@article{GressmannJanhunenMerceretal.2006, author = {Gressmann, Jean and Janhunen, Tomi and Mercer, Robert E. and Schaub, Torsten H. and Thiele, Sven and Tichy, Richard}, title = {On probing and multi-threading in platypus}, year = {2006}, language = {en} } @article{GebserSchaubThiele2007, author = {Gebser, Martin and Schaub, Torsten H. and Thiele, Sven}, title = {GrinGo : a new grounder for answer set programming}, isbn = {978-3-540- 72199-4}, year = {2007}, language = {en} } @article{GressmannJanhunenMerceretal.2006, author = {Gressmann, Jean and Janhunen, Tomi and Mercer, Robert E. and Schaub, Torsten H. and Thiele, Sven and Tichy, Richard}, title = {On probing and multi-threading in platypus}, year = {2006}, language = {en} } @article{DelgrandeLiuSchaubetal.2006, author = {Delgrande, James Patrick and Liu, Daphne H. and Schaub, Torsten H. and Thiele, Sven}, title = {COBA 2.0 : a consistency-based belief change system}, year = {2006}, language = {en} } @article{GressmannJanhunenMerceretal.2005, author = {Gressmann, Jean and Janhunen, Tomi and Mercer, Robert E. and Schaub, Torsten H. and Thiele, Sven and Tichy, Richard}, title = {Platypus : a platform for distributed answer set solving}, year = {2005}, language = {en} } @phdthesis{Thiele2011, author = {Thiele, Sven}, title = {Modeling biological systems with Answer Set Programming}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-59383}, school = {Universit{\"a}t Potsdam}, year = {2011}, abstract = {Biology has made great progress in identifying and measuring the building blocks of life. The availability of high-throughput methods in molecular biology has dramatically accelerated the growth of biological knowledge for various organisms. The advancements in genomic, proteomic and metabolomic technologies allow for constructing complex models of biological systems. An increasing number of biological repositories is available on the web, incorporating thousands of biochemical reactions and genetic regulations. Systems Biology is a recent research trend in life science, which fosters a systemic view on biology. In Systems Biology one is interested in integrating the knowledge from all these different sources into models that capture the interaction of these entities. By studying these models one wants to understand the emerging properties of the whole system, such as robustness. However, both measurements as well as biological networks are prone to considerable incompleteness, heterogeneity and mutual inconsistency, which makes it highly non-trivial to draw biologically meaningful conclusions in an automated way. Therefore, we want to promote Answer Set Programming (ASP) as a tool for discrete modeling in Systems Biology. ASP is a declarative problem solving paradigm, in which a problem is encoded as a logic program such that its answer sets represent solutions to the problem. ASP has intrinsic features to cope with incompleteness, offers a rich modeling language and highly efficient solving technology. We present ASP solutions, for the analysis of genetic regulatory networks, determining consistency with observed measurements and identifying minimal causes for inconsistency. We extend this approach for computing minimal repairs on model and data that restore consistency. This method allows for predicting unobserved data even in case of inconsistency. Further, we present an ASP approach to metabolic network expansion. This approach exploits the easy characterization of reachability in ASP and its various reasoning methods, to explore the biosynthetic capabilities of metabolic reaction networks and generate hypotheses for extending the network. Finally, we present the BioASP library, a Python library which encapsulates our ASP solutions into the imperative programming paradigm. The library allows for an easy integration of ASP solution into system rich environments, as they exist in Systems Biology.}, language = {en} } @article{DelgrandeLiuSchaubetal.2007, author = {Delgrande, James Patrick and Liu, Daphne H. and Schaub, Torsten H. and Thiele, Sven}, title = {COBA 2.0 : a consistency-based belief change system}, year = {2007}, language = {en} } @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{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} } @article{GebserSchaubThieleetal.2011, author = {Gebser, Martin and Schaub, Torsten H. and Thiele, Sven and Veber, Philippe}, title = {Detecting inconsistencies in large biological networks with answer set programming}, series = {Theory and practice of logic programming}, volume = {11}, journal = {Theory and practice of logic programming}, number = {5-6}, publisher = {Cambridge Univ. Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068410000554}, pages = {323 -- 360}, year = {2011}, abstract = {We introduce an approach to detecting inconsistencies in large biological networks by using answer set programming. To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on answer set programming to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions.}, language = {en} } @inproceedings{GebserHinrichsSchaubetal.2010, author = {Gebser, Martin and Hinrichs, Henrik and Schaub, Torsten H. and Thiele, Sven}, title = {xpanda: a (simple) preprocessor for adding multi-valued propositions to ASP}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-41466}, year = {2010}, abstract = {We introduce a simple approach extending the input language of Answer Set Programming (ASP) systems by multi-valued propositions. Our approach is implemented as a (prototypical) preprocessor translating logic programs with multi-valued propositions into logic programs with Boolean propositions only. Our translation is modular and heavily benefits from the expressive input language of ASP. The resulting approach, along with its implementation, allows for solving interesting constraint satisfaction problems in ASP, showing a good performance.}, language = {en} } @misc{GebserSchaubThieleetal.2011, author = {Gebser, Martin and Schaub, Torsten H. and Thiele, Sven and Veber, Philippe}, title = {Detecting inconsistencies in large biological networks with answer set programming}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {561}, issn = {1866-8372}, doi = {10.25932/publishup-41246}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-412467}, pages = {38}, year = {2011}, abstract = {We introduce an approach to detecting inconsistencies in large biological networks by using answer set programming. To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on answer set programming to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions.}, language = {en} }