TY - JOUR A1 - Videla, Santiago A1 - Guziolowski, Carito A1 - Eduati, Federica A1 - Thiele, Sven A1 - Gebser, Martin A1 - Nicolas, Jacques A1 - Saez-Rodriguez, Julio A1 - Schaub, Torsten H. A1 - Siegel, Anne T1 - Learning Boolean logic models of signaling networks with ASP JF - Theoretical computer science N2 - Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge network and the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in silico numerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A-B. (C) 2014 Elsevier B.V. All rights reserved. KW - Answer set programming KW - Signaling transduction networks KW - Boolean logic models KW - Combinatorial multi-objective optimization KW - Systems biology Y1 - 2015 U6 - https://doi.org/10.1016/j.tcs.2014.06.022 SN - 0304-3975 SN - 1879-2294 VL - 599 SP - 79 EP - 101 PB - Elsevier CY - Amsterdam ER - TY - GEN A1 - Razzaq, Misbah A1 - Kaminski, Roland A1 - Romero, Javier A1 - Schaub, Torsten H. A1 - Bourdon, Jeremie A1 - Guziolowski, Carito T1 - Computing diverse boolean networks from phosphoproteomic time series data T2 - Computational Methods in Systems Biology N2 - Logical modeling has been widely used to understand and expand the knowledge about protein interactions among different pathways. Realizing this, the caspo-ts system has been proposed recently to learn logical models from time series data. It uses Answer Set Programming to enumerate Boolean Networks (BNs) given prior knowledge networks and phosphoproteomic time series data. In the resulting sequence of solutions, similar BNs are typically clustered together. This can be problematic for large scale problems where we cannot explore the whole solution space in reasonable time. Our approach extends the caspo-ts system to cope with the important use case of finding diverse solutions of a problem with a large number of solutions. We first present the algorithm for finding diverse solutions and then we demonstrate the results of the proposed approach on two different benchmark scenarios in systems biology: (1) an artificial dataset to model TCR signaling and (2) the HPN-DREAM challenge dataset to model breast cancer cell lines. KW - Diverse solution enumeration KW - Answer set programming KW - Boolean Networks KW - Model checking KW - Time series data Y1 - 2018 SN - 978-3-319-99429-1 SN - 978-3-319-99428-4 U6 - https://doi.org/10.1007/978-3-319-99429-1_4 SN - 0302-9743 SN - 1611-3349 VL - 11095 SP - 59 EP - 74 PB - Springer CY - Berlin ER - TY - JOUR A1 - Ostrowski, Max A1 - Pauleve, L. A1 - Schaub, Torsten H. A1 - Siegel, A. A1 - Guziolowski, Carito T1 - Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming JF - Biosystems : journal of biological and information processing sciences N2 - Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7 min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models. (C) 2016 Elsevier Ireland Ltd. All rights reserved. KW - Model identification KW - Time series data KW - Multiplex phosphoproteomics data KW - Boolean networks KW - Answer Set Programming Y1 - 2016 U6 - https://doi.org/10.1016/j.biosystems.2016.07.009 SN - 0303-2647 SN - 1872-8324 VL - 149 SP - 139 EP - 153 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Guziolowski, Carito A1 - Videla, Santiago A1 - Eduati, Federica A1 - Thiele, Sven A1 - Cokelaer, Thomas A1 - Siegel, Anne A1 - Saez-Rodriguez, Julio T1 - Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming JF - Bioinformatics N2 - 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. Y1 - 2013 U6 - https://doi.org/10.1093/bioinformatics/btt393 SN - 1367-4803 VL - 29 IS - 18 SP - 2320 EP - 2326 PB - Oxford Univ. Press CY - Oxford ER -