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A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice
Janina K Geißert1, Erwin Bohn1, Reihaneh Mostolizadeh2,3,4,5
1Institute for Medical Microbiology and Hygiene, University Hospital Tübingen, Elfriede-Aulhorn-Str. 6, 72076 Tübingen, Germany.
Abstract:
The complex interplay of a pathogen with its virulence and fitness factors, the host's immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection. The expansion of a pathogen within the gastrointestinal tract implies an increased risk of developing severe systemic infections, especially in dysbiotic or immunocompromised individuals. We developed a mechanistic computational model that calculates and simulates such scenarios, based on an ordinary differential equation system, to explain the bacterial population dynamics during gastrointestinal infection. For implementing the model and estimating its parameters, oral mouse infection experiments with the enteropathogen, Yersinia enterocolitica (Ye), were carried out. Our model accounts for specific pathogen characteristics and is intended to reflect scenarios where colonization resistance, mediated by the endogenous microbiome, is lacking, or where the immune response is partially impaired. Fitting our data from experimental mouse infections, we can justify our model setup and deduce cues for further model improvement. The model is freely available, in SBML format, from the BioModels Database under the accession number MODEL2002070001.
Insights
This study presents a computational model simulating bacterial gastrointestinal infections. The model, validated with Yersinia enterocolitica mouse experiments, explains pathogen dynamics when host defenses are compromised.
Area of Science:
- Microbiology
- Computational Biology
- Immunology
Background:
- Gastrointestinal infections involve complex interactions between pathogens, host immunity, and the microbiome.
- Pathogen expansion increases systemic infection risk, particularly in immunocompromised or dysbiotic individuals.
Purpose of the Study:
- To develop a mechanistic computational model simulating bacterial population dynamics in gastrointestinal infections.
- To explain pathogen expansion scenarios with compromised colonization resistance or impaired immune responses.
Main Methods:
- Developed an ordinary differential equation-based computational model.
- Used oral mouse infection experiments with Yersinia enterocolitica (Ye) for model implementation and parameter estimation.
- Validated the model against experimental data to refine its setup.
Main Results:
- The model successfully simulates bacterial population dynamics during gastrointestinal infection.
- Experimental data from Yersinia enterocolitica mouse infections supported the model's justification.
- Identified areas for future model improvement based on experimental fitting.
Conclusions:
- The developed computational model provides insights into pathogen dynamics in compromised host states.
- The model is a valuable tool for understanding gastrointestinal infection outcomes.
- The model is available in SBML format for broader research use.
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