Revisiting the training of logic models of protein signaling networks with ASP

  • Authors:

  • Santiago Videla;Carito Guziolowski;Federica Eduati;Sven Thiele;Niels Grabe;Julio Saez-Rodriguez;Anne Siegel

  • Affiliations:

  • CNRS, UMR 6074 IRISA, Rennes Cedex, France,INRIA, Centre Rennes-Bretagne-Atlantique, Projet Dyliss, Rennes Cedex, France;National Center for Tumor Diseases, TIGA Center, University Heidelberg, Germany,École Centrale de Nantes, IRCCyN, UMR CNRS 6597, Nantes cedex 3, France;Department of Information Engineering, University of Padova, Padova, Italy,European Bioinformatics Institute (EMBL-EBI) Wellcome Trust Genome Campus, Cambridge, UK;INRIA, Centre Rennes-Bretagne-Atlantique, Projet Dyliss, Rennes Cedex, France,CNRS, UMR 6074 IRISA, Rennes Cedex, France,Institute for Computer Science, University of Potsdam, Germany;National Center for Tumor Diseases, TIGA Center, University Heidelberg, Germany;European Bioinformatics Institute (EMBL-EBI) Wellcome Trust Genome Campus, Cambridge, UK;CNRS, UMR 6074 IRISA, Rennes Cedex, France,INRIA, Centre Rennes-Bretagne-Atlantique, Projet Dyliss, Rennes Cedex, France

  • Venue:
  • CMSB'12 Proceedings of the 10th international conference on Computational Methods in Systems Biology
  • Year:
  • 2012

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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.