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bioRxiv · 10.1101/196709

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

Ozik, J.·Collier, N.·Wozniak, J.·Macal, C.·Cockrell, C.·Friedman, S.·Ghaffarizadeh, A.·Heiland, R.·An, G.·Macklin, P.
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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 treatments 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.\n\nResultsIn 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.\n\nConclusionsWhile 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.

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Ozik, J., Collier, N., Wozniak, J., Macal, C., Cockrell, C., Friedman, S., Ghaffarizadeh, A., Heiland, R., An, G., Macklin, P.. 2017-09-30. High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow. https://doi.org/10.1101/196709

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