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.
mpbn is distributed in the CoLoMoTo docker.
pip install mpbn
conda install -c colomoto -c potassco mpbn
- Enumeration of fixed points and attractors:
mpbn -h
- Simulation:
mpbn-sim -h
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: