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Article

A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice

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Institute for Medical Microbiology and Hygiene, University Hospital Tübingen, Elfriede-Aulhorn-Str. 6, 72076 Tübingen, Germany
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Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Sand 14, 72076 Tübingen, Germany
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Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
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German Center for Infection Research (DZIF), Partner Site Tübingen, 72076 Tübingen, Germany
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Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, 72076 Tübingen, Germany
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CEMET GmbH, Eisenbahnstr. 63, 72072 Tübingen, Germany
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Epimos GmbH, Frühlingstraße 2, 97653 Bischofsheim in der Rhön, Germany
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Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Silcherstraße 5, 72076 Tübingen, Germany
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Vladimir Kaberdin
Biology 2022, 11(2), 297; https://doi.org/10.3390/biology11020297
Received: 7 December 2021 / Revised: 8 February 2022 / Accepted: 9 February 2022 / Published: 12 February 2022
(This article belongs to the Section Microbiology)
Computational modeling of bacterial infection is an attractive way to simulate infection scenarios. In the long-term, such models could be used to identify factors that make individuals more susceptible to infection, or how interference with bacterial growth influences the course of bacterial infection. This study used different mouse infection models (immunocompetent, lacking a microbiota, and immunodeficient models) to develop a basic mathematical model of a Yersinia enterocolitica gastrointestinal infection. We showed that our model can reflect our findings derived from mouse infections, and we demonstrated how crucial the exact knowledge about parameters influencing the population dynamics is. Still, we think that computational models will be of great value in the future; however, to foster the development of more complex models, we propose the broad implementation of the interdisciplinary training of mathematicians and biologists.
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. View Full-Text
Keywords: infection; systems biology; computational modeling; population dynamics; gastrointestinal infection; ordinary differential equations; parameter estimation; Yersinia enterocolitica infection; systems biology; computational modeling; population dynamics; gastrointestinal infection; ordinary differential equations; parameter estimation; Yersinia enterocolitica
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MDPI and ACS Style

Geißert, J.K.; Bohn, E.; Mostolizadeh, R.; Dräger, A.; Autenrieth, I.B.; Beier, S.; Deusch, O.; Renz, A.; Eichner, M.; Schütz, M.S. A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice. Biology 2022, 11, 297. https://doi.org/10.3390/biology11020297

AMA Style

Geißert JK, Bohn E, Mostolizadeh R, Dräger A, Autenrieth IB, Beier S, Deusch O, Renz A, Eichner M, Schütz MS. A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice. Biology. 2022; 11(2):297. https://doi.org/10.3390/biology11020297

Chicago/Turabian Style

Geißert, Janina K., Erwin Bohn, Reihaneh Mostolizadeh, Andreas Dräger, Ingo B. Autenrieth, Sina Beier, Oliver Deusch, Alina Renz, Martin Eichner, and Monika S. Schütz. 2022. "A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice" Biology 11, no. 2: 297. https://doi.org/10.3390/biology11020297

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