Other Open Access
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:
- online, using myBinder service at https://mybinder.org/v2/zenodo/10.5281/zenodo.3936123/
- or on your computer, provided you have Docker and Python 3 installed:
- download the notebooks individually from below, or from https://github.com/pauleve/MPBNs-SI-Notebooks/archive/main.zip and extract the zip file
- execute the following commands, where
notebooksis the folder in which you extracted the notebooks:sudo pip install -U colomoto-docker # you may have to use pip3 instead of pip colomoto-docker -V 2020-07-01 --bind notebooks
Visualize online:
- MPBN applied to Bladder Tumorigenesis by Remy et al 2015.ipynb
- MPBN applied to T-Cell differentiation model by Abou-Jaoudé et al. 2015.ipynb
- MPBN applied to Tumour invasion model by Cohen et al. 2015.ipynb
- I3FFL - compatible MPBNs.ipynb
- Scalability on large random BNs.ipynb
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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views
downloads
| All versions | This version | |
|---|---|---|
| Views | 199 | 95 |
| Downloads | 155 | 71 |
| Data volume | 35.9 MB | 6.6 MB |
| Unique views | 163 | 82 |
| Unique downloads | 111 | 44 |
Indexed in
- Publication date:
- July 1, 2020
- DOI:
-
- Keyword(s):
- Jupyter notebooks Computational systems biology
- Related identifiers:
- Compiled by
- 10.5281/zenodo.3715516 (Software)
- Supplement to
- 10.1101/2020.03.22.998377 (Preprint)
- Supplementary material
- 10.5281/zenodo.3719059 (Dataset)
- 10.5281/zenodo.3719029 (Dataset)
- 10.5281/zenodo.3719018 (Dataset)
- 10.5281/zenodo.3715210 (Software)
- License (for files):
- Creative Commons Attribution 4.0 International