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

TL;DR

This paper presents a graph-based model and simulation of the human gut bacteriome to analyze metabolite exchange and its effects on bacterial populations, aiding in understanding gut health and developing treatments.

Contribution

It introduces a two-layer network model and an in-silico simulation for studying molecular communications in the gut bacteriome, revealing how molecular inputs influence bacterial dynamics.

Findings

Regulation of molecular inputs affects bacterial growth.

Manipulating bacteriome composition alters metabolite communication.

The model can identify hidden behaviors in gut microbiome interactions.

Abstract

Alterations in the human gut bacteriome can be associated with human health issues, such as type-2 diabetes and cardiovascular disease. Both external and internal factors can drive changes in the composition and in the interactions of the human gut bacteriome, impacting negatively on the host cells. In this paper, we focus on the human gut bacteriome metabolism and we propose 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 gut bacteriome network system. Our results show that the regulation of molecular inputs can strongly affect bacterial population growth and create an unbalanced network, as shown by the shift in the node weights based on the molecular signals that are produced. Additionally, we show that the metabolite molecular communication production is greatly affected when directly manipulating the composition of the human gut bacteriome network in the virtual GB. These results indicate that our human GB interaction model can help to identify hidden behaviors of the human gut bacteriome depending on the molecular signal interactions. Moreover, the virtual GB can support the research and development of novel medical treatments based on the accurate control of bacterial growth and exchange of metabolites.