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High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow
Jonathan Ozik1, Nicholson Collier1, Justin M Wozniak1
1Argonne National Laboratory, Argonne, IL, USA.
BMC Bioinformatics
|December 23, 2018
Summary
We developed a computational framework integrating PhysiCell and EMEWS for high-throughput cancer modeling. This approach aids in testing hypotheses and understanding therapeutic failure in complex cancer systems.
Area of Science:
- Computational biology
- Systems biology
- Cancer research
Background:
- Cancer is a complex multiscale system involving tumor-host interactions.
- Therapeutic outcomes can be unpredictable due to this complexity.
- Mechanistic computational models are valuable but challenging to explore due to high dimensionality and biological uncertainty.
Purpose of the Study:
- To develop a computational framework for high-throughput hypothesis testing in cancer.
- To integrate agent-based modeling with advanced model exploration techniques.
- To systematically investigate factors influencing cancer treatment efficacy.
Main Methods:
- Integration of PhysiCell (a 3-D multicellular simulator) with EMEWS (an extreme-scale model exploration platform).
- Development of a generalized workflow for high-throughput cancer hypothesis testing.
- Utilizing hundreds or thousands of mechanistic simulations for hypothesis optimization.
Main Results:
- Demonstration of the PhysiCell-EMEWS framework applied to 3-D cancer immunotherapy.
- Gained insights into mechanisms of therapeutic failure.
- Established a workflow for comparing simulations against data-driven error metrics.
Conclusions:
- Combining mechanistic agent-based models with high-throughput exploration environments enables rapid and systematic cancer research.
- These computational experiments can deepen biological understanding and guide future research.
- The approach has the potential to inform clinical practice by improving treatment strategies.
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