Organoid Drug Response Simulator

LINK - https://organoiddresponse.leandrenash.com/

A cutting-edge scientific simulation platform for studying drug responses in organoids using advanced machine learning and interactive data visualization technologies.

Overview

This project provides a web-based interface for simulating and analyzing how organoids respond to various drugs. It combines biological simulation with machine learning predictions to help researchers understand drug effectiveness.

Features

• Interactive Simulation: Real-time visualization of organoid growth patterns
• Drug Response Modeling: Simulate effects of different drug concentrations
• Machine Learning Integration: Predictive modeling of drug effectiveness
• Data Export: Export simulation results for further analysis

Technical Stack

Core Technologies

• Python 3.11: Primary programming language
• Streamlit: Web interface framework
• Plotly: Interactive data visualization
• Scikit-learn: Machine learning implementation
• NumPy/Pandas: Data manipulation and analysis

Components

1. Simulation Engine (simulation.py)

   • Implements OrganoidSimulator class
   • Uses logistic growth model for organoid simulation
   • Handles drug concentration effects
   • Generates synthetic data for ML training

2. Machine Learning Model (ml_model.py)

   • DrugResponsePredictor class using Random Forest
   • Feature scaling with StandardScaler
   • Handles model training and predictions
   • Includes performance metrics

3. Visualization Module (visualization.py)

   • Interactive Plotly graphs
   • Growth curve visualization
   • Prediction accuracy plots
   • Real-time data updates

4. Web Interface (app.py)

   • Streamlit-based dashboard
   • Parameter controls in sidebar
   • Split view for simulation and ML predictions
   • Data export functionality

Getting Started

1. Install dependencies:

   pip install streamlit numpy pandas plotly scikit-learn

2. Run the application:

   streamlit run app.py

3. Access the interface at http://localhost:5000

Usage

1. Adjust Parameters:

   • Drug Concentration (0.0-2.0)
   • Growth Factor (0.1-1.0)
   • Time Period (10-50)

2. View Results:

   • Left panel shows growth simulation
   • Right panel displays ML predictions
   • Export data using the download button

Implementation Details

Simulation Model

• Uses logistic growth equation for organoid population
• Incorporates drug concentration effects through inhibition factor
• Generates synthetic data with realistic variations

Machine Learning Pipeline

• Random Forest Regressor for prediction
• StandardScaler for feature normalization
• Train/Test split for model validation
• Real-time prediction updates

Visualization Features

• Interactive growth curves
• Comparison between normal and drug-affected growth
• Prediction accuracy visualization
• Real-time updates with parameter changes

Future Enhancements

• Integration with real experimental data
• Additional ML models for comparison
• 3D visualization of organoid structures
• Advanced parameter optimization

Technical Considerations

• Optimized for real-time interaction
• Scalable data processing pipeline
• Modular architecture for easy extension
• Comprehensive error handling

License

MIT License

Created as part of a scientific research simulation platform.