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Organoid Drug Response Simulator

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

Screenshot 2025-01-13 at 16 33 58

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.