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Brief Python implementation of Most Permissive Boolean Networks

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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). The mpbn Python module also offers a Most Permissive simulator, which provides trajectory sampling and computes attractor propensities (see paper Variable-Depth Simulation of Most Permissive Boolean Networks for more details).

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: