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

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

DOI: 10.1007/978-3-642-33636-2_20

Volume 7605 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Videla S. et al. (2012) Revisiting the Training of Logic Models of Protein Signaling Networks with ASP. In: Gilbert D., Heiner M. (eds) Computational Methods in Systems Biology. Lecture Notes in Computer Science, vol 7605. Springer, Berlin, Heidelberg

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 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Santiago Videla
    • 1
    • 2
  • Carito Guziolowski
    • 3
    • 4
  • Federica Eduati
    • 5
    • 6
  • Sven Thiele
    • 2
    • 1
    • 7
  • Niels Grabe
    • 3
  • Julio Saez-Rodriguez
    • 6
  • Anne Siegel
    • 1
    • 2
  1. 1.CNRS, UMR 6074 IRISARennes CedexFrance
  2. 2.INRIA, Centre Rennes-Bretagne-Atlantique, Projet DylissRennes CedexFrance
  3. 3.National Center for Tumor Diseases, TIGA CenterUniversity HeidelbergGermany
  4. 4.École Centrale de Nantes, IRCCyN, UMR CNRS 6597Nantes cedex 3France
  5. 5.Department of Information EngineeringUniversity of PadovaPadovaItaly
  6. 6.European Bioinformatics Institute (EMBL-EBI) Wellcome Trust Genome CampusCambridgeUK
  7. 7.Institute for Computer ScienceUniversity of PotsdamGermany