Model-based prediction of bacterial population dynamics in gastrointestinal infection

Abstract

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.

Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection

“…Our model for Ye, as described in Geißert et al [10], considers three different sites with their individual population dynamics. Figure 1 depicts an overview of the model in the form of a Systems Biology Graphical Notation (SBGN) [24] Process Description (PD) diagram [25] based on an illustration by Geißert et al [10]. The lumen and the mucosal site of the small intestine are illustrated as two separate compartments by abbreviated notations L and M in Table 1.…”

“…Some of these processes receive stimulating effects from the immune reaction or from the size of the Ye populations within the mucosa, as colored arcs indicate. Reference [10] provides a more detailed description of the model's structure. Table 1.…”

“…Hence, we assume a much lower carrying capacity of the mucosal site compared to the luminal compartment. Another assumption is that as soon as bacterial numbers exceed the mucosa's carrying capacity, they spill-over to the luminal site, feeding the luminal populations [10].…”

“…This immune activation increases with the number of Ye cells in the mucosa. By triggering the killing of all bacterial cells at the mucosal site, this leads to a reduction in bacterial populations spilling over to the lumen [10].…”

“…Especially in such patients, identifying those at high risk of developing fatal diseases could help tailor personalized therapeutic interventions to improve the outcome of infection. To this aim, we recently developed a computational model that can calculate Ye population dynamics during gastrointestinal infection and predict pathogen expansion, gut colonization, and infection course [10].…”