https://github.com/deepbiolab/neural-bio
A hybrid modeling framework combining neural networks with physics-based constraints for bioreactor process optimization and control.
https://github.com/deepbiolab/neural-bio
bioreactor biotechnology hybrid-modeling neural-networks physics-informed-neural-networks pinn process-optimization
Last synced: 3 months ago
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A hybrid modeling framework combining neural networks with physics-based constraints for bioreactor process optimization and control.
- Host: GitHub
- URL: https://github.com/deepbiolab/neural-bio
- Owner: deepbiolab
- License: mit
- Created: 2025-03-01T13:42:39.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-01T14:30:53.000Z (3 months ago)
- Last Synced: 2025-03-01T15:26:58.769Z (3 months ago)
- Topics: bioreactor, biotechnology, hybrid-modeling, neural-networks, physics-informed-neural-networks, pinn, process-optimization
- Language: Python
- Homepage:
- Size: 9.95 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neural-Bio: Hybrid Modeling Framework for Bioreactor Optimization
A hybrid modeling framework combining neural networks with physics-based constraints for bioreactor process optimization and control.
- Neural network for reaction rate prediction
- First principle model for bioreactor dynamics
- Physics-informed constraints integration
- Adaptive weighted loss function
## Overview
🧬 Neural-Bio is a PyTorch-based framework that implements a hybrid modeling approach for bioprocess optimization. It combines deep learning with mechanistic models to predict and optimize bioreactor processes, incorporating both data-driven insights and physical constraints.
## Installation
```bash
# Clone the repository
git clone https://github.com/deepbiolab/neural-bio.git
cd neural-bio
# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
## Usage
### Training
```bash
python main.py --mode train
```
- Place training data files in the `dataset/train` folder
- Ensure data follows the required format (see Data Format section)
- Adjust configuration settings in `src/config/default_config.py` as needed
- Training and validation performance imgs are saved in the `logs/` directory
### Testing
```bash
python main.py --mode test
```
- Place test data files in the `dataset/test` folder
- Ensure data follows the required format (see Data Format section)
- Verify model checkpoint exists in `checkpoints/` directory
- Test performance is saved in the `logs/` directory
### Prediction
```bash
python main.py --mode predict
```
- Place input data files in the `dataset/predict` folder
- Ensure data follows the required format (see Data Format section)
- Verify model checkpoint exists in `checkpoints/` directory
- Prediction results are saved in the `results/` directory
## Project Structure
```
neural-bio/
├── src/
│ ├── config/ # Configuration settings
│ ├── data/ # Data processing and dataset classes
│ ├── models/ # Neural network and hybrid model implementations
│ ├── trainer # Training utilities
│ ├── inference # inference utilities
│ └── utils/ # Visualization and evaluation tools
├── dataset/ # Data directory (not included)
├── checkpoints/ # Model checkpoints
├── results/ # Prediction results
├── logs/ # Training logs
└── main.py # Main script for training, prediction, and testing
```
## Configuration
The model behavior can be customized through the configuration file `src/config/default_config.py`. Key parameters include:
- Dataset parameters (time steps, variables, etc.)
- Model architecture (layers, dimensions)
- Training parameters (learning rate, batch size)
- Loss function weights
- Evaluation metrics
## Data Format
The framework expects two main data files in CSV format:
### OWU (Observation Wise Unit) Data
Time-series measurements of process variables for each experimental run.
```
run,time,VCD,Glc,Lac,Titer,Glc_feed
```
- `run`: Experiment run index
- `time`: Time point (days)
- State Variables:
- `VCD`: Viable Cell Density (10⁶ cells/mL)
- `Glc`: Glucose concentration (g/L)
- `Lac`: Lactate concentration (g/L)
- `Titer`: Product titer (g/L)
- Feeding Variables:
- `Glc_feed`: Glucose feeding rate (g/L/day)
### DOE (Design of Experiments) Data
Experimental design parameters and initial conditions for each run.
```
run,feed_start,feed_end,Glc_feed_rate,Glc_0,VCD_0
```
- `run`: Experiment run index
- Feeding Schedule:
- `feed_start`: Day to start feeding
- `feed_end`: Day to stop feeding
- `Glc_feed_rate`: Glucose feed concentration
- Initial Conditions:
- `Glc_0`: Initial glucose concentration
- `VCD_0`: Initial viable cell density
### Data Organization
```
dataset/
├── interpolation/
│ ├── train/
│ │ ├── owu.csv
│ │ └── owu_doe.csv
│ ├── test/
│ │ ├── owu.csv
│ │ └── owu_doe.csv
│ └── predict/
│ ├── owu.csv
│ └── owu_doe.csv
```
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Citation
If you use this code in your research, please cite:
```bibtex
@software{neural_bio2025,
author = {Tim-Lin},
title = {Neural-Bio: Hybrid Modeling Framework for Bioreactor Optimization},
year = {2025},
publisher = {GitHub},
url = {https://github.com/deepbiolab/neural-bio}
}
```
## Contact
For questions and feedback, please open an issue on GitHub.