Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics...

Area of Science:

• Evolutionary dynamics
• Cancer biology
• Game theory applications

Background:

• Understanding evolutionary dynamics is crucial for controlling biological systems.
• Similarities between ecosystems and cellular tissues suggest game theory can offer insights into cancer progression.
• Disseminating methods to compare game theoretic models with experimental data is essential.

Purpose of the Study:

• To present a tutorial on an analysis method for game theoretic dynamics equations.
• To enable training parameters, validating equations, and making predictions for cancer treatment strategies.
• To facilitate the comparison of game theoretic models with experimental measurements of population dynamics.

Main Methods:

• Adapted data analysis techniques from reaction kinetics (method of initial rates).
• Focus on training parameters in game theoretic dynamics equations.
• Validation of derived equations and prediction generation for challenging models.

Main Results:

• The tutorial provides a method to train and validate game theoretic models for biological systems.
• The approach allows for predictions to challenge existing models and inform treatment strategies.
• The method avoids computer programming, making it accessible for routine laboratory use.

Conclusions:

• The presented analysis method can bridge the gap between theoretical models and experimental data in cancer research.
• This approach empowers researchers to utilize game theory for a deeper understanding and control of cancer evolution.
• Simplifying complex analyses promotes wider adoption and integration into experimental cancer research workflows.