Chapter

Computational Methods in Systems Biology

Part of the series Lecture Notes in Computer Science pp 342-361

Revisiting the Training of Logic Models of Protein Signaling Networks with ASP

  • Santiago VidelaAffiliated withCarnegie Mellon UniversityCNRS, UMR 6074 IRISAINRIA, Centre Rennes-Bretagne-Atlantique, Projet Dyliss
  • , Carito GuziolowskiAffiliated withCarnegie Mellon UniversityNational Center for Tumor Diseases, TIGA Center, University HeidelbergÉcole Centrale de Nantes, IRCCyN, UMR CNRS 6597
  • , Federica EduatiAffiliated withCarnegie Mellon UniversityDepartment of Information Engineering, University of PadovaEuropean Bioinformatics Institute (EMBL-EBI) Wellcome Trust Genome Campus
  • , Sven ThieleAffiliated withCarnegie Mellon UniversityCNRS, UMR 6074 IRISAINRIA, Centre Rennes-Bretagne-Atlantique, Projet DylissInstitute for Computer Science, University of Potsdam
  • , Niels GrabeAffiliated withCarnegie Mellon UniversityNational Center for Tumor Diseases, TIGA Center, University Heidelberg
  • , Julio Saez-RodriguezAffiliated withCarnegie Mellon UniversityEuropean Bioinformatics Institute (EMBL-EBI) Wellcome Trust Genome Campus
  • , Anne SiegelAffiliated withCarnegie Mellon UniversityCNRS, UMR 6074 IRISAINRIA, Centre Rennes-Bretagne-Atlantique, Projet Dyliss

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Abstract

A fundamental question in systems biology is the construction and training to data of mathematical models. Logic formalisms have become very popular to model signaling networks because their simplicity allows us to model large systems encompassing hundreds of proteins. An approach to train (Boolean) logic models to high-throughput phospho-proteomics data was recently introduced and solved using optimization heuristics based on stochastic methods. Here we demonstrate how this problem can be solved using Answer Set Programming (ASP), a declarative problem solving paradigm, in which a problem is encoded as a logical program such that its answer sets represent solutions to the problem. ASP has significant improvements over heuristic methods in terms of efficiency and scalability, it guarantees global optimality of solutions as well as provides a complete set of solutions. We illustrate the application of ASP with in silico cases based on realistic networks and data.

Keywords

Logic modeling answer set programming protein signaling networks