Graph theory and stability analysis of protein complex interaction networks
Open accessAbstract
Protein complexes play an essential role in many biological processes. Complexes can interact with other complexes to form protein complex interaction network (PCIN) that involves in important cellular processes. There are relatively few studies on examining the interaction topology among protein complexes; and little is known about the stability of PCIN under perturbations. We employed graph theoretical approach to reveal hidden properties and features of four species PCINs. Two main issues are addressed, (i) the global and local network topological properties, and (ii) the stability of the networks under 12 types of perturbations. According to the topological parameter classification, we identified some critical protein complexes and validated that the topological analysis approach could provide meaningful biological interpretations of the protein complex systems. Through the Kolmogorov-Smimov test, we showed that local topological parameters are good indicators to characterise the structure of PCINs. We further demonstrated the effectiveness of the current approach by performing the scalability and data normalization tests. To measure the robustness of PCINs, we proposed to consider eight topological-based perturbations, which are specifically applicable in scenarios of targeted, sustained attacks. We found that the degree-based, betweenness-based and brokering-coefficient-based perturbations have the largest effect on network stability.
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Protein complex interaction networks are non-random networks.
“a graph theoretical study shows that protein complex interaction networks are non-random networks (Jalan, 2013;Huang et al., 2016Huang et al., , 2019)).”
From Using statistical methods to model the fine-tuning of molecular machines and systems (2020) · cites this paper as Huang et al., 2016
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- Authors
- Huang, C., Chen, T., & Ng, K.
- Affiliations
- National Formosa University (TW)
- Asia University (TW)
- Corresponding author
- Ng, Ka‐Lok
- Venue
- IET Systems Biology
- Published
- 2016-04
- Volume
- 10
- Issue
- 2
- Pages
- 64-75
- ISSN
- 1751-8849 (ISSN-L)
- 1751-8857
- PMC
- PMC8687277
- OpenAlex
- W2273224325
- Cited by
- 18
- References
- 66
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 1 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 3 · 2017: 2 · 2016: 2
- FWCI
- 1.01
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IET Systems Biology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Science and Technology, Taiwan (NSC 101‐2221‐E‐150‐088‐MY2)
- Asia University
- Ministry of Science and Technology (103‐asia‐06, NSC 102‐2632‐E‐468‐001‐MY3)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- network topology, network stability, network robustness, perturbation analysis, topological parameters, betweenness centrality, targeted attacks, protein complexes
- MeSH terms
- Adaptation, Physiological, Algorithms, Animals, Computer Simulation, Humans, Models, Biological, Models, Statistical, Signal Transduction, Proteome, Protein Interaction Mapping
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