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

Abstract

AbstractIn vitromodels ofMycobacterium tuberculosis (Mtb)infection are a valuable tool to examine host-pathogen interactions and screen drugs. With the development of more complexin vitromodels, 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 anin vitrosetting. Thisin silicomodel 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 morein vitro Mtbinfection models due to its flexible structure, providing a powerful analysis tool that can be used in tandem with experiments.Author SummaryTuberculosis is an infectious disease that causes over 1.4 million deaths every year. During infection, immune cells surround the bacteria forming structures called granulomas in the lungs. New laboratory models generate spheroids that aim to recreate these structures to help understand infection and find new ways to treat tuberculosis. Computational modeling is used to compare these newer spheroid models to traditional models, which don’t recreate the structure of the cell clusters. After calibration to data from laboratory experiments to ensure that the computational model can represent both systems, the structures were characterized over time. The traditional and spheroid model were also compared by looking at how model inputs impact outputs, allowing users to figure out when one model should be used over the other. This computational tool can be used to help integrate data from different laboratory models, generate hypothesis to be tested in laboratory models, and predict pathways to be targeted by drugs.