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Notebooks demonstrating Most Permissive Boolean Networks

Paulevé, Loïc


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    <dct:description>&lt;p&gt;These notebooks demonstrate Most Permissive Boolean Networks (&lt;a href="http://dx.doi.org/10.1101/2020.03.22.998377"&gt;doi:10.1101/2020.03.22.998377&lt;/a&gt;) on several case studies of biological networks.&lt;/p&gt; &lt;p&gt;They can be executed interactively within the &lt;a href="http://colomoto.org/notebook"&gt;CoLoMoTo Docker&lt;/a&gt; version &lt;code&gt;2020-07-01&lt;/code&gt;:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;online, using myBinder service at &lt;a href="https://mybinder.org/v2/zenodo/10.5281/zenodo.3936123/"&gt;https://mybinder.org/v2/zenodo/10.5281/zenodo.3936123/&lt;/a&gt;&lt;/li&gt; &lt;li&gt;or on your computer, provided you have &lt;a href="https://docs.docker.com/get-docker/"&gt;Docker&lt;/a&gt; and Python 3 installed: &lt;ol&gt; &lt;li&gt;download the notebooks individually from below, or from &lt;a href="https://github.com/pauleve/MPBNs-SI-Notebooks/archive/main.zip"&gt;https://github.com/pauleve/MPBNs-SI-Notebooks/archive/main.zip&lt;/a&gt; and extract the zip file&lt;/li&gt; &lt;li&gt;execute the following commands, where &lt;code&gt;notebooks&lt;/code&gt; is the folder in which you extracted the notebooks: &lt;pre&gt;&lt;code class="language-bash"&gt;sudo pip install -U colomoto-docker # you may have to use pip3 instead of pip colomoto-docker -V 2020-07-01 --bind notebooks&lt;/code&gt;&lt;/pre&gt; &lt;/li&gt; &lt;/ol&gt; &lt;/li&gt; &lt;/ul&gt; &lt;p&gt;Visualize online:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;&lt;a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3936123/files/MPBN%20applied%20to%20Bladder%20Tumorigenesis%20by%20Remy%20et%20al%202015.ipynb"&gt;MPBN applied to Bladder Tumorigenesis by Remy et al 2015.ipynb&lt;/a&gt;&lt;/li&gt; &lt;li&gt;&lt;a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3936123/files/MPBN%20applied%20to%20T-Cell%20differentiation%20model%20by%20Abou-Jaoud%C3%A9%20et%20al.%202015.ipynb"&gt;MPBN applied to T-Cell differentiation model by Abou-Jaoud&amp;eacute; et al. 2015.ipynb&lt;/a&gt;&lt;/li&gt; &lt;li&gt;&lt;a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3936123/files/MPBN%20applied%20to%20Tumour%20invasion%20model%20by%20Cohen%20et%20al.%202015.ipynb"&gt;MPBN applied to Tumour invasion model by Cohen et al. 2015.ipynb&lt;/a&gt;&lt;/li&gt; &lt;li&gt;&lt;a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3936123/files/I3FFL%20-%20compatible%20MPBNs.ipynb"&gt;I3FFL - compatible MPBNs.ipynb&lt;/a&gt;&lt;/li&gt; &lt;li&gt;&lt;a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3936123/files/Scalability%20on%20large%20random%20BNs.ipynb"&gt;Scalability on large random BNs.ipynb&lt;/a&gt;&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;The notebooks rely on the Python library &lt;a href="https://github.com/pauleve/mpbn"&gt;mpbn&lt;/a&gt;, see the documentation for usage and examples at &lt;a href="https://mpbn.readthedocs.io/"&gt;https://mpbn.readthedocs.io&lt;/a&gt;&lt;/p&gt;</dct:description>
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