Design Consideration in the game CancerGDV

Introduction

This code is a simple demonstration of 3-strategy evolution cancer tumor: glycoltyic cells (GLY), vascular overproducers (VOP), and defectors (DEF) proposed by Gluzman

This work is in progress, any contributions or issues are welcome on GitHub at: https://github.com/sangttruong/cancergame

Installation

Install and run the code as the following command

git clone https://github.com/sangttruong/cancergame

Gallery

We also provide a sample visualization from our code. Some demos:

Initialize cancer game without user input

Initialize cancer game with user input

Dependencies

• python (version>=3.7)
• numpy (version>=1.19.3)
• matplotlib (version>=3.3.0)

Authors

This repository is currently maintained by Sang Truong, Hieu Tran, and Steven Borgaert.

Acknowledgements

We thank Andy Le, Brian Howard, Dee Wu for their initial work during Summer 2020.

References

• Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature: https://pubmed.ncbi.nlm.nih.gov/28183139/
• Optimizing Cancer Treatment Using Game Theory: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6947530/
• Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory: https://royalsocietypublishing.org/doi/pdf/10.1098/rspb.2019.2454
• egtplot: A Python Package for Three-Strategy Evolutionary Games: https://github.com/mirzaevinom/egtplot. Paper: https://www.biorxiv.org/content/10.1101/300004v2.full.pdf
• PDE numerical method: https://www.youtube.com/watch?v=ZSNl5crAvsw
• EAAI 2022 challenge: http://cs.gettysburg.edu/~tneller/games/aiagd/index.html