Simple implementation of Most Permissive Boolean networks

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for pauleve from gravatar.com pauleve

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers
Report project as malware

Project description

The mpbn Python module offers a simple implementation of reachability and attractor analysis (minimal trap spaces) in Most Permissive Boolean Networks (doi:10.1038/s41467-020-18112-5).

It is built on the minibn module from colomoto-jupyter which allows importation of Boolean networks in many formats. See http://colomoto.org/notebook.

Installation

CoLoMoTo Notebook environment

mpbn is distributed in the CoLoMoTo docker.

Using pip

pip install mpbn

Using conda

conda install -c colomoto -c potassco mpbn

Usage

Command line

  • Enumeration of fixed points and attractors:
mpbn -h
  • Simulation:
mpbn-sim -h

Python interface

Documentation is available at https://mpbn.readthedocs.io.

Example notebooks:

For the simulation:

Supported by

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