MERRIN: MEtabolic Regulation Rule INference from time series data

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 LIPME - Laboratoire des Interactions Plantes Microbes Environnement
3 Freie Universität Berlin
4 LaBRI - Laboratoire Bordelais de Recherche en Informatique

Motivation: 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. Results: We present a novel approach to infer Boolean rules for metabolic regulation from time series data and a prior knowledge network. Our method is based on a combination of answer set programming and linear programming. By solving both combinatorial and linear arithmetic constraints we generate candidate Boolean regulations that can reproduce the given data when coupled to the metabolic network. We evaluate our approach on a core regulated metabolic network and show how the quality of the predictions depends on the available kinetic, fluxomics or transcriptomics time series data.

Mots clés: Multiscale dynamics; Boolean networks; Model synthesis; Hybrid metabolic-regulatory models; Logic programming

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

Kerian Thuillier, Caroline Baroukh, Alexander Bockmayr, Ludovic Cottret, Loïc Paulevé, et al.. MERRIN: MEtabolic Regulation Rule INference from time series data. Bioinformatics, 2022, 38 (Supplement_2), pp.ii127-ii133. ⟨10.1093/bioinformatics/btac479⟩. ⟨hal-03701755v1⟩