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mxlpy

https://github.com/computational-biology-aachen/mxlpy

Science Score: 57.0%

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    Low similarity (17.3%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
Statistics
  • Stars: 5
  • Watchers: 3
  • Forks: 4
  • Open Issues: 3
  • Releases: 24
Created about 1 year ago · Last pushed 6 months ago
Metadata Files
Readme Changelog Contributing License Citation

README.md

mxlpy-logo

MxlPy

pypi docs License Coverage Ruff security: bandit PyPI Downloads

MxlPy (pronounced "em axe el pie") is a Python package for mechanistic learning (Mxl) - the combination of mechanistic modeling and machine learning to deliver explainable, data-informed solutions.

Installation

You can install mxlpy using pip: pip install mxlpy.

Due to their sizes, the machine learning packages are optional dependencies. You can install them using

```shell

One of them respectively

pip install mxlpy[torch] pip install mxlpy[tensorflow] pip install mxlpy[keras]

together

pip install mxlpy[torch, tensorflow, keras] ```

If you want access to the sundials solver suite via the assimulo package, we recommend setting up a virtual environment via pixi or mamba / conda using the conda-forge channel.

bash pixi init pixi add python assimulo pixi add --pypi mxlpy

How to cite

If you use this software in your scientific work, please cite this article:

Development setup

You have two choices here, using uv (pypi-only) or using pixi (conda-forge, including assimulo)

uv

  • Install uv as described in the docs.
  • Run uv sync --all-extras --all-groups to install dependencies locally

pixi

  • Install pixi as described in the docs
  • Run pixi install --frozen

LLMs

We support the llms.txt convention for making documentation available to large language models and the applications that make use of them. It is located at docs/llms.txt

Tool family

MxlPy is part of a larger family of tools that are designed with a similar set of abstractions. Check them out!

  • MxlBricks is built on top of MxlPy to build mechanistic models composed of pre-defined reactions (bricks)
  • MxlWeb brings simulation of mechanistic models to the browser!

Owner

  • Name: Computational-Biology-Aachen
  • Login: Computational-Biology-Aachen
  • Kind: organization

Citation (citation.bibtex) 

@article {van Aalst2025.05.06.652335,
	author = {van Aalst, Marvin and Nies, Tim and Pfennig, Tobias and Matuszy{\'n}ska, Anna Barbara},
	title = {MxlPy - Python Package for Mechanistic Learning in Life Science},
	elocation-id = {2025.05.06.652335},
	year = {2025},
	doi = {10.1101/2025.05.06.652335},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Summary: Recent advances in artificial intelligence have accelerated the adoption of ML in biology, enabling powerful predictive models across diverse applications. However, in scientific research, the need for interpretability and mechanistic insight remains crucial. To address this, we introduce MxlPy, a Python package that combines mechanistic modelling with ML to deliver explainable, data-informed solutions. MxlPy facilitates mechanistic learning, an emerging approach that integrates the transparency of mathematical models with the flexibility of data-driven methods. By streamlining tasks such as data integration, model formulation, output analysis, and surrogate modelling, MxlPy enhances the modelling experience without sacrificing interpretability. Designed for both computational biologists and interdisciplinary researchers, it supports the development of accurate, efficient, and explainable models, making it a valuable tool for advancing bioinformatics, systems biology, and biomedical research. Availability: MxlPy source code is freely available at https://github.com/Computational- Biology-Aachen/MxlPy. The full documentation with features and examples can be found here https://computational-biology-aachen.github.io/MxlPyCompeting Interest StatementThe authors have declared no competing interest.Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64, 507704013, 458090666, 390686111},
	URL = {https://www.biorxiv.org/content/early/2025/05/10/2025.05.06.652335},
	eprint = {https://www.biorxiv.org/content/early/2025/05/10/2025.05.06.652335.full.pdf},
	journal = {bioRxiv}
}

GitHub Events

Total
  • Create event: 26
  • Issues event: 39
  • Release event: 16
  • Watch event: 4
  • Delete event: 10
  • Issue comment event: 61
  • Push event: 121
  • Pull request review event: 2
  • Pull request event: 16
  • Fork event: 2
Last Year
  • Create event: 26
  • Issues event: 39
  • Release event: 16
  • Watch event: 4
  • Delete event: 10
  • Issue comment event: 61
  • Push event: 121
  • Pull request review event: 2
  • Pull request event: 16
  • Fork event: 2

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 29
  • Total pull requests: 24
  • Average time to close issues: 12 days
  • Average time to close pull requests: 7 days
  • Total issue authors: 4
  • Total pull request authors: 2
  • Average comments per issue: 2.62
  • Average comments per pull request: 0.0
  • Merged pull requests: 22
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 29
  • Pull requests: 24
  • Average time to close issues: 12 days
  • Average time to close pull requests: 7 days
  • Issue authors: 4
  • Pull request authors: 2
  • Average comments per issue: 2.62
  • Average comments per pull request: 0.0
  • Merged pull requests: 22
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • marvinvanaalst (17)
  • tnies (6)
  • PhotosyntheticBatman (5)
  • AnnaMatuszynska (1)
Pull Request Authors
  • marvinvanaalst (19)
  • tnies (5)
Top Labels
Issue Labels
enhancement (21) bug (5) help wanted (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 214 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 18
  • Total maintainers: 2
pypi.org: mxlpy

A package to build metabolic models

  • Versions: 18
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 214 Last month
Rankings
Dependent packages count: 9.2%
Average: 30.6%
Dependent repos count: 51.9%
Maintainers (2)
marvin.vanaalst cpbl-aachen
Last synced: 6 months ago

Dependencies

.github/workflows/python-publish.yml actions
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pyproject.toml pypi
  • dill >=0.3.9
  • matplotlib >=3.9.2
  • more-itertools >=10.5.0
  • numpy >=2.1.2
  • pandas >=2.2.3
  • pebble >=5.0.7
  • python-libsbml >=5.20.4
  • scipy >=1.14.1
  • seaborn >=0.13.2
  • sympy >=1.13.1
  • tabulate >=0.9.0
  • torch >=2.4.1
  • tqdm >=4.66.6
  • typing-extensions >=4.12.2
uv.lock pypi
  • 193 dependencies
.github/workflows/docs.yml actions
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.github/workflows/tests.yml actions
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