I lead PhysiCell, an open source, cross-platform agent-based modeling platform for multicellular systems.
This platform can simulate 10^5 or more cells with 5+ diffusing substrates (e.g., oxygen, metabolic factors, signaling factors, drugs) in 2D or 3D. PhysiCell uses off-lattice agents, where each cell agent has its own cell cycle status, death models, motility rules, secretion/uptake of chemical factors, basic mechanics, and growth rules. Each cell agent can have user-defined rules and custom data.
On top of the agent modeling platform, we are developing a software ecosystem. Any PhysiCell model can be rapidly converted into a cloud-hosted, interactive model using xml2jupyter, which opens up new possibilities for public outreach, education, and scientific communication and dissemination. Examples:
- PhysiCell model of cancer immunotherapy
- PhysiCell model of an adversarial microbial system
- PhysiCell version of Thanos vs. the Avengers
PhysiCell models can also be run on HPC (high performance computing) environments for massive model exploration, and machine learning can help to accelerate and interpret these investigations.
We are planning our first ever PhysiCell hackathon for July 2020, with some NCI funding available for students and early career researchers. We also have funds for two 6-week visitors for long-term PhysiCell projects in summer 2020.
- PhysiCell method paper
- PhysiCell wins PLoS Computational Biology Research Prize for Public Impact
- PhysiCell in high throughput on HPC
- Using HPC and Active Learning for accelerated discovery in immuno-oncology