Dataset Information

PAND: A Distribution to Identify Functional Linkage from Networks with Preferential Attachment Property.

ABSTRACT: Technology advances have immensely accelerated large-scale mapping of biological networks, which necessitates the development of accurate and powerful network-based algorithms to make functional inferences. A prevailing approach is to leverage functions of neighboring nodes to predict unknown molecular function. However, existing neighbor-based algorithms have ignored the scale-free property hidden in many biological networks. By assuming that neighbor sharing is constrained by the preferential attachment property, we developed a Preferential Attachment based common Neighbor Distribution (PAND) to calculate the probability of the neighbor-sharing event between any two nodes in scale-free networks, which nearly perfectly matched the observed probability in simulations. By applying PAND to a human protein-protein interaction (PPI) network, we showed that smaller probabilities represented closer functional linkages between proteins. With the PAND-derive linkages, we were able to build new networks where the links are more functionally reliable than those of the human PPI network. We then applied simple annotation schemes to a PAND-derived network to make reliable functional predictions for proteins. We also developed an R package called PANDA (PAND-derived functional Associations) to implement the methods proposed in this study. In conclusion, PAND is a useful distribution to calculate the probability of the neighbor-sharing events in scale-free networks. With PAND, we are able to extract reliable functional linkages from real biological networks and builds new networks that are better bases for further functional inference.

SUBMITTER: Li H

PROVIDER: S-EPMC4497646 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

PAND: A Distribution to Identify Functional Linkage from Networks with Preferential Attachment Property.

Li Hua H Tong Pan P Gallegos Juan J Dimmer Emily E Cai Guoshuai G Molldrem Jeffrey J JJ Liang Shoudan S

PloS one 20150709 7

Technology advances have immensely accelerated large-scale mapping of biological networks, which necessitates the development of accurate and powerful network-based algorithms to make functional inferences. A prevailing approach is to leverage functions of neighboring nodes to predict unknown molecular function. However, existing neighbor-based algorithms have ignored the scale-free property hidden in many biological networks. By assuming that neighbor sharing is constrained by the preferential ...[more]

PMID: 26158709

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Project description:BackgroundMany biological networks show some characteristics of scale-free networks. Scale-free networks can evolve through preferential attachment where new nodes are preferentially attached to well connected nodes. In networks which have evolved through preferential attachment older nodes should have a higher average connectivity than younger nodes. Here we have investigated preferential attachment in the context of metabolic networks.ResultsThe connectivities of the enzymes in the metabolic network of Escherichia coli were determined and representatives for these enzymes were located in 11 eukaryotes, 17 archaea and 46 bacteria. E. coli enzymes which have representatives in eukaryotes have a higher average connectivity while enzymes which are represented only in the prokaryotes, and especially the enzymes only present in betagamma-proteobacteria, have lower connectivities than expected by chance. Interestingly, the enzymes which have been proposed as candidates for horizontal gene transfer have a higher average connectivity than the other enzymes. Furthermore, It was found that new edges are added to the highly connected enzymes at a faster rate than to enzymes with low connectivities which is consistent with preferential attachment.ConclusionHere, we have found indications of preferential attachment in the metabolic network of E. coli. A possible biological explanation for preferential attachment growth of metabolic networks is that novel enzymes created through gene duplication maintain some of the compounds involved in the original reaction, throughout its future evolution. In addition, we found that enzymes which are candidates for horizontal gene transfer have a higher average connectivity than other enzymes. This indicates that while new enzymes are attached preferentially to highly connected enzymes, these highly connected enzymes have sometimes been introduced into the E. coli genome by horizontal gene transfer. We speculate that E. coli has adjusted its metabolic network to a changing environment by replacing the relatively central enzymes for better adapted orthologs from other prokaryotic species.

Dataset Information

PAND: A Distribution to Identify Functional Linkage from Networks with Preferential Attachment Property.

Publications

PAND: A Distribution to Identify Functional Linkage from Networks with Preferential Attachment Property.

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