Metadata-Version: 2.4
Name: autoreduce
Version: 0.3.0
Summary: Python based automated model reduction tools for SBML models
Project-URL: Documentation, https://autoreduce.readthedocs.io
Project-URL: Source, https://github.com/ayush9pandey/AutoReduce
Project-URL: Tracker, https://github.com/ayush9pandey/AutoReduce/issues
Author-email: Ayush Pandey <ayushpandey@ucmerced.edu>
License: BSD 3-Clause License
        
        Copyright (c) 2025, Ayush Pandey
        All rights reserved.
        
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License-File: LICENSE
Keywords: Automated Model Reduction,Hill functions,Modeling,QSSA,SBML
Requires-Python: >=3.9
Requires-Dist: numpy
Requires-Dist: python-libsbml
Requires-Dist: scipy
Requires-Dist: sympy
Provides-Extra: all
Requires-Dist: matplotlib; extra == 'all'
Requires-Dist: seaborn; extra == 'all'
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Requires-Dist: pytest-cov; extra == 'test'
Requires-Dist: pytest-xdist; extra == 'test'
Description-Content-Type: text/markdown

# AutoReduce: An automated model reduction tool

Python toolbox to automatically obtain reduced model expressions using time-scale separation, conservation laws, and other assumptions.

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## Overview

AutoReduce is a Python package for automated model reduction of SBML models. It provides tools for:
- Automated model reduction using QSSA (Quasi-Steady State Approximation)
- Hill function approximation
- Integration with [BioCRNPyler](https://biocrnpyler.readthedocs.io/) for synthetic biology models
- Analysis of gene expression models

Refer to the [bioRxiv paper](https://www.biorxiv.org/content/10.1101/2020.02.15.950840v2.full.pdf) and [Journal of Robust and Nonlinear Control paper](https://onlinelibrary.wiley.com/doi/full/10.1002/rnc.6013) for more details.

## Quick Start

```python
from autoreduce.converters import load_sbml

# Load your SBML model
sys = load_sbml('your_sbml_file.xml', outputs=['your_output'])

# Solve conservation laws
conservation_laws = sys.solve_conservation_laws(
    conserved_sets=[
        ['species1', 'species2', 'species3'],  # First conserved set
        ['species4', 'species5']               # Second conserved set
    ],
    states_to_eliminate=['species_to_eliminate1', 'species_to_eliminate2']
)

# Solve timescale separation using QSSA
reduced_qssa_model = sys.solve_timescale_separation(
    ['fast_species1', 'fast_species2']
    )
```

For more examples, check out the [documentation](https://autoreduce.readthedocs.io/en/latest/examples.html).

## Installation

Install the latest version of AutoReduce:

```bash
pip install autoreduce
```

Install with all optional dependencies:

```bash
pip install autoreduce[all]
```

For development installation:

```bash
git clone https://github.com/ayush9pandey/autoreduce.git
cd autoreduce
pip install -e ".[all]"
```

## Documentation

Full documentation is available at [autoreduce.readthedocs.io](https://autoreduce.readthedocs.io/).

## Contributing

We welcome contributions! Please see our [contributing guide](https://autoreduce.readthedocs.io/en/latest/contributing.html) for details.

## Versions

AutoReduce versions:
- 0.3.0 (current release): Major updates including improved API and documentation
- 0.2.0 (alpha release): `pip install autoreduce==0.2.0`
- 0.1.0 (alpha release): `pip install autoreduce==0.1.0`

## Contact

For questions, feedback, or suggestions, please contact:
- Ayush Pandey (ayushpandey at ucmerced dot edu)
- [GitHub Issues](https://github.com/ayush9pandey/autoreduce/issues)

## License

Released under the BSD 3-Clause License (see `LICENSE`)

Copyright (c) 2025, Ayush Pandey. All rights reserved.
