@article{GuziolowskiVidelaEduatietal.2013,
  author    = {Guziolowski, Carito and Videla, Santiago and Eduati, Federica and Thiele, Sven and Cokelaer, Thomas and Siegel, Anne and Saez-Rodriguez, Julio},
  title     = {Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming},
  series = {Bioinformatics},
  volume    = {29},
  journal   = {Bioinformatics},
  number    = {18},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1367-4803},
  doi       = {10.1093/bioinformatics/btt393},
  pages     = {2320 -- 2326},
  year      = {2013},
  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.},
  language  = {en}
}
@article{KaminskiSchaubSiegeletal.2013,
  author    = {Kaminski, Roland and Schaub, Torsten H. and Siegel, Anne and Videla, Santiago},
  title     = {Minimal intervention strategies in logical signaling networks with ASP},
  series = {Theory and practice of logic programming},
  volume    = {13},
  journal   = {Theory and practice of logic programming},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068413000422},
  pages     = {675 -- 690},
  year      = {2013},
  abstract  = {Proposing relevant perturbations to biological signaling networks is central to many problems in biology and medicine because it allows for enabling or disabling certain biological outcomes. In contrast to quantitative methods that permit fine-grained (kinetic) analysis, qualitative approaches allow for addressing large-scale networks. This is accomplished by more abstract representations such as logical networks. We elaborate upon such a qualitative approach aiming at the computation of minimal interventions in logical signaling networks relying on Kleene's three-valued logic and fixpoint semantics. We address this problem within answer set programming and show that it greatly outperforms previous work using dedicated algorithms.},
  language  = {en}
}
@article{VidelaGuziolowskiEduatietal.2015,
  author    = {Videla, Santiago and Guziolowski, Carito and Eduati, Federica and Thiele, Sven and Gebser, Martin and Nicolas, Jacques and Saez-Rodriguez, Julio and Schaub, Torsten H. and Siegel, Anne},
  title     = {Learning Boolean logic models of signaling networks with ASP},
  series = {Theoretical computer science},
  volume    = {599},
  journal   = {Theoretical computer science},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-3975},
  doi       = {10.1016/j.tcs.2014.06.022},
  pages     = {79 -- 101},
  year      = {2015},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Videla2014,
  author    = {Videla, Santiago},
  title     = {Reasoning on the response of logical signaling networks with answer set programming},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-71890},
  school      = {Universit{\"a}t Potsdam},
  year      = {2014},
  abstract  = {Deciphering the functioning of biological networks is one of the central tasks in systems biology. In particular, signal transduction networks are crucial for the understanding of the cellular response to external and internal perturbations. Importantly, in order to cope with the complexity of these networks, mathematical and computational modeling is required. We propose a computational modeling framework in order to achieve more robust discoveries in the context of logical signaling networks. More precisely, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). ASP provides a declarative language for modeling various knowledge representation and reasoning problems. Moreover, available ASP solvers provide several reasoning modes for assessing the multitude of answer sets. Therefore, leveraging its rich modeling language and its highly efficient solving capacities, we use ASP to address three challenging problems in the context of logical signaling networks: learning of (Boolean) logical networks, experimental design, and identification of intervention strategies. Overall, the contribution of this thesis is three-fold. Firstly, we introduce a mathematical framework for characterizing and reasoning on the response of logical signaling networks. Secondly, we contribute to a growing list of successful applications of ASP in systems biology. Thirdly, we present a software providing a complete pipeline for automated reasoning on the response of logical signaling networks.},
  language  = {en}
}
@misc{KaminskiSchaubSiegeletal.2013,
  author    = {Kaminski, Roland and Schaub, Torsten H. and Siegel, Anne and Videla, Santiago},
  title     = {Minimal intervention strategies in logical signaling networks with ASP},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  number    = {4-5},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41570},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-415704},
  pages     = {675 -- 690},
  year      = {2013},
  abstract  = {Proposing relevant perturbations to biological signaling networks is central to many problems in biology and medicine because it allows for enabling or disabling certain biological outcomes. In contrast to quantitative methods that permit fine-grained (kinetic) analysis, qualitative approaches allow for addressing large-scale networks. This is accomplished by more abstract representations such as logical networks. We elaborate upon such a qualitative approach aiming at the computation of minimal interventions in logical signaling networks relying on Kleene's three-valued logic and fixpoint semantics. We address this problem within answer set programming and show that it greatly outperforms previous work using dedicated algorithms.},
  language  = {en}
}