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

J Comput Biol. 2015 Jan;22(1):25-36. doi: 10.1089/cmb.2014.0175.


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.

Keywords: biological network; graphical model; network motif; prior information.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Escherichia coli / physiology*
  • Gene Regulatory Networks / physiology*
  • Models, Genetic*
  • Transcription, Genetic / physiology*