SAT-Based Method for Finding Attractors in Asynchronous Multi-Valued Networks
| Autor: | Soh, Takehide, Magnin, Morgan, Le Berre, Daniel, Banbara, Mutsunori, Tamura, Naoyuki |
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| Přispěvatelé: | Kobe University, National Institute of Informatics (NII), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ), Centre de Recherche en Informatique de Lens (CRIL), Université d'Artois (UA)-Centre National de la Recherche Scientifique (CNRS), Nagoya University, Ana Fred, Hugo Gamboa |
| Rok vydání: | 2023 |
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| Zdroj: | Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023)-14th International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2023) 14th International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2023) 14th International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2023), Feb 2023, Lisbon, Portugal |
| Popis: | International audience; In this paper, we propose a SAT-based method for finding attractors of bounded size in asynchronous automata networks. The automata network is a multi-valued mathematical model which has been studied for the quali- tative modeling of biological regulatory networks. An attractor is a minimal set of states in automata networks that cannot be escaped and thus loops indefinitely. Attractors are crucial to validate the initial design of a biological model and predict possible asymptotic behaviors, e.g., how cells may result through maturation in differentiated cell types. Developing an efficient computational method to find attractors is thus an important research topic. Our contribution is a translation of the problem of finding attractors of automata networks into a sequence of propositional satisfiability (SAT) problems. We also propose to add two optional constraints to improve the computation time of attractors. Experiments are carried out using 30 automata networks, 8 coming from real biological case studies and 22 crafted ones with controlled attractor size. The experimental results show that our method scales better than the state-of-the-art ASP method when the size of the attractors increases. |
| Databáze: | OpenAIRE |
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