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 - 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 - TY - THES A1 - Thiele, Sven T1 - Modeling biological systems with Answer Set Programming T1 - Modellierung biologischer Systeme mit Answer Set Programming N2 - 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. N2 - In den letzten Jahren wurden große Fortschritte bei der Identifikation und Messung der Bausteine des Lebens gemacht. Die Verfügbarkeit von Hochdurchsatzverfahren in der Molekularbiology hat das Anwachsen unseres biologischen Wissens dramatisch beschleunigt. Durch die technische Fortschritte in Genomic, Proteomic und Metabolomic wurde die Konstruktion komplexer Modelle biologischer Systeme ermöglicht. Immer mehr biologische Datenbanken sind über das Internet verfügbar, sie enthalten tausende Daten biochemischer Reaktionen und genetischer Regulation. System Biologie ist ein junger Forschungszweig der Biologie, der versucht Biologische Systeme in ihrer Ganzheit zu erforschen. Dabei ist man daran interessiert möglichst viel Wissen aus den unterschiedlichsten Bereichen in ein Modell zu aggregieren, welches das Zusammenwirken der verschiedensten Komponenten nachbildet. Durch das Studium derartiger Modelle erhofft man sich ein Verständnis der aufbauenden Eigenschaften, wie zum Beispiel Robustheit, des Systems zu erlangen. Es stellt sich jedoch die Problematik, das sowohl die biologischen Modelle als auch die verfügbaren Messwerte, oft unvollständig, miteinander unvereinbar oder fehlerhaft sind. All dies macht es schwierig biologisch sinnvolle Schlussfolgerungen zu ziehen. Daher, möchten wir in dieser Arbeit Antwortmengen Programmierung (engl. Answer Set Programming; ASP) als Werkzeug zur diskreten Modellierung system biologischer Probleme vorschlagen. ASP verfügt über eingebaute Eigenschaften zum Umgang mit unvollständiger Information, eine reichhaltige Modellierungssprache und hocheffiziente Berechnungstechniken. Wir präsentieren ASP Lösungen zur Analyse von Netzwerken genetischer Regulierungen, zur Prüfung der Konsistenz mit gemessene Daten, und zur Identifikation von Gründen für Inkonsistenz. Diesen Ansatz erweitern wir um die Möglichkeit zur Berechnung minimaler Reparaturen an Modell und Daten, welche Konsistenz erzeugen. Mithilfe dieser Methode werden wir in die Lage versetzt, auch im Fall von Inkonsistenz, noch ungemessene Daten vorherzusagen. Weiterhin, präsentieren wir einen ASP Ansatz zur Analyse metabolischer Netzwerke. Bei diesem Ansatz, nutzen wir zum einen aus das sich Erreichbarkeit mit ASP leicht spezifizieren lässt und das ASP mehrere mächtige Methoden zur Schlussfolgerung bereitstellt, welche sich auch kombiniert lassen. Dadurch wird es möglich die Synthese Möglichkeiten eines Metabolischen Netzwerks zu erforschen und Hypothesen für Erweiterungen des metabolischen Netzwerks zu berechnen. Zu guter Letzt, präsentieren wir die BioASP Softwarebibliothek. Die BioASP-Bibliothek kapselt unsere ASP Lösungen in das imperative Programmierparadigma und vereinfacht eine Integration von ASP Lösungen in heterogene Betriebsumgebungen, wie sie in der System Biologie vorherrschen. KW - Antwortmengen Programmierung KW - System Biologie KW - Inkonsistenz KW - Unvollständigkeit KW - Reparatur KW - answer set programming KW - systems biology KW - inconsistency KW - incompleteness KW - repair Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-59383 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 - 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 - Delgrande, James Patrick A1 - Liu, Daphne H. A1 - Schaub, Torsten H. A1 - Thiele, Sven T1 - COBA 2.0 : a consistency-based belief change system Y1 - 2007 ER - TY - JOUR A1 - Gressmann, Jean A1 - Janhunen, Tomi A1 - Mercer, Robert E. A1 - Schaub, Torsten H. A1 - Thiele, Sven A1 - Tichy, Richard T1 - On probing and multi-threading in platypus Y1 - 2006 UR - http://www2.in.tu-clausthal.de/~tmbehrens/NMR_Proc_TR4.pdf ER - TY - JOUR A1 - Gressmann, Jean A1 - Janhunen, Tomi A1 - Mercer, Robert E. A1 - Schaub, Torsten H. A1 - Thiele, Sven A1 - Tichy, Richard T1 - On probing and multi-threading in platypus Y1 - 2006 ER - TY - JOUR A1 - Delgrande, James Patrick A1 - Liu, Daphne H. A1 - Schaub, Torsten H. A1 - Thiele, Sven T1 - COBA 2.0 : a consistency-based belief change system Y1 - 2006 UR - http://www2.in.tu-clausthal.de/~tmbehrens/NMR_Proc_TR4.pdf ER - TY - JOUR A1 - Gressmann, Jean A1 - Janhunen, Tomi A1 - Mercer, Robert E. A1 - Schaub, Torsten H. A1 - Thiele, Sven A1 - Tichy, Richard T1 - Platypus : a platform for distributed answer set solving Y1 - 2005 UR - http://www.cs.uni-potsdam.de/wv/pdfformat/grjamescthti05a.pdf ER -