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High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

Journal Article · · BMC Bioinformatics
 [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
+ Show Author Affiliations
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 Lab. (ANL), Argonne, IL (United States); Indiana Univ., Bloomington, IN (United States); Univ. of Chicago, IL (United States); Univ. of Southern California, Los Angeles, CA (United States)
Sponsoring Organization:
National Inst. of Health (NIH) (United States); National Science Foundation (NSF) (United States); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)
Grant/Contract Number:
AC02-05CH11231; AC02-06CH11357
OSTI ID:
1493923
Alternate ID(s):
OSTI ID: 1560659
Journal Information:
BMC Bioinformatics, Journal Name: BMC Bioinformatics Journal Issue: S18 Vol. 19; ISSN 1471-2105
Publisher:
BioMed CentralCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (7)

Hybrid modeling frameworks of tumor development and treatment journal July 2019
A Review of Cell-Based Computational Modeling in Cancer Biology journal November 2019
Learning-accelerated discovery of immune-tumour interactions journal January 2019
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
PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems journal February 2018
Key challenges facing data-driven multicellular systems biology text January 2018

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Related Subjects

60 APPLIED LIFE SCIENCES
97 MATHEMATICS AND COMPUTING
EMEWS
PhysiCell
agent-based model
cancer
high throughput computing
hypothesis testing
immunotherapy