ArticleBMC bioinformatics2019
Identification of critical connectors in the directed reaction-centric graphs of microbial metabolic networks.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- hiPSCGEM01: A Genome-Scale Metabolic Model for Fibroblast-Derived Human iPSCs.Bioengineering (Basel, Switzerland) · 2025Article
- Chemical and transcriptomic diversity do not correlate with ascending levels of social complexity in the insect order Blattodea.Ecology and evolution · 2024Article
- Metabolomics in Radiotherapy-Induced Early Adverse Skin Reactions of Breast Cancer Patients.Breast cancer (Dove Medical Press) · 2024Article
- Identification of Renal Transplantation Rejection Biomarkers in Blood Using the Systems Biology Approach.Iranian biomedical journal · 2023Article
- Review
- Hypergraph models of biological networks to identify genes critical to pathogenic viral response.BMC bioinformatics · 2021Article
- Opportunities at the Interface of Network Science and Metabolic Modeling.Frontiers in bioengineering and biotechnology · 2020Review
- A Motif-Based Network Analysis of Regulatory Patterns in Doxorubicin Effects on Treating Breast Cancer, a Systems Biology Study.Avicenna journal of medical biotechnologyArticle
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Authors and funding
3 authors.
Funding
Abstract
backgroundDetection 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.
resultsWe 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.
conclusionsWe 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.
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