The Role of Dimensionality in Understanding Granuloma Formation
- Univ. of Michigan Medical School, Ann Arbor, MI (United States). Dept. of Microbiology and Immunology; Univ. of Michigan, Ann Arbor, MI (United States). Statistics Online Computational Resource (SOCR), Dept.of Health Behavior and Biological Sciences; University of Michigan
- Univ. of Michigan Medical School, Ann Arbor, MI (United States). Dept. of Microbiology and Immunology; Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Chemical Engineering,
- Univ. of Michigan Medical School, Ann Arbor, MI (United States). Dept. of Microbiology and Immunology; Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Chemical Engineering
- Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Chemical Engineering
- Univ. of Michigan Medical School, Ann Arbor, MI (United States). Dept. of Microbiology and Immunology; Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Computational Medicine and Bioinformatics
Within the first 2–3 months of a Mycobacterium tuberculosis (Mtb) infection, 2–4 mm spherical structures called granulomas develop in the lungs of the infected hosts. These are the hallmark of tuberculosis (TB) infection in humans and non-human primates. A cascade of immunological events occurs in the first 3 months of granuloma formation that likely shapes the outcome of the infection. Understanding the main mechanisms driving granuloma development and function is key to generating treatments and vaccines. In vitro, in vivo, and in silico studies have been performed in the past decades to address the complexity of granuloma dynamics. This study builds on our previous 2D spatio-temporal hybrid computational model of granuloma formation in TB (GranSim) and presents for the first time a more realistic 3D implementation. We use uncertainty and sensitivity analysis techniques to calibrate the new 3D resolution to non-human primate (NHP) experimental data on bacterial levels per granuloma during the first 100 days post infection. Due to the large computational cost associated with running a 3D agent-based model, our major goal is to assess to what extent 2D and 3D simulations differ in predictions for TB granulomas and what can be learned in the context of 3D that is missed in 2D. Our findings suggest that in terms of major mechanisms driving bacterial burden, 2D and 3D models return very similar results. For example, Mtb growth rates and molecular regulation mechanisms are very important both in 2D and 3D, as are cellular movement and modulation of cell recruitment. The main difference we found was that the 3D model is less affected by crowding when cellular recruitment and movement of cells are increased. Overall, we conclude that the use of a 2D resolution in GranSim is warranted when large scale pilot runs are to be performed and if the goal is to determine major mechanisms driving infection outcome (e.g., bacterial load). To comprehensively compare the roles of model dimensionality, further tests and experimental data will be needed to expand our conclusions to molecular scale dynamics and multi-scale resolutions.
- Research Organization:
- Lawrence Berkeley National Lab (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1630007
- Journal Information:
- Computation, Journal Name: Computation Journal Issue: 4 Vol. 6; ISSN 2079-3197
- Publisher:
- MDPICopyright Statement
- Country of Publication:
- United States
- Language:
- English
Data-Driven Model Validation Across Dimensions
|
journal | March 2019 |
Similar Records
A Multi-Compartment Hybrid Computational Model Predicts Key Roles for Dendritic Cells in Tuberculosis Infection
A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination