RT Journal Article
SR Electronic
T1 Model-based prediction of bacterial population dynamics in gastrointestinal infection
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.08.11.244202
DO 10.1101/2020.08.11.244202
A1 Janina K. Geißert
A1 Erwin Bohn
A1 Reihaneh Mostolizadeh
A1 Andreas Dräger
A1 Ingo B. Autenrieth
A1 Sina Beier
A1 Oliver Deusch
A1 Martin Eichner
A1 Monika S. Schütz
YR 2020
UL http://biorxiv.org/content/early/2020/08/12/2020.08.11.244202.abstract
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.