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Title: Learning-accelerated discovery of immune-tumour interactions

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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [2]
  1. 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
  2. Indiana Univ., Bloomington, IN (United States). Intelligent Systems Engineering
  3. The Univ. of Vermont Medical Center, Burlington, VT (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Science Foundation (NSF); National Institutes of Health (NIH); USDOE
OSTI Identifier:
1574308
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Molecular Systems Design & Engineering
Additional Journal Information:
Journal Volume: 4; Journal Issue: 4; Journal ID: ISSN 2058-9689
Publisher:
Royal Society of Chemistry
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES

Citation Formats

Ozik, Jonathan, Collier, Nicholson, Heiland, Randy, An, Gary, and Macklin, Paul. Learning-accelerated discovery of immune-tumour interactions. United States: N. p., 2019. Web. doi:10.1039/c9me00036d.
Ozik, Jonathan, Collier, Nicholson, Heiland, Randy, An, Gary, & Macklin, Paul. Learning-accelerated discovery of immune-tumour interactions. United States. doi:10.1039/c9me00036d.
Ozik, Jonathan, Collier, Nicholson, Heiland, Randy, An, Gary, and Macklin, Paul. Fri . "Learning-accelerated discovery of immune-tumour interactions". United States. doi:10.1039/c9me00036d. https://www.osti.gov/servlets/purl/1574308.
@article{osti_1574308,
title = {Learning-accelerated discovery of immune-tumour interactions},
author = {Ozik, Jonathan and Collier, Nicholson and Heiland, Randy and An, Gary and Macklin, Paul},
abstractNote = {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.},
doi = {10.1039/c9me00036d},
journal = {Molecular Systems Design & Engineering},
number = 4,
volume = 4,
place = {United States},
year = {2019},
month = {6}
}

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