path which have several linked elements through the cell from the membrane till the nucleus. We present two different types of search for analyzing the EGF/proliferation system with the help of Pathway Logic tool, which provides a knowledge-based development environment to carry out the modeling of the signaling. The first one is a standard (forward) search. The second one is a novel approach based on narrowing, which allows us to trace backwards the causes of a given final state. The analysis allows the identification of critical elements that have to be activated to provoke proliferation.

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          Executable cell biology.

          Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models--mathematical and computational--which differ in their representations of biological phenomena. We call the approach of constructing computational models of biological systems 'executable biology', as it focuses on the design of executable computer algorithms that mimic biological phenomena. We survey the main modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research and highlight some of the challenges that executable biology poses for biology and computer science. We claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research. This will drive biology toward a more precise engineering discipline.
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            BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains.

            BioNetGen allows a user to create a computational model that characterizes the dynamics of a signal transduction system, and that accounts comprehensively and precisely for specified enzymatic activities, potential post-translational modifications and interactions of the domains of signaling molecules. The output defines and parameterizes the network of molecular species that can arise during signaling and provides functions that relate model variables to experimental readouts of interest. Models that can be generated are relevant for rational drug discovery, analysis of proteomic data and mechanistic studies of signal transduction.
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              Conditional rewriting logic as a unified model of concurrency

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                Author and article information

                Journal
                Biomed Res Int
                Biomed Res Int
                BMRI
                BioMed Research International
                Hindawi Publishing Corporation
                2314-6133
                2314-6141
                2017
                16 January 2017
                : 2017
                : 1809513
                Affiliations
                1Universidad Complutense de Madrid, Madrid, Spain
                2Bio and Health Informatics Lab, Seoul National University, Seoul, Republic of Korea
                3Cancer Research Center (CSIC/USAL) and IBSAL, Salamanca, Spain
                4Biosciences Division, SRI International, Menlo Park, CA, USA
                5University of Salamanca, Salamanca, Spain
                6Computer Science Laboratory, SRI International, Menlo Park, CA, USA
                Author notes
                *Gustavo Santos-García: santos@ 123456usal.es

                Academic Editor: Isabelle Bichindaritz

                Author information
                http://orcid.org/0000-0001-6609-5493
                Article
                10.1155/2017/1809513
                5278199
                28513565
                0b6d8a95-7fe5-46d5-98b9-163c65e688df
                Copyright © 2017 Adrián Riesco et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 5 August 2016
                : 30 October 2016
                Funding
                Funded by: National Institutes of Health
                Award ID: GM068146-01
                Award ID: CA112970-01
                Funded by: National Science Foundation
                Award ID: IIS-0513857
                Award ID: CNS-1318848
                Funded by: Spanish projects Strongsoft
                Award ID: TIN2012-39391-C04-04
                Award ID: TRACES TIN2015-67522-C3-3-R
                Award ID: PI12/00624
                Funded by: Comunidad de Madrid project N-Greens Software-CM
                Award ID: S2013/ICE-2731
                Categories
                Research Article

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