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12 results for “Boolean networks”
Data and code related to "Difficult control is related to instability in biologically inspired Boolean networks"
<p>This repository contains data and code related to the publication "Difficult control is related to instability in biologically inspired Boolean networks" by Bryan C. Daniels and Enrico Borriello.</p> <p>The python code in the `isolated_fixed_points_code` directory can be used to recreate all results in the paper. See the README.md file in the `isolated_fixed_points_code` directory for more information about how to run the code.</p> <p>The files `240916_cell_collective_ck_and_isolated_fp_data.csv`, `240916_iowa_database_ck_and_isolated_fp_data.csv`, and `240916_random_ck_and_isolated_fp_data.csv` contain data about the networks analyzed in the paper, including the number of attractors and mean control kernel size of each network.</p>
Supplementary material for "Learning Boolean controls in regulated metabolic networks: a case-study"
<p>This record contains notebooks and Docker image for reproducing the learning of Boolean controls in regulated metabolic networks, and the case study presented in the CMSB 2021 conference proceeding article "Learning Boolean controls in regulated metabolic networks: a case-study".</p> <p>Visualize notebooks online:</p> <ul> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-Simulations.ipynb">CaseStudy-Simulations.ipynb</a></li> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-SearchSpace.ipynb">CaseStudy-SearchSpace.ipynb</a></li> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-Inference.ipynb">CaseStudy-Inference.ipynb</a></li> </ul> <p>Notebooks can be executed interactively within the Docker image <code>bioasp/boolean-caspo-flux:cmsb2021 </code>which extends the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> version <code>2021-02-01.</code></p> <p>Alternatively, they can be executed without any installation at <a href="https://mybinder.org/v2/zenodo/10.5281/zenodo.5070151/">https://mybinder.org/v2/zenodo/10.5281/zenodo.5070151/</a>.</p> <p>The Docker image can be executed as follows:</p> <pre><code class="language-bash">docker pull bioasp/boolean-caspo-flux:cmsb2021 docker run -it --rm -p 8888:8888 bioasp/boolean-caspo-flux:cmsb2021 </code></pre> <p>then point your browser to <a href="http://127.0.0.1:8888">http://127.0.0.1:8888</a>.</p> <p>The image can be imported using the command <code>docker load</code> with the image file provided in this record:</p> <pre><code>docker load -i image.tar.gz</code></pre> <p>or with the <code>donodo</code> command available at <a href="https://github.com/pauleve/donodo">https://github.com/pauleve/donodo</a>:</p> <pre><code>pip install -U donodo donodo pull 10.5281/zenodo.5070151</code></pre>
VLBNs - Very Large Boolean Networks
<p>This archive contains instances of Boolean networks (in BoolNet format) generated from scale-free random network structure, on which has been applied the inhibitor-dominant rule to devise the logical functions.</p> <p>The generated networks range from 1,000 to 100,000 nodes, with in-degrees ranging up to 1400.</p> <p>Contents:</p> <ul> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3714876/files/Analysis.ipynb/%3Fdownload%3D1">Analysis.ipynb</a>: in/out-degree scatter plots of the generated networks</li> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3714876/files/Generation.ipynb/%3Fdownload%3D1">Generation.ipynb</a>: notebook used to generate the networks (requirs colomoto_jupyter python module)</li> <li>random-{N}-{id}.bnet: generated Boolean networks with N components</li> </ul>
Supplementary material for "Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis"
<p>This archive contains supplementary material for the paper <a href="https://doi.org/10.48550/arXiv.2207.13307">Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis</a> by Loïc Paulevé (2023):</p> <ul> <li>sources of the executable paper in Markdown and Jupyter notebook formats</li> <li>benchmark dataset</li> <li>scripts for converting the executable paper in LaTeX for arXiv deposit</li> </ul>
Supplementary code and data for "Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks"
