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:
Metadata
Release files for mpbn 4.4
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| mpbn-4.4.tar.gz | 18.9 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mpbn-4.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 42.5 kB
Release files / mpbn-4.4.tar.gz
| Download URL | mpbn-4.4.tar.gz |
|---|---|
| Size | 18.9 kB |
| Tags | Source |
|
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No |
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twine/6.2.0 CPython/3.14.5
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Release files / mpbn-4.4-py3-none-any.whl
| Download URL | mpbn-4.4-py3-none-any.whl |
|---|---|
| Size | 23.6 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.14.5
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