High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Univ. of Chicago, IL (United States). Dept. of Surgery
- Opto-Knowledge Systems, Inc., Torrance, CA (United States)
- Univ. of Southern California, Los Angeles, CA (United States). Lawrence J. Ellison Center for Transformative Medicine
- Indiana Univ., Bloomington, IN (United States). Intelligent Systems Engineering
Cancer 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 technologies—one to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimization—can provide a computational means for high-throughput hypothesis testing, and eventually, optimization. In 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. While 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); Univ. of Chicago, IL (United States); Univ. of Southern California, Los Angeles, CA (United States); Indiana Univ., Bloomington, IN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA); National Inst. of Health (NIH) (United States); National Science Foundation (NSF)
- Grant/Contract Number:
- AC02-06CH11357; AC02-05CH11231; R01GM115839; R01CA180149; S10OD018495; 1720625
- OSTI ID:
- 1493923
- Journal Information:
- BMC Bioinformatics, Vol. 19, Issue S18; ISSN 1471-2105
- Publisher:
- BioMed CentralCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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