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Notebooks demonstrating Most Permissive Boolean Networks

Paulevé, Loïc

These notebooks demonstrate Most Permissive Boolean Networks (doi:10.1101/2020.03.22.998377) on several case studies of biological networks.

They can be executed interactively within the CoLoMoTo Docker version 2020-07-01:

Visualize online:

The notebooks rely on the Python library mpbn, see the documentation for usage and examples at https://mpbn.readthedocs.io

Files (471.7 kB)
Name Size
bonesis-preview-20200701.zip
md5:958161ca649ac91aad84b4dc89535dc7
32.2 kB Download
Dockerfile
md5:e8b15575e2de6864d270debc5a408bf5
198 Bytes Download
I3FFL - compatible MPBNs.ipynb
md5:adfa465fb4fec42ff75070e80fbdb5ad
10.4 kB Download
MPBN applied to Bladder Tumorigenesis by Remy et al 2015.ipynb
md5:e3d7e324f7cf193bf79402c7c70716a4
116.2 kB Download
MPBN applied to T-Cell differentiation model by Abou-Jaoudé et al. 2015.ipynb
md5:24f93796a30972d8711200cdaa0cd3bf
191.6 kB Download
MPBN applied to Tumour invasion model by Cohen et al. 2015.ipynb
md5:2d4c1f697ff8ee844b4acfdb52ffc66c
70.9 kB Download
Scalability on large random BNs.ipynb
md5:64b4aaa5b22b12933b30ca5e43f0e248
50.3 kB Download
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