High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow
BackgroundCancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatment's success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologiesone to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimizationcan provide a computational means for high-throughput hypothesis testing, and eventually, optimization.ResultsIn this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization.ConclusionsWhile key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science - Office of Advanced Scientific Computing Research; National Institutes of Health (NIH)
- DOE Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1560659
- Resource Relation:
- Conference: 2017 Computational Approaches for Cancer at SC17, 11/17/17 - 11/17/17, Denver, CO, US
- Country of Publication:
- United States
- Language:
- English
Hybrid modeling frameworks of tumor development and treatment
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journal | July 2019 |
Learning-accelerated discovery of immune-tumour interactions
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journal | January 2019 |
Key challenges facing data-driven multicellular systems biology
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journal | October 2019 |
Supporting Computational Apprenticeship Through Educational and Software Infrastructure: A Case Study in a Mathematical Oncology Research Lab | journal | February 2021 |
PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems
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journal | February 2018 |
Key challenges facing data-driven multicellular systems biology | text | January 2018 |
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