title: Designing Experiments to Discriminate Families of Logic Models.
abstract: Logic models of signaling pathways are a promising way of building effective in silico functional models of a cell, in particular of signaling pathways. The automated learning of Boolean logic models describing signaling pathways can be achieved by training to phosphoproteomics data, which is particularly useful if it is measured upon different combinations of perturbations in a high-throughput fashion. However, in practice, the number and type of allowed perturbations are not exhaustive. Moreover, experimental data are unavoidably subjected to noise. As a result, the learning process results in a family of feasible logical networks rather than in a single model. This family is composed of logic models implementing different internal wirings for the system and therefore the predictions of experiments from this family may present a significant level of variability, and hence uncertainty. In this paper, we introduce a method based on Answer Set Programming to propose an optimal experimental design that aims to narrow down the variability (in terms of input-output behaviors) within families of logical models learned from experimental data. We study how the fitness with respect to the data can be improved after an optimal selection of signaling perturbations and how we learn optimal logic models with minimal number of experiments. The methods are applied on signaling pathways in human liver cells and phosphoproteomics experimental data. Using 25% of the experiments, we obtained logical models with fitness scores (mean square error) 15% close to the ones obtained using all experiments, illustrating the impact that our approach can have on the design of experiments for efficient model calibration.
authors.affiliation: UMR 6074 IRISA, CNRS, Campus de Beaulieu , Rennes , France ; Dyliss project, INRIA, Campus de Beaulieu , Rennes , France ; Institut für Informatik, Universität Potsdam , Potsdam , Germany ; LBSI, Fundación Instituto Leloir, CONICET , Buenos Aires , Argentina.
authors.forename: Santiago
authors.initials: S
authors.lastname: Videla
authors.affiliation: IRCCyN UMR CNRS 6597, École Centrale de Nantes , Nantes , France.
authors.forename: Irina
authors.initials: I
authors.lastname: Konokotina
authors.affiliation: Department of Mechanical Engineering, National Technical University of Athens , Athens , Greece.
authors.forename: Leonidas G
authors.initials: LG
authors.lastname: Alexopoulos
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute , Hinxton , UK.
authors.forename: Julio
authors.initials: J
authors.lastname: Saez-Rodriguez
authors.affiliation: Institut für Informatik, Universität Potsdam , Potsdam , Germany.
authors.forename: Torsten
authors.initials: T
authors.lastname: Schaub
authors.affiliation: UMR 6074 IRISA, CNRS, Campus de Beaulieu , Rennes , France ; Dyliss project, INRIA, Campus de Beaulieu , Rennes , France.
authors.forename: Anne
authors.initials: A
authors.lastname: Siegel
authors.affiliation: IRCCyN UMR CNRS 6597, École Centrale de Nantes , Nantes , France.
authors.forename: Carito
authors.initials: C
authors.lastname: Guziolowski
keywords: Boolean logic models; answer set programming; experimental design; phosphoproteomic; signaling networks