Speaker
Description
The coordination of the immune system is essential for maintaining health. Recent clinical studies show breast cancer patients with high dendritic cell (DC) clustering in tumour-draining lymph nodes have improved survival outcomes, when compared to those with a lower degree of DC clustering. However, the mechanistic basis for this spatial organization effect remains unclear.
We develop a spatially dynamic model of T cells interacting with Dendritic cells within the lymph node. We present a probabilistic agent-based model (ABM) of T cells, and use it to derive the deterministic, phenotypically structured partial differential equation (PS-PDE) of T cell activation and motion. Using the PS-PDE, we derive an analytic approximation of the expected level of T activation, based on the topology of a given Dendritic cell population. Our analytic approximation enables us to identify T cell characteristics that benefit most from Dendritic cell clustering, to result in an enhanced stimulation distribution. We perform a sensitivity analysis with our models, to identify T cell characteristics that result in desirable T cell activation.
Our key findings show that T cells with an intermediate level of stimulation uptake benefit most from higher levels of Dendritic cell clustering, activating with a comparable or greater abundance, and greater heterogeneity, when compared to T cells of a similar characteristic but with a lower level of Dendritic cell clustering.
