skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: 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 » 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.« less

Authors:
 [1];  [1];  [1];  [1];  [2];  [3];  [4];  [5];  [2];  [5]
  1. Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Univ. of Chicago, IL (United States). Dept. of Surgery
  3. Opto-Knowledge Systems, Inc., Torrance, CA (United States)
  4. Univ. of Southern California, Los Angeles, CA (United States). Lawrence J. Ellison Center for Transformative Medicine
  5. Indiana Univ., Bloomington, IN (United States). Intelligent Systems Engineering
Publication Date:
Research Org.:
Argonne National Lab. (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:
Journal Article: 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. 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, and Macklin, Paul. Fri . "High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow". United States. doi: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},
issn = {1471-2105},
number = S18,
volume = 19,
place = {United States},
year = {2018},
month = {12}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Save / Share: