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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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Metabolic network with 13 MixNet classes with proportions.  = 1.4,  = 4.1,  = 45.4,  = 1.8,  = 6.0,  = 6.3,  = 4.6,  = 2.8,  = 6.4,  = 2.8,  = 7.4,  = 10.1,  = 0.9.
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Figure 6: Metabolic network with 13 MixNet classes with proportions. = 1.4, = 4.1, = 45.4, = 1.8, = 6.0, = 6.3, = 4.6, = 2.8, = 6.4, = 2.8, = 7.4, = 10.1, = 0.9.

Mentions: The first result of MixNet is that 45% of the reactions of the metabolic network of B. aphidicola are "chain-like" reactions that are not sufficiently structured from the connectivity point of view to be split into more subsets of reactions. Indeed, Class 3 has a mean degree close to 2 which indicates chains of reactions with only few branch lines (Table 4). It seems to be consistent with the fact that most of the redundant metabolic pathways disappeared from the metabolic network of B. aphidicola [21]. The twelve remaining classes form 2 meta components whose links are very loose (they are not represented on the summary plot of Figure 6, but these components are connected through reactions of class 3).


Deciphering the connectivity structure of biological networks using MixNet.

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

Metabolic network with 13 MixNet classes with proportions.  = 1.4,  = 4.1,  = 45.4,  = 1.8,  = 6.0,  = 6.3,  = 4.6,  = 2.8,  = 6.4,  = 2.8,  = 7.4,  = 10.1,  = 0.9.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Metabolic network with 13 MixNet classes with proportions. = 1.4, = 4.1, = 45.4, = 1.8, = 6.0, = 6.3, = 4.6, = 2.8, = 6.4, = 2.8, = 7.4, = 10.1, = 0.9.
Mentions: The first result of MixNet is that 45% of the reactions of the metabolic network of B. aphidicola are "chain-like" reactions that are not sufficiently structured from the connectivity point of view to be split into more subsets of reactions. Indeed, Class 3 has a mean degree close to 2 which indicates chains of reactions with only few branch lines (Table 4). It seems to be consistent with the fact that most of the redundant metabolic pathways disappeared from the metabolic network of B. aphidicola [21]. The twelve remaining classes form 2 meta components whose links are very loose (they are not represented on the summary plot of Figure 6, but these components are connected through reactions of class 3).

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