Agent-based model of the human colon to investigate mechanisms of pathogen colonization resistance

Recent global burden of disease studies have shown that bacterial infections are responsible for over 13 million deaths worldwide, or 1 in every 8 deaths, each year. Enteric diarrheal infections, in particular, pose a significant challenge and strain on healthcare systems as many are difficult to address pharmaceutically, and thus rely primarily on the patient's own immune system and gut microbiome to fight the infection. Nonetheless, the specific mechanisms behind gut microbiome colonization resistance of enteric pathogens are not well defined and microbiome diversity is difficult to represent and study experimentally. To address this gap, we have constructed an agent-based computational model of the colonic epithelium cross section to investigate the colonic invasion of enteric pathogens. The model focuses on three main regions: epithelial layer, mucosal bilayer, and adjacent lumen, and utilizes four main cell types as agents: anaerobic bacteria, facultative anaerobic bacteria, human goblet cells, and pathogens. Utilizing this model, we are able to describe the healthy microbiome cell localization and dynamics from our mucosal bilayer. In addition, we are also able to investigate the impact of host dietary fiber consumption and simulate pathogen invasion. The model exemplifies the possibility and potential to explore key gut microbiome colonization resistance mechanisms and environmental impacts on the gut microbiome using computational methods.

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Publication Details

Journal
Biophysical Journal
Published
2025-03-27
DOI
https://doi.org/10.1016/j.bpj.2025.03.022
Primary Topic
Yersinia bacterium, plague, ectoparasites research
Type
article
Field-Weighted Citation Impact
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article

Agent-based model of the human colon to investigate mechanisms of pathogen colonization resistance

Mark P. Suprenant, Carly Ching, Darash Desai, Muhammad H. Zaman et al.
Biophysical Journal
Yersinia bacterium, plague, ectoparasites research
article

Agent-based model of the human colon to investigate mechanisms of pathogen colonization resistance

Mark P. Suprenant, Carly Ching, Darash Desai, Muhammad H. Zaman, Stanley M. Fletcher
article en

Abstract

Recent global burden of disease studies have shown that bacterial infections are responsible for over 13 million deaths worldwide, or 1 in every 8 deaths, each year. Enteric diarrheal infections, in particular, pose a significant challenge and strain on healthcare systems as many are difficult to address pharmaceutically, and thus rely primarily on the patient's own immune system and gut microbiome to fight the infection. Nonetheless, the specific mechanisms behind gut microbiome colonization resistance of enteric pathogens are not well defined and microbiome diversity is difficult to represent and study experimentally. To address this gap, we have constructed an agent-based computational model of the colonic epithelium cross section to investigate the colonic invasion of enteric pathogens. The model focuses on three main regions: epithelial layer, mucosal bilayer, and adjacent lumen, and utilizes four main cell types as agents: anaerobic bacteria, facultative anaerobic bacteria, human goblet cells, and pathogens. Utilizing this model, we are able to describe the healthy microbiome cell localization and dynamics from our mucosal bilayer. In addition, we are also able to investigate the impact of host dietary fiber consumption and simulate pathogen invasion. The model exemplifies the possibility and potential to explore key gut microbiome colonization resistance mechanisms and environmental impacts on the gut microbiome using computational methods.

Biophysical JournalVol. 124(9)
Boston University (US), Boston Medical Center (US)
Zero hunger
Openalex Percentile: Top 93%
Yersinia bacterium, plague, ectoparasites research
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Agent-based model of the human colon to investigate mechanisms of pathogen colonization resistance — Mark P. Suprenant, Carly Ching, et al. · Biophysical Journal (2025) | TGRS Research Map | TGRS