Learning-accelerated discovery of immune-tumour interactions

Ozik, Jonathan; Collier, Nicholson; Heiland, Randy; An, Gary; Macklin, Paul

doi:10.1039/c9me00036d

Title: Learning-accelerated discovery of immune-tumour interactions

Journal Article · 07 June 2019 · Molecular Systems Design & Engineering

DOI: https://doi.org/10.1039/c9me00036d · OSTI ID:1574308

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Argonne National Lab. (ANL), Lemont, IL (United States). Decision and Infrastructure Sciences; Univ. of Chicago, Chicago, IL (United States). Consortium for Advanced Science and Engineering
Indiana Univ., Bloomington, IN (United States). Intelligent Systems Engineering
The Univ. of Vermont Medical Center, Burlington, VT (United States)

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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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: National Science Foundation (NSF); National Institutes of Health (NIH); USDOE

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1574308

Journal Information:: Molecular Systems Design & Engineering, Vol. 4, Issue 4; ISSN 2058-9689

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 21 works

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