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Identification of bifurcation transitions in biological regulatory networks using Answer-Set Programming

Abstract : Background Numerous cellular differentiation processes can be captured using discrete qualitative models of biological regulatory networks. These models describe the temporal evolution of the state of the network subject to different competing transitions, potentially leading the system to different attractors. This paper focusses on the formal identification of states and transitions that are crucial for preserving or pre-empting the reachability of a given behaviour. Methods In the context of non-deterministic automata networks, we propose a static identification of so-called bifurcations, i.e., transitions after which a given goal is no longer reachable. Such transitions are naturally good candidates for controlling the occurrence of the goal, notably by modulating their propensity. Our method combines Answer-Set Programming with static analysis of reachability properties to provide an under-approximation of all the existing bifurcations. Results We illustrate our discrete bifurcation analysis on several models of biological systems, for which we identify transitions which impact the reachability of given long-term behaviour. In particular, we apply our implementation on a regulatory network among hundreds of biological species, supporting the scalability of our approach. Conclusions Our method allows a formal and scalable identification of transitions which are responsible for the lost of capability to reach a given state. It can be applied to any asynchronous automata networks, which encompass Boolean and multi-valued models. An implementation is provided as part of the Pint software, available at http://loicpauleve.name/pint.
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Contributor : Loïc Paulevé Connect in order to contact the contributor
Submitted on : Thursday, July 20, 2017 - 11:15:41 PM
Last modification on : Wednesday, April 27, 2022 - 3:48:12 AM



Louis Fippo Fitime, Olivier Roux, Carito Guziolowski, Loïc Paulevé. Identification of bifurcation transitions in biological regulatory networks using Answer-Set Programming. Algorithms for Molecular Biology, BioMed Central, 2017, 12 (1), pp.19. ⟨10.1186/s13015-017-0110-3⟩. ⟨hal-01566380⟩



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