StableMotifs-python

This module brings a Python interface to the tool StableMotifs (Jorge G. T. Zañudo and Réka Albert), for the control of Boolean networks.

The control predictions can be processed using the algorecell_types library, which eases the display and comparison with other control methods.

Installation

Using conda

conda install -c colomoto stablemotifs-python

Using pip

pip install stablemotifs
python -m stablemotifs_setup

Documentation

Documentation is available at https://stablemotifs-python.readthedocs.io.

Examples can be found at:

• https://nbviewer.jupyter.org/github/algorecell/StableMotifs-python/tree/master/examples/

Quick usage

>>> import stablemotifs

Model loading:

>>> sm = stablemotifs.load("network.txt") # in BooleanNet format
# alternatively, load with biolqm in any format
>>> import biolqm
>>> lm = biolqm.load("model.zginml") # or any format support by bioLQM
>>> sm = stablemotifs.load(lm)

Reprogramming predictions:

>>> sr = sm.reprogramming_to_attractor({"A": 1, "B": 0})
>>> sr.as_table()

See help(sr) for other display methods