A computational framework for exploring structural protein variability in virus variants using a codon network model

Authors

  • L. Praveenkumar Department of Mathematics, The Gandhigram Rural Institute - Deemed to be University, Dindigul, Tamil Nadu 624302, India
  • G. Mahadevan Department of Mathematics, The Gandhigram Rural Institute - Deemed to be University, Dindigul, Tamil Nadu 624302, India
  • A. A. Navish Department of Mathematics, The Gandhigram Rural Institute - Deemed to be University, Dindigul, Tamil Nadu 624302, India

DOI:

https://doi.org/10.55630/j.biomath.2025.06.105

Keywords:

virus variants, codon network, connected domination, community, centrality

Abstract

This study applies a graph-theoretic framework to analyze the structural dynamics of codon networks derived from SARS-CoV-2 spike protein sequences. By employing a dual-level analysis of Minimum Connected Dominating Sets (MCDS) and community structures, we explore the mathematical underpinnings of viral protein organization. First, we construct the MCDS to identify critical codons that ensure global network connectivity, providing key insights into structurally significant regions of the protein sequence. Next, we analyze the community structures within the network to determine localized structural and functional roles, facilitating the identification of specialized codon groups. Centrality measures are employed to quantify the significance of codons within both the MCDS and the identified communities, highlighting their roles in maintaining network integrity. Furthermore, we investigate the impact of mutations across SARS-CoV-2 variants, assessing their influence on codon connectivity and functional stability. A statistical analysis of MCDS and community node variability provides deeper insights into the structural robustness of the spike protein. This study underscores the potential of mathematical modeling in virology and highlights essential codons as potential targets for therapeutic intervention.

Downloads

Published

2025-06-10

Issue

Vol. 14 No. 1 (2025)

Section

Original Articles

License

Copyright (c) 2025 L. Praveenkumar, G. Mahadevan, A. A. Navish

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The journal Biomath is an open access journal. All published articles are immeditely available online and the respective DOI link activated. All articles can be access for free and no reader registration of any sort is required. No fees are charged to authors for article submission or processing. Online publications are funded through volunteer work, donations and grants.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).