Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations

Authored by Tatiana Shashkova, Anna Popenko, Alexander Tyakht, Kirill Peskov, Yuri Kosinsky, Lev Bogolubsky, Andrei Raigorodskii, Dmitry Ischenko, Dmitry Alexeev, Vadim Govorun

Date Published: 2016

DOI: 10.1371/journal.pone.0148386

Sponsors: No sponsors listed

Platforms: Java

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: https://github.com/dreamlab13/abmbiota

Abstract

Background Intestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes. Methodology/Principal Findings In this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery. Conclusion/Significance The model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.

Tags

inflammation Metabolism Dynamics Susceptibility Bacterial biofilms Chain fatty-acids Antibiotic perturbation Gastrointestinal-tract Intestinal microbiota Colon