
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
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- 4901 Applied mathematics
- 4606 Distributed computing and systems software
- 4602 Artificial intelligence
- 0806 Information Systems
- 0802 Computation Theory and Mathematics
Citation

Published In
DOI
ISSN
Publication Date
Volume
Related Subject Headings
- 4901 Applied mathematics
- 4606 Distributed computing and systems software
- 4602 Artificial intelligence
- 0806 Information Systems
- 0802 Computation Theory and Mathematics