A Graph-based Molecular Communications Model Analysis of the Human Gut Bacteriome

Samitha Sulakshana Somathilaka, Daniel P. Martins, Wiley Barton, Orla O'Sullivan, Paul D. Cotter, Sasitharan Balasubramaniam

Research output: Contribution to journalArticlepeer-review

Abstract

Alterations in the human Gut Bacteriome (GB) can be associated with human health issues, such as type-2 diabetes and obesity. Both external and internal factors can drive changes in the composition and in interactions of the human GB, impacting negatively on the host cells. This paper focuses on the human GB metabolism and proposes a two-layer network system to investigate its dynamics. Furthermore, we develop an in-silico simulation model (virtual GB), allowing us to study the impact of the metabolite exchange through molecular communications in the human GB network system. Our results show that regulation of molecular inputs strongly affect bacterial population growth and create an unbalanced network, as shown by shifts in the node weights based on the produced molecular signals. Additionally, we show that the metabolite molecular communication production is greatly affected when directly manipulating the composition of the human GB network in the virtual GB. These results indicate that our human GB interaction model can help to identify hidden behaviors of the human GB depending on molecular signal interactions. Moreover, the virtual GB can support the research and development of novel medical treatments based on the accurate control of bacterial population growth and exchange of metabolites.

Original languageEnglish
JournalIEEE Journal of Biomedical and Health Informatics
DOIs
Publication statusPublished - 04 Feb 2022

Keywords

  • Analytical models
  • Bioinformatics
  • Biological network systems
  • Biological system modeling
  • graph analysis
  • human gut bacteriome
  • metabolic interactions
  • Microorganisms
  • molecular communications
  • Production
  • Sociology
  • Statistics

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