Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations

Abstract

BackgroundIntestinal 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 FindingsIn 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/SignificanceThe 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.

Comparative Genomic Analysis of the Human Gut Microbiome Reveals a Broad Distribution of Metabolic Pathways for the Degradation of Host-Synthetized Mucin Glycans and Utilization of Mucin-Derived Monosaccharides

“…Computational modeling of metabolism (Palsson, 2006 ; Orth et al, 2010 ) may be used for elucidation of mucin glycan degradation by HGM organisms. Previously, genome-based models have been published for single representatives of the HGM (Thiele et al, 2005 , 2011 , 2012 ; Orth et al, 2011 ; Heinken et al, 2014 ) as well as for whole HGM communities (Levy and Borenstein, 2013 ; Bauer et al, 2015 ; Heinken and Thiele, 2015 ; Noecker et al, 2016 ; Shashkova et al, 2016 ; Magnúsdóttir et al, 2017 ). Additionally, computational models for human metabolism are also available (Thiele et al, 2013b ; Mardinoglu et al, 2014 ), and host-microbial metabolic interactions have been modeled (Heinken et al, 2013 , 2016 ; Thiele et al, 2013a ; Shoaie and Nielsen, 2014 ; Levy et al, 2015 ).…”

Human gut microbiota plays a role in the metabolism of drugs

“…imbalance in the composition of this bacterial community can lead to transient intestinal dysfunctions and pathological states 28 . in recent years, an icreasing number of studies on the impact of several classes of drugs, xenobiotics and also dietary plant substances on the composition of the gut microbiota have been published 29 . The microbial intestinal metabolism is adversely affected especially by antibiotics 30 .…”

Agent‐Based Modeling in Molecular Systems Biology

“…Agent-based modeling (ABM) is a computational modeling paradigm that has been employed in a wide range of areas of research such as economics, [11,12] social sciences, [13,14] environmental engineering, [15,16] as well as biological studies including microbiome, [17][18][19] cancer, [20,21] and systems biology. [22][23][24] ABM…”