Metadata-Version: 2.1
Name: jaxkineticmodel
Version: 0.0.4
Summary: A tool for building, training, and simulating metabolic kinetic models in Jax/Diffrax
Author-email: Paul van Lent <p.h.vanlent@tudelft.nl>, Léon Planken <l.r.planken@tudelft.nl>, Thomas Abeel <t.abeel@tudelft.nl>
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: diffrax>=0.6.0
Requires-Dist: equinox>=0.11.8
Requires-Dist: functions==0.7.0
Requires-Dist: jax==0.4.35
Requires-Dist: jaxlib==0.4.35
Requires-Dist: matplotlib>=3.9.2
Requires-Dist: optax==0.2.3
Requires-Dist: pandas>=1.1.4
Requires-Dist: pytest>=8.3.4
Requires-Dist: scikit_learn==1.5.2
Requires-Dist: scipy>=1.11.1
Requires-Dist: sympy>=1.13.3
Requires-Dist: tomli>=2.1.0; python_version < "3.11"
Requires-Dist: python-libsbml==5.20.4
Requires-Dist: scikit-optimize>=0.10.2
Requires-Dist: markdown-include>=0.8.1
Requires-Dist: xgboost>=2.1.3
Provides-Extra: test
Requires-Dist: libroadrunner>=2.7.0; extra == "test"



# JaxKineticModel
Python package to simulate, build, and train kinetic models.

### Installation

```
git clone https://github.com/AbeelLab/jaxkineticmodel.git
python -m pip install .
```


### Documentation package 
Documentation on jaxkineticmodel can be found [here](https://abeellab.github.io/jaxkineticmodel/)

### Running experiments as reported in paper
- Datasets generated from SBML models can be found in [datasets](datasets/)
- Glucose pulse datasets can be found [here](datasets/VanHeerden_Glucose_Pulse/)
- Experiments as reported in [1] can be found in [scripts](scripts/). Results from these experiments can found in the 
[4tu-repository](https://data.4tu.nl/private_datasets/o-HY8kDJhoCXyNOijO9Eaylje7E2dU-ex-edboPBDZ8)

### Source code
All data, source code, scripts, and results can be found in the [4tu-repository](https://data.4tu.nl/private_datasets/o-HY8kDJhoCXyNOijO9Eaylje7E2dU-ex-edboPBDZ8)

### Reference
If you use this package in your work, please reference:

[1] Lent, P. V., Bunkova, O., Planken, L., Schmitz, J., & Abeel, T. (2024). 
Neural Ordinary Differential Equations Inspired Parameterization of Kinetic Models. bioRxiv, 2024-12.
