A model of tuberculosis progression using CompuCell3D

TL;DR

A novel TB within-host dynamics modelling framework developed using CompuCell3D, an open-source computer software used for simulating cellular biological processes both within and between cells 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.

Abstract

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 paper, we 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. We used this model to compare the generated results with those from a previously developed within-host infectious disease model. We found that, although the results of our CC3D model mostly agree qualitatively with those from the previously developed model, there are quantitative differences. Additionally, we 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 our CC3D model of TB progression with another agent-based modelling approach to the same problem.

Figures (14)

• Figure 1: Schematic describing the basic processes of the model (modified from Bowness2018). An arrow from box A to box B indicates box A impacts upon box B. Abbreviations: PDE, partial differential equation; ABM, agent-based model.
• Figure 2: Representation of a pixel-copy attempt by one of the pixels of a slow-growing extracellular bacterium to replace one of the surrounding Medium pixels on the two-dimensional square lattice during a MCS. Here, $P$ represents the probability of moving to the cell configuration pointed to by the arrow. The two images on the right-hand side of the figure show two potential outcomes of a pixel-copy attempt, leading to two different potential cell configurations - the top configuration where the pixel-copy attempt is successful; the bottom configuration where it is not.
• Figure 3: Visual illustration that the number of times each recruitment site will be counted for a given blood vessel will sum to 56. The blue $5 \times 5$ square represents a blood vessel in our model.
• Figure 4: Flowchart for extracellular bacteria transition rules. Each extracellular bacterium checks the scaled average oxygen concentration across their volume at time $t$, $\overline{O}'(t)$. If it is above threshold $O_{high}$, it becomes (or remains) fast-growing. Otherwise, if it is below threshold $O_{low}$, it becomes (or remains) slow-growing. Otherwise, it remains in its current state, either fast-growing or slow-growing. Abbreviations: EB, extracellular bacterium; FGEB, fast-growing extracellular bacterium; SGEB, slow-growing extracellular bacterium.
• Figure 5: Flowcharts for macrophage transition rules due to phagocytosis. Here, "neighbouring" is defined as being within the von Neumann neighbourhood of range 1 for any of the boundary pixels of the macrophage in question. For more details, see Section \ref{['sec: cell-state transitions']}. Abbreviations: $M_{\phi}$, macrophage; RM, resting macrophage; IM, infected macrophage; CIM, chronically infected macrophage; EB, extracellular bacteria; $B_I$, intracellular bacterial load.