Establishing a massively parallel computational model of the adaptive immune response

Parallel agent-based models of the adaptive immune response can efficiently recapitulate emerging spatiotemporal properties of T-cell motility during clonal selection across multiple length and time scales. Here, we present a distributed, three-dimensional (3D) computational model of T-cell priming, and associated parallel data structures and algorithms that enable fully deterministic cell simulations at scale. We demonstrate performant usage of modern clusters with over 350x speedup, and explore trade-offs between simulation accuracy, code complexity, and communication overhead. This study highlights the potential for parallel 3D models to explore immunological research questions and guides implementation and performance considerations for this class of biology-inspired agent-based models.

Duke Scholars

Author Amanda Randles Biomedical Engineering