Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: European Molecular Biology Network (EMBnet) Conference 2008: 20th Anniversary Celebration. Leading applications and technologies in bioinformatics

Open Access Proceedings

Deciphering the connectivity structure of biological networks using MixNet

Franck Picard12*, Vincent Miele12, Jean-Jacques Daudin3, Ludovic Cottret1 and Stéphane Robin3

Author Affiliations

1 CNRS UMR 5558, Université Lyon-1, Laboratoire de Biométrie et Biologie Evolutive, 43 bd du 11 novembre 1918, F-69622, Villeurbanne, France

2 CNRS UMR 8071, Université d'Evry, INRA UMR 1152, Laboratoire Statistique et Génome, 523, place des Terrasses, F-91000 Evry, France

3 UMR 518 AgroParisTech/INRA, 16 rue Claude Bernard, F-75231, Paris, France

For all author emails, please log on.

BMC Bioinformatics 2009, 10(Suppl 6):S17  doi:10.1186/1471-2105-10-S6-S17

Published: 16 June 2009



As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles.


We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the E. coli transcriptional regulatory network, the macaque cortex network, a foodweb network and the Buchnera aphidicola metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering.


We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.