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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: http://malthus.micro.med.umich.edu/lab/tb.html

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.


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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