In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models

Area of Science:

• Computational biology
• Immunology
• Microbiology

Background:

• In vitro models are crucial for studying Mycobacterium tuberculosis (Mtb) infection and drug discovery.
• Analyzing complex data from advanced in vitro models requires sophisticated tools.

Purpose of the Study:

• To develop and validate an agent-based model (ABM) for simulating Mtb infection in traditional and spheroid in vitro cultures.
• To investigate host-pathogen interactions and predict outcomes of immune responses and drug screening.

Main Methods:

• An agent-based model (ABM) was created to represent immune cell and Mtb interactions.
• The ABM simulated traditional and spheroid cell cultures by adjusting cell movement and spatial parameters.
• Simulations were calibrated to experimental data, using identical parameters for both culture types.

Main Results:

• Simulations revealed heterogeneous bacterial counts and T cell infiltration in spheroid models.
• Predicted that equivalent activated macrophages do not guarantee similar bacterial reduction.
• Identified STAT1 activation as a rate-limiting step in macrophage activation within spheroids.

Conclusions:

• Host immune responses can control Mtb growth in both culture-dependent and independent ways.
• The choice of in vitro culture model can influence outcomes for drug screening and macrophage activation studies.
• The flexible ABM can represent diverse in vitro Mtb infection models, accelerating research discoveries.