Simulation Design
The simulator of cell expansion was made using the Pygame module of python and is hosted on Zenodo and GitHub - https://github.com/Sakib1418/Game-of-cells. The simulation was designed to integrate with OpenAI gym , a collection of simulated environments and associated toolkits to test and compare RL agent algorithms. As the new gym environment was made, the Stable Baselines3 module was used on top of gym to explore current RL algorithms. The properties of the actors (cells) attempt to simulate actual CAR T-cells, for example movement and regeneration rate. Due to current lack of measured parameters such as conversion probability on encountering a bead, reasonable estimates are made in this initial work. All simulation values and cell parameters are listed in Table 2. To observe agent response with different cells, new cell types are conceptualized by changing these cell-properties (Table 1). How these parameters are formed into equations governing the fate of the cell and on the the culture environment or simulation trajectory overall is detailed in the game pseudocode (Supplement 2). Installation of the simulation-game, data analysis and reproduction of the plots and usage are detailed in the project GitHub repository.
Table 2: Parameters and their descriptions