Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming

Abstract

Motivation: 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.Availability: caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/.Supplementary information: Supplementary materials are available at Bioinformatics online.Contact: santiago.videla@irisa.fr

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

“…Model checking and symbolic reasoning have been used to verify properties of manually constructed biological models , complete partially specified pathways using perturbation data (Köksal et al, 2013), and synthesize gene regulatory networks directly from data (Dunn et al, 2014;Moignard et al, 2015) (reviewed in ). In addition, other types of declarative approaches, such as integer programming (Budak et al, 2015;Chasman et al, 2014;Jain et al, 2016;Ourfali et al, 2007;Sharan and Karp, 2013;Silverbush and Sharan, 2014) and answer set programming (Guziolowski et al, 2013), have been applied to biological pathway analysis. The TPS model summarization strategy, which makes it applicable to comprehensive signaling networks containing more than a hundred thousand edges and phosphosites, sets it apart from these related methods (Supplemental Results and Figure S15).…”

Treewidth and Counting Projected Answer Sets

“…Answer Set Programming (ASP) [10] is an active research area of artificial intelligence. It provides a logic-based declarative modelling language and problem solving framework [24] for hard computational problems, which has been widely applied [2,27,40,41]. In ASP, questions are encoded into rules and constraints that form a disjunctive (logic) program over atoms.…”

Learning Boolean logic models of signaling networks with ASP

“…Although not shown in this work, we have found that the variability of input-output behaviors is significantly lower than models topologies. Recent advances in this direction and considering real-world experimental data, can be found in [30]. Briefly, in the aforecited work it is shown that if the experimental error is considered, several thousands of Boolean logic models fit the available data similarly well.…”