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bioRxiv · 10.1101/573972

Learning-accelerated Discovery of Immune-Tumour Interactions

Ozik, J.·Collier, N.·Heiland, R.·Macklin, P.
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Abstract

We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour-immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints.

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Ozik, J., Collier, N., Heiland, R., Macklin, P.. 2019-03-11. Learning-accelerated Discovery of Immune-Tumour Interactions. https://doi.org/10.1101/573972

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