Tumor Growth Cellular Automaton

A Python-based cellular automaton simulation for modeling tumor growth, angiogenesis, and mutation dynamics. Features an interactive web interface built with Gradio for real-time visualization.

Overview

This project is a Python port of camodel.m, implementing a 2D cellular automaton that simulates:

• Tumor proliferation — Cancer cells divide and spread based on local microenvironment conditions
• Angiogenesis — Blood vessels sprout and grow toward the tumor mass
• Mutation — Normal tumor cells can mutate into more aggressive variants
• Metastasis — Tumor cells can spread via the vascular system

Features

• 🔬 Three simulation conditions: Control, Aggressive, and Drug Treatment scenarios
• 📊 Real-time visualization: Watch the tumor evolve iteration by iteration
• 📈 Statistical tracking: Growth curves and survival rate plots
• ⚡ Configurable parameters: Adjust grid size and iteration count
• 🌐 Web interface: Easy-to-use Gradio UI

Installation

# Clone the repository
git clone https://github.com/aimed-lab/MLPA_demo.git
cd MLPA_demo

# Install dependencies
pip install gradio numpy matplotlib scipy pillow

Usage

python app.py

Then open your browser to the local URL displayed (typically http://127.0.0.1:7860).

Interface Controls

Parameter	Description	Range
Condition	Simulation scenario	Control, Aggressive, DrugTreatment
Iterations	Number of simulation steps	50–500
Grid Size	Simulation grid dimensions	50–300

Simulation Conditions

Condition	Tumor Growth	Death Rate	Mutation	Metastasis	Description
Control	Moderate	High	Low	Low	Baseline tumor behavior
Aggressive	High	High	Medium	High	Fast-growing, highly metastatic
DrugTreatment	Low	High	Very Low	Very Low	Simulates therapeutic intervention

Cell Types & Visualization

Color	Cell Type	Description
⬜ White	Normal tissue	Healthy cells
🟦 Blue	Tumor	Primary tumor cells
🩵 Light Blue	Mutated tumor	Mutated variant cells
🩷 Pink	Blood vessel	Original and grown vasculature
🟥 Dark Red	Sprouting vessel	Actively growing vessel tip

How It Works

1. Initialization: A small tumor seed is placed at the grid center; a blood vessel runs along one edge.

2. Tumor Growth: Each iteration, tumor cells probabilistically convert neighboring normal cells. Growth rate is modulated by a spatially-varying microenvironment map.

3. Angiogenesis: Vessel cells can "break" and sprout toward the tumor. Sprouts navigate using a combination of chemotaxis (toward tumor) and random movement.

4. Mutation: Tumor cells have a small probability of mutating into a more aggressive phenotype each iteration.

5. Metastasis: Once the tumor reaches a critical size and contacts vasculature, cells can seed new tumors along blood vessels.

Output

The simulation produces:

• Live grid visualization — Updated every 5 iterations
• Growth statistics plot — Tracks normal and mutated tumor cell counts
• Survival curve — Estimates net survival based on tumor burden

Project Structure

MLPA_demo/
├── app.py          # Main application with simulation logic and Gradio UI
└── README.md       # This file

Requirements

• Python 3.8+
• NumPy
• Matplotlib
• SciPy
• Pillow
• Gradio

License

MIT License — see LICENSE for details.

Acknowledgments

Based on the MATLAB cellular automaton model camodel.m for tumor growth simulation.