Computational analysis of molecular networks using spectral graph theory, complexity measures and information theory
Open accessAbstract
Molecular networks are described in terms of directed multigraphs, so-called network motifs. Spectral graph theory, reciprocal link and complexity measures were utilized to quantify network motifs. It was found that graph energy, reciprocal link and cyclomatic complexity can optimally specify network motifs with some degree of degeneracy. Biological networks are built up from a finite number of motif patterns; hence, a graph energy cutoff exists and the Shannon entropy of the motif frequency distribution is not maximal. Also, frequently found motifs are irreducible graphs. Network similarity was quantified by gauging their motif frequency distribution functions using Jensen-Shannon entropy. This method allows us to determine the distance between two networks regardless of their nodes’ identities and network sizes. This study provides a systematic approach to dissect the complex nature of biological networks. Our novel method different from any other approach. The findings support the view that there are organizational principles underlying molecular networks.
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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., 2019
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References 94 · Cited by 4
- Authors
- Huang, C.-H., Tsai, J. J. P., Kurubanjerdjit, N., & Ng, K.-L.
- Affiliations
- National Formosa University (TW)
- Asia University (TW)
- Mae Fah Luang University (TH)
- Corresponding author
- Ng, Ka-Lok
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2019-02-02
- OpenAlex
- W2911758388
- Cited by
- 4
- References
- 94
- Citations by year
- 2023: 1 · 2021: 2 · 2020: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Funders
- Ministry of Science and Technology, Taiwan (MOST107-2221-E150-038, MOST106-2221-E468-017, MOST107-2632-E468-002)
- Asia University (106-asia-06, 107-asia-02, 107-asia-09)
- 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
- Physical Sciences › Mathematics › Geometry and Topology › Graph theory and applications
- Keywords
- molecular networks, network motifs, directed multigraphs, spectral graph theory, graph energy, cyclomatic complexity, Shannon entropy, network similarity, irreducible graphs
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