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The Agent-based model (ABM) describing tuberculosis (TB)
granuloma formation and function in the lung
GranSim is a hybrid agent-based computational model (ABM) that describes the formation and function of a granuloma during Mycobacterium tuberculosis infection in the lung. Although granulomas are 3D entities, due to the high computational costs associated to simulate a 3D version, GranSim has been developed and curated since 2004 only in 2D. [2-4, 6-10] This website and the recent manuscript [hyperlink here] illustrate our first attempt to simulate a 3D TB granuloma in the lung.
GranSim captures cellular behaviour of Macrophages (i.e., resting, activated, infected and chronically infected) and T lymphocytes (i.e., CD4+, CD8+, and regulatory), bacterial behaviour (i.e., intracellular replicating, extracellular replicating, and extracellular non-replicating) and molecule dynamics (degradation and diffusion of relevant cytokines and chemokines, such as TNF, IL10). Bacteria can be simulated either as agents or as continuous entities.
The initial 3D environment comprises a grid of 100x100x100 micro-compartments, each one with size 20x20x20 mm. The size of each micro compartment has been determined by matching the typical size of the largest cells in the model (i.e., macrophages), reconciling their average speed with GranSim iteration clock (i.e., 10 minutes) [Ref Segovia]. Each micro-compartment can accommodate at most one macrophage and one T cell (typically half the size of a macrophage), or 2 T cells. The grid is initialized with a certain density of vascular sources, as well as of resident macrophages. The infection is triggered by a single infected macrophage placed in the center of the grid.
Upon infection, macrophages start to secrete danger signals represented by molecules such as cytokines and chemokines. The main cytokines modelled are Tumor Necrosis Factor (TNF) and Intelukin-10 (IL-10), representing the main branch of pro and anti-inflammatory cues during infection progression. We also model 3 chemokines (i.e., CCL2, CCL5 and CXCL9/10/11) and they are used to differentially drive movement of cells. Cytokines and chemokines are secreted by cells and diffuse on the grid. The diffusion process is modelled as a PDE and the numerical solver used is the FFT based algorithm implemented using the FFTW library (see Press WHT, Saul A.; Vetterling, William T.; Flannery,, P. B. Numerical Recipes: The Art of Scientific Computing (3rd ed). 3rd ed. New York: Cambridge University Press; 2007). The diffusion time step is 60 seconds.
Example of 3D-GranSim Time Lapse Simulations
3-Dimensional Granuloma Simulator
For more details on GranSim rules and specifications see the documentation file:
Parameter Table for GranSim:
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:
For a 2D version of GranSim click here.
Simeone Marino, Caitlin Hult, Paul Wolberg, Jennifer J. Linderman, Denise E. Kirschner,
The Role of Dimensionality in Understanding Granuloma Formation,
Computation 2018, 6(4), 58,
PMCID: (pending), NIHMSID: (pending)
(This article belongs to the Special Issue:
Computational Modeling in Inflammation and Regenerative Medicine edited by Profs. Dr. Yoram Vodovotz and Dr. Rami Namas)
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,
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
Supplemental Information -
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
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).,
Supplemental Information -
Supplement Set 1
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,
Table 1 | Supplemental Information -
Supplement Set 1
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,
Errata - The Table in this paper has a small error: The correct T cell
movement rate is 2 micrometers/minute.