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Deciphering the connectivity structure of biological networks using MixNet.

Picard F, Miele V, Daudin JJ, Cottret L, Robin S - BMC Bioinformatics (2009)

Bottom Line: 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 makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: CNRS UMR 5558, Université Lyon-1, Laboratoire de Biométrie et Biologie Evolutive, 43 bd du 11 novembre 1918, F-69622, Villeurbanne, France. picard@biomserv.univ-lyon1.fr

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

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Macaque Cortex Network with 8 MixNet classes, with proportions.  = 17.0,  = 14.9,  = 2.1,  = 4.3,  = 19.2,  = 14.9,  = 10.6,  = 17.0
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Figure 3: Macaque Cortex Network with 8 MixNet classes, with proportions. = 17.0, = 14.9, = 2.1, = 4.3, = 19.2, = 14.9, = 10.6, = 17.0

Mentions: The dorsal visual stream area is a very densely connected zone in the brain, and has been viewed as homogeneous in a previous study [15]. On the contrary, MixNet emphasizes different connectivity behaviors (Figure 3). This zone is split into 3 classes (1-2-3) and MixNet still catches the strong inter-class connexion pattern . This split is explained by the intensity of connexions with other zones, and by the differences in flows direction (balanced flow for class 2, unbalanced for class 1). MixNet identifies hubs like V4, a provincial hub that constitutes a group on its own (group 3), but also sets of hubs like the Frontal Eye Field (FEF) and node 7a, that are known to receive and send many long range pathways and to connects visual and sensimotor zones respectively. Those hubs form class 4 which is also responsible of the split of the dorsal visual stream area, since inter-classes connectivity probability are very different:


Deciphering the connectivity structure of biological networks using MixNet.

Picard F, Miele V, Daudin JJ, Cottret L, Robin S - BMC Bioinformatics (2009)

Macaque Cortex Network with 8 MixNet classes, with proportions.  = 17.0,  = 14.9,  = 2.1,  = 4.3,  = 19.2,  = 14.9,  = 10.6,  = 17.0
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2697640&req=5

Figure 3: Macaque Cortex Network with 8 MixNet classes, with proportions. = 17.0, = 14.9, = 2.1, = 4.3, = 19.2, = 14.9, = 10.6, = 17.0
Mentions: The dorsal visual stream area is a very densely connected zone in the brain, and has been viewed as homogeneous in a previous study [15]. On the contrary, MixNet emphasizes different connectivity behaviors (Figure 3). This zone is split into 3 classes (1-2-3) and MixNet still catches the strong inter-class connexion pattern . This split is explained by the intensity of connexions with other zones, and by the differences in flows direction (balanced flow for class 2, unbalanced for class 1). MixNet identifies hubs like V4, a provincial hub that constitutes a group on its own (group 3), but also sets of hubs like the Frontal Eye Field (FEF) and node 7a, that are known to receive and send many long range pathways and to connects visual and sensimotor zones respectively. Those hubs form class 4 which is also responsible of the split of the dorsal visual stream area, since inter-classes connectivity probability are very different:

Bottom Line: 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 makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: CNRS UMR 5558, Université Lyon-1, Laboratoire de Biométrie et Biologie Evolutive, 43 bd du 11 novembre 1918, F-69622, Villeurbanne, France. picard@biomserv.univ-lyon1.fr

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

Show MeSH