Model Revision of Logical Regulatory Networks Using Logic-Based Tools

Authors Filipe Gouveia , Inês Lynce , Pedro T. Monteiro

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Filipe Gouveia
  • INESC-ID/Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-029, Lisboa, Portugal
Inês Lynce
  • INESC-ID/Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-029, Lisboa, Portugal
Pedro T. Monteiro
  • INESC-ID/Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-029, Lisboa, Portugal

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Filipe Gouveia, Inês Lynce, and Pedro T. Monteiro. Model Revision of Logical Regulatory Networks Using Logic-Based Tools. In Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018). Open Access Series in Informatics (OASIcs), Volume 64, pp. 23:1-23:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


Recently, biological data has been increasingly produced calling for the existence of computational models able to organize and computationally reproduce existing observations. In particular, biological regulatory networks have been modeled relying on the Sign Consistency Model or the logical formalism. However, their construction still completely relies on a domain expert to choose the best functions for every network component. Due to the number of possible functions for k arguments, this is typically a process prone to error. Here, we propose to assist the modeler using logic-based tools to verify the model, identifying crucial network components responsible for model inconsistency. We intend to obtain a model building procedure capable of providing the modeler with repaired models satisfying a set of pre-defined criteria, therefore minimizing possible modeling errors.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Logic programming and answer set programming
  • Logical Regulatory Networks
  • Model Revision
  • Answer Set Programming
  • Boolean Satisfiability
  • Logic-based tools


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  1. Armin Biere, Marijn Heule, and Hans van Maaren. Handbook of satisfiability, volume 185. IOS press, 2009. Google Scholar
  2. Atul J Butte and Isaac S Kohane. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. In Biocomputing 2000, pages 418-429. World Scientific, 1999. Google Scholar
  3. Edmund M Clarke, Orna Grumberg, and Doron Peled. Model checking. MIT press, 1999. Google Scholar
  4. Elena Dubrova and Maxim Teslenko. A SAT-based algorithm for finding attractors in synchronous boolean networks. IEEE/ACM transactions on computational biology and bioinformatics, 8(5):1393-1399, 2011. Google Scholar
  5. Timur Fayruzov, Jeroen Janssen, Dirk Vermeir, Chris Cornelis, and Martine De Cock. Modelling gene and protein regulatory networks with answer set programming. International journal of data mining and bioinformatics, 5(2):209-229, 2011. Google Scholar
  6. Louis Fippo Fitime, Olivier Roux, Carito Guziolowski, and Loïc Paulevé. Identification of bifurcation transitions in biological regulatory networks using Answer-Set Programming. Algorithms for Molecular Biology, 12(1):19, 2017. Google Scholar
  7. Nir Friedman. Inferring cellular networks using probabilistic graphical models. Science, 303(5659):799-805, 2004. Google Scholar
  8. Martin Gebser, Carito Guziolowski, Mihail Ivanchev, Torsten Schaub, Anne Siegel, Sven Thiele, and Philippe Veber. Repair and Prediction (under Inconsistency) in Large Biological Networks with Answer Set Programming. In KR, 2010. Google Scholar
  9. Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub. Answer set solving in practice. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(3):1-238, 2012. Google Scholar
  10. João Guerra and Inês Lynce. Reasoning over biological networks using maximum satisfiability. In Principles and Practice of Constraint Programming, pages 941-956. Springer, 2012. Google Scholar
  11. Carito Guziolowski, Santiago Videla, Federica Eduati, Sven Thiele, Thomas Cokelaer, Anne Siegel, and Julio Saez-Rodriguez. Exhaustively characterizing feasible logic models of a signaling network using answer set programming. Bioinformatics, page btt393, 2013. Google Scholar
  12. Stuart Kauffman. Homeostasis and differentiation in random genetic control networks. Nature, 224(5215):177, 1969. Google Scholar
  13. Elie Merhej, Steven Schockaert, and Martine De Cock. Repairing inconsistent answer set programs using rules of thumb: A gene regulatory networks case study. International Journal of Approximate Reasoning, 83:243-264, 2017. Google Scholar
  14. Pedro T Monteiro, Wassim Abou-Jaoudé, Denis Thieffry, and Claudine Chaouiya. Model Checking Logical Regulatory Networks. IFAC Proceedings Volumes, 47(2):170-175, 2014. Google Scholar
  15. Aurélien Naldi, Elisabeth Remy, Denis Thieffry, and Claudine Chaouiya. Dynamically consistent reduction of logical regulatory graphs. Theoretical Computer Science, 412(21):2207-2218, 2011. Google Scholar
  16. Aurélien Naldi, Denis Thieffry, and Claudine Chaouiya. Decision diagrams for the representation and analysis of logical models of genetic networks. In CMSB, volume 7, pages 233-247. Springer, 2007. Google Scholar
  17. Loïc Paulevé. Reduction of Qualitative Models of Biological Networks for Transient Dynamics Analysis. IEEE/ACM transactions on computational biology and bioinformatics, 2017. Google Scholar
  18. Ilya Shmulevich, Edward R Dougherty, Seungchan Kim, and Wei Zhang. Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics, 18(2):261-274, 2002. Google Scholar
  19. Anne Siegel, Ovidiu Radulescu, Michel Le Borgne, Philippe Veber, Julien Ouy, and Sandrine Lagarrigue. Qualitative analysis of the relation between DNA microarray data and behavioral models of regulation networks. Biosystems, 84(2):153-174, 2006. Google Scholar
  20. René Thomas. Boolean formalization of genetic control circuits. Journal of theoretical biology, 42(3):563-585, 1973. Google Scholar
  21. René Thomas. Regulatory networks seen as asynchronous automata: a logical description. Journal of theoretical biology, 153(1):1-23, 1991. Google Scholar
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