A Model of Tuberculosis Progression using CompuCell3D

Tuberculosis (TB) is an airborne disease caused by the bacterium Mycobacterium tuberculosis (M. tb). Prior to the COVID-19 pandemic, TB was the leading cause of death from an infectious agent globally. However, most people exposed to M. tb do not develop active TB and go on to display symptoms. Instead, in the majority of cases, the bacteria are contained within a granuloma (an aggregation of immune cells) without being eliminated; this is called latent TB. The spatial organisation of the bacteria and immune cells is important in determining whether an individual exposed to M. tb will develop latent or active TB. In this seminar, I present a multi-cell, multiscale model of TB progression to investigate the importance of the spatial organisation. This is a novel TB within-host dynamics modelling framework, having been developed using CompuCell3D (CC3D), an open-source computer software used for simulating cellular biological processes both within and between cells. I used this model to compare the generated results with those from a previously developed within-host infectious disease model. I found that, although the results of the CC3D model mostly agree qualitatively with those from the previously developed model, there are quantitative differences. Additionally, I conducted a robustness analysis of key model parameters from the CC3D model to determine their importance to the CC3D model output, using a methodology specifically designed for agent-based models. The model output appears to be robust in response to perturbations in parameters controlling chemotactic movement, but less so in response to perturbations in parameters controlling persistence of movement in cells, cell adhesion and volume constraints. This work compares my CC3D model of TB progression with another agent-based modelling approach to the same problem. For more information see: Doran, James WG, Christopher F. Rowlatt, Gibin G. Powathil, Ruth Bowness, and Christian A. Yates. "A model of tuberculosis progression using CompuCell3D." arXiv preprint arXiv:2602.24258 (2026). https://arxiv.org/pdf/2602.24258 and Hamis, Sara, Stanislav Stratiev, and Gibin G. Powathil. "Uncertainty and sensitivity analyses methods for agent-based mathematical models: An introductory review." The physics of cancer: Research advances (2021): 1-37. https://www.worldscientific.com/doi/pdf/10.1142/9789811223495_0001 *Contents* 00:00 - Introduction 04:43 - A Model of Tuberculosis Progression using CompuCell3D 53:12 - Discussion and Questions For a copy of the slides for this video visit: https://drive.google.com/file/d/1YS_-JR-WmslnCrjh522UFqLV0OIa7FHw Moderator: James A. Glazier If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/