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A structural analysis of the hypoxia response network

Authors
  • Li, Jianjie
  • Gao, Yuqi
  • Yu, Xuan
Type
Published Article
Journal
PeerJ
Publisher
PeerJ
Publication Date
Apr 06, 2021
Volume
9
Identifiers
DOI: 10.7717/peerj.10985
PMID: 33868803
PMCID: PMC8034363
Source
PubMed Central
Keywords
Disciplines
  • Computational Science
License
Unknown
External links

Abstract

Background The hypoxia-inducible factor-1 (HIF-1) signaling pathway is an important topic in high-altitude medicine. Network analysis is a novel method for integrating information on different aspects and levels of biological networks. However, this method has not been used in research on the HIF-1 signaling pathway network. To introduce this method into HIF-1-related research fields and verify its feasibility and effectiveness, we used a network analytical method to explore the structural attributes of the HIF-1 signaling pathway network. Methods First, we analyzed the overall network of the HIF-1 signaling pathway using information retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG). We performed topology analysis, centrality analysis, and subgroup analysis of the network. Then, we analyzed the core network based on the overall network analysis. We analyzed the properties of the topology, the bow-tie structure, and the structural complexity of the core network. Results We obtained topological structure diagrams and quantitative indicators of the overall and core networks of the HIF-1 signaling pathway. For the structure diagrams, we generated topology diagrams of the network and the bow-tie structure of the core network. As quantitative indicators, we identified topology, centrality, subgroups, the bow-tie structure, and structural complexity. The topology indicators were the number of nodes, the number of lines, the network diameter, and the network density. The centrality indicators were the degree, closeness, and betweenness. The cohesive subgroup indicator was the components of the network. The bow-tie structure indicators included the core, input, and tendril-like structures. The structural complexity indicators included a power-law fitting model and its scale parameter. Conclusions The core network could be extracted based on the subgroup analysis of the overall network of the HIF-1 signaling pathway. The critical elements of the network could be identified in the centrality analysis. The results of the study show the feasibility and effectiveness of the network analytical method used to explore the network properties of the HIF-1 signaling pathway and provide support for further research.

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