mpbn 4.3.2

Simple implementation of Most Permissive Boolean networks

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 -c daemontus 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:

• https://nbviewer.org/github/bnediction/mpbn/tree/master/examples/
• http://doi.org/10.5281/zenodo.3719097

For the simulation:

• https://nbviewer.org/github/bnediction/mpbn/blob/master/examples/Simulation.ipynb