Learning-accelerated discovery of immune-tumour interactions
Journal Article
·
· Molecular Systems Design & Engineering
- 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.
- Research Organization:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- National Institutes of Health (NIH); National Science Foundation (NSF); USDOE
- Grant/Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1574308
- Journal Information:
- Molecular Systems Design & Engineering, Journal Name: Molecular Systems Design & Engineering Journal Issue: 4 Vol. 4; ISSN 2058-9689; ISSN MSDEBG
- Publisher:
- Royal Society of ChemistryCopyright Statement
- Country of Publication:
- United States
- Language:
- English
| Key challenges facing data-driven multicellular systems biology | text | January 2018 |
| Utilizing Differential Evolution into optimizing targeted cancer treatments | preprint | January 2020 |
| Evolving Nano Particle Cancer Treatments with Multiple Particle Types | preprint | January 2020 |
| Supporting Computational Apprenticeship Through Educational and Software Infrastructure: A Case Study in a Mathematical Oncology Research Lab | journal | February 2021 |
Key challenges facing data-driven multicellular systems biology
|
journal | October 2019 |
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