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

Authored by Randy Heiland, Paul Macklin, Jonathan Ozik, Nicholson Collier, Justin M Wozniak, Charles Macal, Chase Cockrell, Samuel H Friedman, Ahmadreza Ghaffarizadeh, Gary An

Date Published: 2018

DOI: 10.1186/s12859-018-2510-x

Sponsors: United States Department of Energy (DOE)

Platforms: EMEWS

Model Documentation: Other Narrative

Model Code URLs: https://github.com/MathCancer/PhysiCell-EMEWS

Abstract

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.

Tags

Agent-based model Simulation systems biology cancer Model invasion Mechanisms Cells Sbml Therapy Breast-cancer Hypothesis testing Immunotherapy Physicell High throughput computing Emews