High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow
Abstract
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 challengesmore »
- Authors:
-
- 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
- Publication Date:
- Research Org.:
- 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 Org.:
- USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA); National Inst. of Health (NIH) (United States); National Science Foundation (NSF)
- OSTI Identifier:
- 1493923
- Grant/Contract Number:
- AC02-06CH11357; AC02-05CH11231; R01GM115839; R01CA180149; S10OD018495; 1720625
- Resource Type:
- Accepted Manuscript
- Journal Name:
- BMC Bioinformatics
- Additional Journal Information:
- Journal Volume: 19; Journal Issue: S18; Journal ID: ISSN 1471-2105
- Publisher:
- BioMed Central
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; 97 MATHEMATICS AND COMPUTING; agent-based model; PhysiCell; cancer; immunotherapy; high throughput computing; EMEWS; hypothesis testing
Citation Formats
Ozik, Jonathan, Collier, Nicholson, Wozniak, Justin M., Macal, Charles, Cockrell, Chase, Friedman, Samuel H., Ghaffarizadeh, Ahmadreza, Heiland, Randy, An, Gary, and Macklin, Paul. High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow. United States: N. p., 2018.
Web. doi:10.1186/s12859-018-2510-x.
Ozik, Jonathan, Collier, Nicholson, Wozniak, Justin M., Macal, Charles, Cockrell, Chase, Friedman, Samuel H., Ghaffarizadeh, Ahmadreza, Heiland, Randy, An, Gary, & Macklin, Paul. High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow. United States. https://doi.org/10.1186/s12859-018-2510-x
Ozik, Jonathan, Collier, Nicholson, Wozniak, Justin M., Macal, Charles, Cockrell, Chase, Friedman, Samuel H., Ghaffarizadeh, Ahmadreza, Heiland, Randy, An, Gary, and Macklin, Paul. Fri .
"High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow". United States. https://doi.org/10.1186/s12859-018-2510-x. https://www.osti.gov/servlets/purl/1493923.
@article{osti_1493923,
title = {High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow},
author = {Ozik, Jonathan and Collier, Nicholson and Wozniak, Justin M. and Macal, Charles and Cockrell, Chase and Friedman, Samuel H. and Ghaffarizadeh, Ahmadreza and Heiland, Randy and An, Gary and Macklin, Paul},
abstractNote = {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.},
doi = {10.1186/s12859-018-2510-x},
journal = {BMC Bioinformatics},
number = S18,
volume = 19,
place = {United States},
year = {Fri Dec 21 00:00:00 EST 2018},
month = {Fri Dec 21 00:00:00 EST 2018}
}
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