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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 on GranSim rules and specifications see the documentation file: gransimrules-v2.pdf

Parameter Table for GranSim: S1_Table.louisjos-May2021.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:


Multi-scale Gransim is a computational model that simulates the formation, function and treatment of tuberculosis (TB) granulomas in the lung that builds on GranSim. It is a hybrid model: ordinary differential equations describe the kinetics of molecular interactions and action of antibiotics; partial differential equations describe diffusion of molecules within the lung; an agent-based model captures the actions of individual immune cells and bacteria in a stochastic framework. The model is multi-scale, including molecular and cellular events that produce emergent behavior at the tissue scale. The framework accommodates multiple antibiotics, includes accounting of their pharmacokinetics and pharmacodynamics, and thus can predict the impact of antibiotic treatment on TB granulomas. The model is calibrated and validated against multiple datasets from non-human primates and humans.

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.

Antibiotic Drug Treatment in GranSim

To see all PKPD equations used in GranSim, view this file: PKPD_eqns.pdf

To see all PKPD code files used in GranSim, download and extract this file:

For a 3D version of GranSim click here.

Jose L. Segovia-Juarez, Suman Ganguli, and Denise Kirschner, Identifying control mechanism of granuloma formation during M. tuberculosis infection using an agent based model , Journal of Theoretical Biology. 231, Issue 3, pp 357-376, 2004, PMID: 15501468, PMCID: (exempt)

Errata - The Table in this paper has a small error: The correct T cell movement rate is 2 micrometers/minute.

J. Christian J. Ray, JoAnne L. Flynn, and Denise E. Kirschner, Synergy between Individual TNF-Dependent Functions Determines Granuloma Performance for Controlling Mycobacterium tuberculosis Infection. Journal of Immunology, 2009, 182: pp 3706-3717, DOI: 10.4049/jimmunol.0802297, PMID: 19265149, PMCID: 3182770.

Errata - Table 1 | Supplemental Information - Supplement Set 1

Fallahi-Sichani, M, El-Kebir, M, Marino, S, Kirschner, D*, Linderman, J. Multi-scale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation. Journal of Immunology, 2011, March 15, vol. 186, no. 6, pp 3472-3483 (corresponding author)., DOI: 10.4049/jimmunol.1003299, PMID: 21321109, PMCID: 3127549

Supplemental Information - Supplement Set 1

Nicholas A. Cilfone, Christopher B. Ford, Simeone Marino, Joshua T. Mattila, Hannah P. Gideon, JoAnne L. Flynn, Denise E. Kirschner and Jennifer J. Linderman, Computational Modeling Predicts IL-10 Control of Lesion Sterilization by Balancing Early Host Immunity-Mediated Antimicrobial Responses with Caseation during Mycobacterium tuberculosis Infection, J Immunol. 2015 Jan 15;194(2):664-77 DOI: 10.4049/jimmunol.1400734, PMID: 25512604, PMCID: 4283220

Elsje Pienaar, Nicholas A. Cilfone, Philana Ling Lin, Veronique Dartois, Joshua T. Mattila, J. Russell Butler, JoAnne L. Flynn, Denise E. Kirschner, Jennifer J. Linderman, A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment, Journal of Theoretical Biology (2015), pp. 166-179, published online: 24-DEC-2014 DOI: 10.1016/j.jtbi.2014.11.021, PMID: 25497475, PMCID: 4332617

Supplemental Information - Supplement1

Warsinske HC, Pienaar E, Linderman JJ, Mattila JT and Kirschner DE, Deletion of TGF-β1 Increases Bacterial Clearance by Cytotoxic T Cells in a Tuberculosis Granuloma Model, Front. Immunol. 8:1843, Accepted December 2017, DOI: 10.3389/fimmu.2017.01843, PMID: 29326718, PMCID: 5742530

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