  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.

Molecular Networks;Emerging Technologies