title: Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.
abstract: BACKGROUND
  Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions.
  
  
  RESULTS
  We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design.
  
  
  BACKGROUND
  caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/.
  
  
  BACKGROUND
  Supplementary materials are available at Bioinformatics online.
  
  
  BACKGROUND
  santiago.videla@irisa.fr.
authors.affiliation: École Centrale de Nantes, IRCCyN UMR CNRS 6597, 44321, Nantes, France.
authors.forename: Carito
authors.initials: C
authors.lastname: Guziolowski
authors.forename: Santiago
authors.initials: S
authors.lastname: Videla
authors.forename: Federica
authors.initials: F
authors.lastname: Eduati
authors.forename: Sven
authors.initials: S
authors.lastname: Thiele
authors.forename: Thomas
authors.initials: T
authors.lastname: Cokelaer
authors.forename: Anne
authors.initials: A
authors.lastname: Siegel
authors.forename: Julio
authors.initials: J
authors.lastname: Saez-Rodriguez
mesh_terms: D045744:Cell Line, Tumor; D006801:Humans; D008128:Logic; D040901:Proteomics; D015398:Signal Transduction; D012984:Software