Skip to main content

MxlModels is a Python package of reference mechanistic models.

Project description

mxlmodels-logo

MxlModels

MxlModels is a Python package of reference mechanistic models. It contains the same models as in the MxlBricks repo, but written as single, flat files to make inspection easier.

Usually, these here will be created by codegen from MxlBricks.

Installation

You can install mxlpy using pip: pip install mxlmodels.

Done. Simple as that.

Models

Name Description
Yokota 1985 Photorespiration
Poolman 2000 CBB cycle, based on Pettersson & Ryde-Pettersson 1988
Ebenhöh 2011 PSII & two-state quencher & ATP synthase
Ebenhöh 2014 PETC & state transitions & ATP synthase from Ebenhoeh 2011
Matuszyńska 2016 NPQ 2011 + PSII & four-state quencher
Matuszyńska 2016 PhD ?
Matuszyńska 2019 Merges PETC (Ebenhöh 2014), NPQ (Matuszynska 2016) and CBB (Poolman 2000)
Saadat 2021 2019 + Mehler (Valero ?) & Thioredoxin & extendend PSI states & consumption
Ebeling 2026 unpublishd

Tool family 🏠

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

  • MxlPy is a Python package for mechanistic learning (Mxl)
  • MxlBricks is a Python package to build mechanistic models composed of pre-defined reactions (bricks)
  • MxlWeb brings simulation of mechanistic models to the browser!
  • pysbml simplifies SBML models for import/export with MxlPy
  • Parameteriser looks up kinetic parameters from BRENDA and other databases

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mxlmodels-1.0.0.tar.gz (627.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mxlmodels-1.0.0-py3-none-any.whl (53.7 kB view details)

Uploaded Python 3

File details

Details for the file mxlmodels-1.0.0.tar.gz.

File metadata

  • Download URL: mxlmodels-1.0.0.tar.gz
  • Upload date:
  • Size: 627.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for mxlmodels-1.0.0.tar.gz
Algorithm Hash digest
SHA256 04e7de6e4b550e8cc5e675af487c04160a6242be4f902bfd5499da67683f153c
MD5 4ebc5741bb73eacd5dfe790374a027de
BLAKE2b-256 75fd1158aff22d98ac8fc47b211d9f719f7163f7486b63810af538b4e2d22fb4

See more details on using hashes here.

Provenance

The following attestation bundles were made for mxlmodels-1.0.0.tar.gz:

Publisher: python-publish.yml on Computational-Biology-Aachen/mxl-models

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file mxlmodels-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: mxlmodels-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 53.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for mxlmodels-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 5605aa9702a14f42c7dcac444ceb0a4a674b5873a2dd1e56f8f4e4ea6b024692
MD5 dd8d75316cf89653034914f635358f39
BLAKE2b-256 13c6a7abde09f8b782539db7d9c6aadc9d910abc6eb5f6e3de06a458bab501ff

See more details on using hashes here.

Provenance

The following attestation bundles were made for mxlmodels-1.0.0-py3-none-any.whl:

Publisher: python-publish.yml on Computational-Biology-Aachen/mxl-models

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page