TY - JOUR A1 - Gebser, Martin A1 - Schaub, Torsten H. A1 - Thiele, Sven T1 - GrinGo : a new grounder for answer set programming Y1 - 2007 SN - 978-3-540- 72199-4 ER - 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 - JOUR A1 - Gebser, Martin A1 - Schaub, Torsten H. A1 - Thiele, Sven A1 - Veber, Philippe T1 - Detecting inconsistencies in large biological networks with answer set programming JF - Theory and practice of logic programming N2 - 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. KW - answer set programming KW - bioinformatics KW - consistency KW - diagnosis Y1 - 2011 U6 - https://doi.org/10.1017/S1471068410000554 SN - 1471-0684 VL - 11 IS - 5-6 SP - 323 EP - 360 PB - Cambridge Univ. Press CY - New York ER - TY - CHAP A1 - Gebser, Martin A1 - Hinrichs, Henrik A1 - Schaub, Torsten H. A1 - Thiele, Sven T1 - xpanda: a (simple) preprocessor for adding multi-valued propositions to ASP N2 - 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. Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-41466 ER - TY - GEN A1 - Gebser, Martin A1 - Schaub, Torsten H. A1 - Thiele, Sven A1 - Veber, Philippe T1 - Detecting inconsistencies in large biological networks with answer set programming T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 561 KW - answer set programming KW - bioinformatics KW - consistency KW - diagnosis Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-412467 SN - 1866-8372 IS - 561 ER -