Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming

Santiago Videla (1) , Carito Guziolowski (2, 3) , Federica Eduati (4, 5) , Sven Thiele (1, 6) , Niels Grabe (2) , Julio Saez-Rodriguez (5) , Anne Siegel (1)

1 Dyliss - Dynamics, Logics and Inference for biological Systems and Sequences
2 Bioquant
3 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes
4 DEI - Dipartimento di Ingegneria de l'Informazione [Padova]
5 EMBL-EBI - European Bioinformatics Institute [Hinxton]
6 Institut für informatik [Potsdam]

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.

Domaines: Bio-informatique [q-bio.QM]; Bio-Informatique, Biologie Systémique [q-bio.QM]; Apprentissage [cs.LG]; Modélisation et simulation

Santiago Videla, Carito Guziolowski, Federica Eduati, Sven Thiele, Niels Grabe, et al.. Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming. CMSB - 10th Computational Methods in Systems Biology 2012, D. Gilbert and M. Heiner, Oct 2012, London, United Kingdom. pp.342-361, ⟨10.1007/978-3-642-33636-2_20⟩. ⟨hal-00737112v2⟩