Learning Boolean controls in regulated metabolic networks: a case-study

Kerian Thuillier (1) , Caroline Baroukh (2) , Alexander Bockmayr (3) , Ludovic Cottret (2) , Loïc Paulevé (4) , Anne Siegel (1)

1 Dyliss - Dynamics, Logics and Inference for biological Systems and Sequences
2 INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
3 Freie Universität Berlin
4 LaBRI - Laboratoire Bordelais de Recherche en Informatique

Many techniques have been developed to infer Boolean regulations from a prior knowledge network and experimental data. Existing methods are able to reverse-engineer Boolean regulations for transcriptional and signaling networks, but they fail to infer regulations that control metabolic networks. This paper provides a formalisation of the inference of regulations for metabolic networks as a satisfiability problem with two levels of quantifiers, and introduces a method based on Answer Set Programming to solve this problem on a small-scale example.

Mots clés: Inference; Regulated metabolism; Satisfiability problem

Domaines: Bio-informatique [q-bio.QM]; Intelligence artificielle [cs.AI]

Kerian Thuillier, Caroline Baroukh, Alexander Bockmayr, Ludovic Cottret, Loïc Paulevé, et al.. Learning Boolean controls in regulated metabolic networks: a case-study. 2021. ⟨hal-03207589v2⟩