PT  - JOURNAL ARTICLE
AU  - Janina K. Geißert
AU  - Erwin Bohn
AU  - Reihaneh Mostolizadeh
AU  - Andreas Dräger
AU  - Ingo B. Autenrieth
AU  - Sina Beier
AU  - Oliver Deusch
AU  - Martin Eichner
AU  - Monika S. Schütz
TI  - Model-based prediction of bacterial population dynamics in gastrointestinal infection
AID  - 10.1101/2020.08.11.244202
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.08.11.244202
4099  - http://biorxiv.org/content/early/2020/08/12/2020.08.11.244202.short
4100  - http://biorxiv.org/content/early/2020/08/12/2020.08.11.244202.full
AB  - The complex interplay of a pathogen with the host immune response and the endogenous microbiome determines the course and outcome of gastrointestinal infection (GI). Expansion of a pathogen within the gastrointestinal tract implies an increased risk to develop systemic infection. Through computational modeling, we aimed to calculate bacterial population dynamics in GI in order to predict infection course and outcome. For the implementation and parameterization of the model, oral mouse infection experiments with Yersinia enterocolitica were used. Our model takes into account pathogen specific characteristics, such as virulence, as well as host properties, such as microbial colonization resistance or immune responses. We were able to confirm the model calculations in these scenarios by experimental mouse infections and show that it is possible to computationally predict the infection course. Far future clinical application of computational modeling of infections may pave the way for personalized treatment and prevention strategies of GI.Competing Interest StatementThe authors have declared no competing interest.