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

• Published in: BMC bioinformatics Vol. 19; no. Suppl 18; pp. 483 - 97
• Main Authors: Ozik, Jonathan, Collier, Nicholson, Wozniak, Justin M., Macal, Charles, Cockrell, Chase, Friedman, Samuel H., Ghaffarizadeh, Ahmadreza, Heiland, Randy, An, Gary, Macklin, Paul
• Format: Journal Article
• Language: English
• Published: London BioMed Central 21.12.2018; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: 60 APPLIED LIFE SCIENCES; Active learning; Agent-based model; Algorithms; Analysis; Antigens; Bioinformatics; Biology; Biomechanics; Biomedical and Life Sciences; Cancer; Cancer immunotherapy; Cancer therapies; Care and treatment; Computation; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer applications; Computer simulation; Dynamical systems; EMEWS; Experiments; Failure; Gene expression; High throughput computing; High-throughput screening (Biochemical assaying); Host systems; Humans; Hypotheses; hypothesis testing; Hypoxia; Immune system; Immunotherapy; Knowledge representation; Life Sciences; Mathematical models; MATHEMATICS AND COMPUTING; Methodology; Microarrays; Models, Theoretical; Multiscale analysis; Neoplasms - diagnosis; Optimization; Parameters; PhysiCell; Scale models; Space exploration; Treatment outcome; Tumor cells; Workflow; Hypothesis testing; Immunotherapy; EMEWS; PhysiCell; High throughput computing; Agent-based model; Cancer
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2510-x

Background 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. Results 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. Conclusions 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.