Probabilistic generation of random networks taking into account information on motifs occurrence

Frédéric Y. Bois (1, 2) , Ghislaine Gayraud (3)

1 INERIS - Institut National de l'Environnement Industriel et des Risques
2 UTC - Université de Technologie de Compiègne
3 LMAC - Laboratoire de Mathématiques Appliquées de Compiègne

Because of the huge number of graphs possible even with a small number of nodes, inference on network structure is known to be a challenging problem. Generating large random directed graphs with prescribed probabilities of occurrences of some meaningful patterns (motifs) is also difficult. We show how to generate such random graphs according to a formal probabilistic representation, using fast Markov chain Monte Carlo methods to sample them. As an illustration, we generate realistic graphs with several hundred nodes mimicking a gene transcription interaction network in Escherichia coli.

Mots clés: NETWORK MOTIF; PRIOR INFORMATION; GRAPHICAL MODEL; BIOLOGICAL NETWORK

Domaines: Toxicologie

Frédéric Y. Bois, Ghislaine Gayraud. Probabilistic generation of random networks taking into account information on motifs occurrence. Journal of Computational Biology, 2015, 22 (1), pp.25-36. ⟨10.1089/cmb.2014.0175⟩. ⟨ineris-01862569⟩