RCC Agent Learning Model

This project simulates an agent-based model for simulating RCC (Renal Cell Carcinoma) in a 3D environment. The model is designed to study the bioloigical function of the immune response and the interactions between different types of cellular agents, relying on data-driven learning procedures to simulate the function for specific patients.

The model can be run in 3D interface mode using Solara and pythreejs to visualize the agents in 3D.

Environment Setup

To ensure full reproducibility of the analysis, we recommend using Python 3.13 and running the project inside a dedicated Python virtual environment. Here is a step-by-step guide to set up the environment:

1. Create a virtual environment

python -m venv .venv

2. Activate the environment

Linux / macOS

source .venv/bin/activate

Windows (PowerShell)

.venv\Scripts\Activate.ps1

4. Install the project in editable mode

pip install -e .

5. Install required dependencies

Install the required dependencies with:

pip install -r requirements.txt

Running the Simulation

After having set up the environment and installed the dependencies, you can run the simulation using the following from the root directory:

python run.py

Reproducing Analysis Results

To reproduce the results, you can run the analysis.py notebook, which contains the code used to generate the figures and results presented in the paper "An Agent-based Learning Model Integrating Sex Differences in Renal Cell Carcinoma".