RT Journal Article SR Electronic T1 Learning-accelerated Discovery of Immune-Tumour Interactions JF bioRxiv FD Cold Spring Harbor Laboratory SP 573972 DO 10.1101/573972 A1 Jonathan Ozik A1 Nicholson Collier A1 Randy Heiland A1 Gary An A1 Paul Macklin YR 2019 UL http://biorxiv.org/content/early/2019/03/12/573972.abstract AB 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.