Dataset Information


Identification of critical connectors in the directed reaction-centric graphs of microbial metabolic networks.



Detection of central nodes in asymmetrically directed biological networks depends on centrality metrics quantifying individual nodes' importance in a network. In topological analyses on metabolic networks, various centrality metrics have been mostly applied to metabolite-centric graphs. However, centrality metrics including those not depending on high connections are largely unexplored for directed reaction-centric graphs.


We applied directed versions of centrality metrics to directed reaction-centric graphs of microbial metabolic networks. To investigate the local role of a node, we developed a novel metric, cascade number, considering how many nodes are closed off from information flow when a particular node is removed. High modularity and scale-freeness were found in the directed reaction-centric graphs and betweenness centrality tended to belong to densely connected modules. Cascade number and bridging centrality identified cascade subnetworks controlling local information flow and irreplaceable bridging nodes between functional modules, respectively. Reactions highly ranked with bridging centrality and cascade number tended to be essential, compared to reactions that other central metrics detected.


We demonstrate that cascade number and bridging centrality are useful to identify key reactions controlling local information flow in directed reaction-centric graphs of microbial metabolic networks. Knowledge about the local flow connectivity and connections between local modules will contribute to understand how metabolic pathways are assembled.


PROVIDER: S-EPMC6567475 | BioStudies | 2019-01-01

REPOSITORIES: biostudies

Similar Datasets

2015-01-01 | S-EPMC4268260 | BioStudies
2020-01-01 | S-EPMC7244137 | BioStudies
2020-01-01 | S-EPMC7021909 | BioStudies
2015-01-01 | S-EPMC4614120 | BioStudies
2019-01-01 | S-EPMC6668804 | BioStudies
2018-01-01 | S-EPMC6189051 | BioStudies
1000-01-01 | S-EPMC2654898 | BioStudies
2010-01-01 | S-EPMC2922375 | BioStudies
2020-01-01 | S-EPMC7398553 | BioStudies
2015-01-01 | S-EPMC4477107 | BioStudies