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This granuloma snapshot taken from a Non-human primate from Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome. It shows a caseous necrotic granuloma central core, cuff of lymphocytes, and an inner ring of macrophages.

The Agent-based model (ABM) describing tuberculosis (TB) granuloma formation and function in the lung

GranSim, the Agent-based model (ABM) describing tuberculosis (TB) granuloma formation and function in the lung, was developed based on four basic concepts: an environment (section of the lung parenchyma), agents (immune cells), ABM rules that govern the agents and their interactions, and the time-step (Δt) used to update events. The attached documentation illustrates the details of how each of these features have been implemented in the form of a pseudocode. The model was first published in 2004 but has been continually updated to include the latest biological information and technological advances.

Example of GranSim Time Lapse Simulation

2-Dimensional Granuloma Simulator

For more details see the documentation file - ABMrules-doc.v3a2.pdf

For more information regarding each individual type of model we use GranSim in (multi-scale, multi-compartment, molecular details, etc) please see our individual publications on those topics at:

One multi-scale adaptation of GranSim is GranSim-CBM, which integrates metabolic and agent-based modeling. GranSim-CBM predicts how growth adaptations of Mycobacterium tuberculosis affects granuloma scale outcomes of infection.

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