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Communication Dans Un Congrès Année : 2022

Minimal trap spaces of Logical models are maximal siphons of their Petri net encoding

Résumé

Boolean modelling of gene regulation but also of post-transcriptomic systems has proven over the years that it can bring powerful analyses and corresponding insight to the many cases where precise biological data is not sufficiently available to build a detailed quantitative model. This is even more true for very large models where such data is frequently missing and led to a constant increase in size of logical models à la Thomas. Besides simulation, the analysis of such models is mostly based on attractor computation, since those correspond roughly to observable biological phenotypes. The recent use of trap spaces made a real breakthrough in that field allowing to consider medium-sized models that used to be out of reach. However, with the continuing increase in model-size, the state-of-the-art computation of minimal trap spaces based on prime-implicants shows its limits as there can be a huge number of implicants. In this article we present an alternative method to compute minimal trap spaces, and hence complex attractors, of a Boolean model. It replaces the need for prime-implicants by a completely different technique, namely the enumeration of maximal siphons in the Petri net encoding of the original model. After some technical preliminaries, we expose the concrete need for such a method and detail its implementation using Answer Set Programming. We then demonstrate its efficiency and compare it to implicant-based methods on some large Boolean models from the literature.
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Dates et versions

hal-03721508 , version 1 (12-07-2022)

Identifiants

Citer

Van-Giang Trinh, Belaid Benhamou, Kunihiko Hiraishi, Sylvain Soliman. Minimal trap spaces of Logical models are maximal siphons of their Petri net encoding. CMSB 2022 - International Conference on Computational Methods in Systems Biology, Sep 2022, Bucarest, Romania. pp.158--176, ⟨10.1007/978-3-031-15034-0_8⟩. ⟨hal-03721508⟩
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