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Learning-accelerated discovery of immune-tumour interactions

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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.

Graphical abstract: Learning-accelerated discovery of immune-tumour interactions

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Publication details

The article was received on 08 Mar 2019, accepted on 18 Apr 2019 and first published on 07 Jun 2019


Article type: Paper
DOI: 10.1039/C9ME00036D
Mol. Syst. Des. Eng., 2019, Advance Article
  • Open access: Creative Commons BY-NC license
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    Learning-accelerated discovery of immune-tumour interactions

    J. Ozik, N. Collier, R. Heiland, G. An and P. Macklin, Mol. Syst. Des. Eng., 2019, Advance Article , DOI: 10.1039/C9ME00036D

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