Standard Calibration and Online Estimation of Cell Specific Growth and Protein Synthesis Rates in Chi.Bio Mini-Bioreactors

This project consists of a web-based application developed in Python to estimate in real time the growth rate (μ) and synthesis rate (Π) (e.g., GFP production) of Escherichia coli cultures.

The original manufacturer’s software was modified to implement a custom solution that integrates:

• Real-time data acquisition and processing.
• Estimation algorithms for growth and synthesis dynamics.
• User interfaces for OD and fluorescence calibration protocols.

Additionally, the system supports unit standardization in terms of Particles and Molecules of Equivalent Fluorescein (MEFL), facilitating reproducibility and data comparability.

This platform improves experimental control and enables automated decision-making in synthetic biology workflows.

The application is launched using a shell script (cb_new.sh) which runs a Gunicorn server with WebSocket support.

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How to Run the Application

From the root of the project, launch:

sh cb_new.sh

This script executes the following Gunicorn command:

gunicorn3 \
  -k geventwebsocket.gunicorn.workers.GeventWebSocketWorker \
  -w 1 \
  -b localhost:5001 \
  --timeout 80 \
  app_new:application

This will serve the app_new.py application using a WebSocket-enabled Gunicorn worker on the specified IP and port

Ensure that the port 5001 is open and accessible from your client machine or browser.

Accessing Calibration Screens

Once the script cb_new.sh has been launched and the Chi.Bio platform is initialized, you can access the calibration interfaces through the web UI:

• OD Calibration: Navigate to the "OD Regulation" section and click the "Calibration" button.

  

• Fluorescence Calibration: Go to the "Spectrophotometer" section and click the "Calibration FP" button.

  

These interfaces allow you to perform and verify the calibration procedures for optical density (OD) and fluorescence, respectively.

Main Features

• Real-time estimation of:

  • Growth rate (μ)
  • Synthesis rate (e.g., GFP production)

• WebSocket communication for real-time data streaming

• Live plotting of optical density and calculated rates

• Flexible for integration into control systems or bioprocess dashboards

Project Structure

    /Chi.Bio_bioreactor/
        ├── app_new.py              ← Main Python application (Gunicorn entry point)
        ├── cb_new.sh               ← Bash script to start the server
        ├── static/                 ← Static assets (JS/CSS)
        ├── templates/              ← HTML templates 
        ├── utils/                  ← Estimation functions and data processing logic
        └── README.md               ← This file
        

Requirements

Install the required Python packages:

pip install -r requirements.txt

Example dependencies:

• gevent
• gunicorn
• flask
• numpy, scipy
• gevent-websocket
• etc.

Accessing the Application

Once the server is running, open your browser and go to:

http://localhost:5001

You should see the application interface for live data visualization and analysis.

Notes

• w 1 is used to run a single Gunicorn worker to maintain WebSocket state.
• You can modify the IP or port in cb_new.sh if needed.
• Estimation algorithms are likely implemented in modules inside utils/.