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:
-
- 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)
- Publication Date:
- Research Org.:
- Argonne National Laboratory (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. https://doi.org/10.1039/c9me00036d
Ozik, Jonathan, Collier, Nicholson, Heiland, Randy, An, Gary, and Macklin, Paul. Fri .
"Learning-accelerated discovery of immune-tumour interactions". United States. https://doi.org/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 = {Fri Jun 07 00:00:00 EDT 2019},
month = {Fri Jun 07 00:00:00 EDT 2019}
}
Web of Science
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