Deciphering the connectivity structure of biological networks using MixNet

Franck Picard,Jean-Jacques Daudin,Stéphane Robin,Ludovic Cottret,Vincent Miele

doi:10.1186/1471-2105-10-s6-s17

Abstract

BackgroundAs 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.ResultsWe 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.ConclusionWe 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.

Highlights

As biological networks often show complex topological features, mathematical methods are required to extract meaningful information
We show how flexible the method is, how it summarizes the connectivity structure of a complex network, and how this summary can be used to understand topology-based biological features
Brief recall of MixNet principles In this first paragraph we briefly recall the principle of mixture models when applied to random graphs

Summary

Introduction

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. One characteristics of interest when studying complex networks is their topology or the way particules, proteins or social agents interact [1]. Since networks show complex structural patterns, one common task is to find an appropriate way to summarize their structure. Since summarizing a topology using those indicators gives a crude view of the networks topology, another research direction has been to gather nodes that behave from the point of view of a user defined criterion [5,6,7]

Methods

Results

Conclusion