In silicoagent-based modeling approach to characterize multiplein vitrotuberculosis infection models

Abstract

In vitro models of Mycobacterium tuberculosis (Mtb) infection are a valuable tool to examine host-pathogen interactions and screen drugs. With the development of more complex in vitro models, there is a need for tools to help analyze and integrate data from these models. We introduce an agent-based model (ABM) representation of the interactions between immune cells and bacteria in an in vitro setting. This in silico model was used to independently simulate both traditional and spheroid cell culture models by changing the movement rules and initial spatial layout of the cells. These two setups were calibrated to published experimental data in a paired manner, by using the same parameters in both simulations. Within the calibrated set, heterogeneous outputs are seen for outputs of interest including bacterial count and T cell infiltration into the macrophage core of the spheroid. The simulations are also able to predict many outputs with high time resolution, including spatial structure. The structure of a single spheroid can be followed across the time course of the simulation, allowing the relationship between cell localization and immune activation to be explored. Uncertainty analyses are performed for both model setups using latin hypercube sampling and partial rank correlation coefficients to allow for easier comparison, which can provide insight into ideal use cases for the independent setups. Future model iterations can be guided by the limitations of the current model, specifically which parts of the output space were harder to reach. This ABM can be used to represent more in vitro Mtb infection models due to its flexible structure, providing a powerful analysis tool that can be used in tandem with experiments.

Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma

“…In Petrucciani et al, both spheroid and traditional simulations were initialized to reflect the corresponding experimental conditions and were calibrated to experimental data at day 6. (13). We assumed parameters to be the same between traditional and spheroid simulations.…”

“…Limitations of the established model were discussed in Petrucciani et al (13) Briefly, simplifications were made to macrophage activation pathway and certain populations of T cells were excluded (i.e., regulatory and γδ). Another consideration is that asserting that differential bacterial control is solely due to dimension is a simplifying assumption.…”

“…The computational model established and calibrated in Petrucciani et al (13) was used to simulate the traditional and spheroid cell cultures. Briefly, this computational model is an agent-based model (ABM) with bacteria, macrophages, CD4+ and CD8+ T cells represented as agents.…”

“…In a computational model of the spheroid and traditional cultures, we found that one in silico model structure was able to represent both culture setups with shared parameters, that macrophage activation is dependent on IFN-γ from infiltrating T cells, and that bacterial growth could be controlled in a structure dependent or independent manner. (13) Here, we aim use this computational model to identify potential mechanistic reasons for the experimentally observed difference in bacterial control between spheroid and traditional cultures. Not only will this help us better understand how the structure of cultures impacts immune responses, but also can provide insights into how to engineer more complex in vitro models.…”