<p>Supplementary code and data for the conference article</p> <blockquote> <p>"Tackling universal properties of minimal trap spaces of boolean networks" Sara Riva, Jean-Marie Lagniez, Gustavo Magaña López, and Loïc Paulevé Proceedings of CMSB 2023, LCNS, Springer.<a href="https://doi.org/10.1007/978-3-031-42697-1_11"> https://doi.org/10.1007/978-3-031-42697-1_11</a></p> </blockquote> <p>Requirements</p> <ul> <li> <p>Python at least 3.9</p> </li> <li> <p><a href="https://github.com/bnediction/bonesis">bonesis</a> (v0.5.6) - <a href="https://doi.org/10.5281/zenodo.7984628">https://doi.org/10.5281/zenodo.7984628</a></p> <p>With pip:</p> <ul> <li><code>pip install bonesis==0.5.6</code></li> </ul> <p>With conda:</p> <ul> <li><code>conda install -c potassco -c colomoto bonesis=0.5.6</code></li> </ul> </li> <li> <p>for the synthesis: <a href="https://github.com/sybila/biodivine-aeon-py/">AEON.py</a></p> <p>With pip:</p> <ul> <li><a href="https://rustup.rs">https://rustup.rs</a></li> <li><code>pip install biodivine-aeon</code></li> </ul> <p>With conda</p> <ul> <li><code>conda install -c daemontus biodivine_aeon</code></li> </ul> </li> </ul> <p> </p> <p>Usage</p> <ul> <li> to execute the CEGAR approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_cegar.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>The first parameter is the .bnet file, the second one is the marker (in JSON), and the third is the maximum number k of components in a perturbation. To fix the marker, it is also possible to use a special node (ex: p) in the bnet file and require a marker '{"p":1}'. In the Moon22 dataset, information about uncontrollable components are available, one can use <code>--exclude '["Apoptosis","Growth_Arrest","Proliferation"]'</code>. If the marker is given as a component, it must be involed in the uncontrollable list.</p> <p>Moreover, the markers of the Moon22 dataset are contained in the bnet files in a special component <code>p</code>. Concerning the Trappist dataset, the possible markers are contained in files .markers, the user can choose between them.</p> <p>The dataset synthetic_bns contains random generated BNs. Also in this case, different possible markers are contained in files .markers.</p> <ul> <li>to execute the Complementary approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_complementary.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>Again, uncontrollable components can be specified.</p> <ul> <li>to execute the Enumeration & Filtering approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_trapspaces_naive.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>Again, uncontrollable components can be specified.</p> <ul> <li>to execute the CEGAR approach for a synthesis problem:</li> </ul> <pre><code>python synthesis.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.aeon '{"p":1}' </code></pre> <p>The first parameter is the prior knowledge graph in an .aeon file and the second one is the marker (in JSON). Another option is <code>--maxclause</code> which allow one to specify the maximum integer number of clauses authorised in local functions (by default is 128). The option <code>--no-canonic</code> leads to specify the criteria for the refinement of the under-approximation (3 for TS(y)|=M ; 1 for \exists y s.t. TS(y)!=TS(x) or 0 to obtain just a different candidate solution).</p>
Supporting data for "Synthesis and Simulation of Ensembles of Boolean Networks for Cell Fate Decision" by Chevalier et al., 2020
<p>Code, data, and notebooks used for the synthesis and simulations of ensembles of Boolean networks for the tumor invasion model introduced in <a href="https://doi.org/10.1371/journal.pcbi.1004571">(Cohen et al, 2015)</a></p> <p>Visualize online:</p> <ul> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3938904/files/Simulations%20-%20Mutant%20analysis.ipynb">Simulations - Mutant analysis.ipynb </a></li> <li> <a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3938904/files/Tumour%20-%20Synthesis%20with%20BoNesis.ipynb">Tumour - Synthesis with BoNesis.ipynb</a></li> </ul> <p>The notebooks can be executed within the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> image 2020-07-01:</p> <pre><code>pip install -U colomoto-docker colomoto-docker -V 2020-07-01 --bind . </code></pre> <p>The synthesis additionally requires executing the following command (within the Docker image):</p> <pre><code>pip install --user bonesis-preview-20200514.zip </code></pre> <p>The ensembles have been generated with the following commands.</p> <pre><code>python synthesis.py synthesis --exact-pkn --globalfps python synthesis.py synthesis --exact-pkn --globalfps --mutant p53 --mutant NICD </code></pre> <p> </p> <ul> </ul>
Fly Boolean Implication Network
GEO Series GSE119084. Drosophila melanogaster. 0 samples. Type: Expression profiling by array; Third-party reanalysis.
Plant Boolean Implication Network
GEO Series GSE119083. Arabidopsis thaliana. 0 samples. Type: Expression profiling by array; Third-party reanalysis.
Human Boolean Implication Network
GEO Series GSE119087. Homo sapiens. 0 samples. Type: Expression profiling by array; Third-party reanalysis.
Mouse Boolean Implication Network
GEO Series GSE119085. Mus musculus. 0 samples. Type: Expression profiling by array; Third-party reanalysis.
Adapted Boolean Network Models for Extracellular Matrix Formation
GEO Series GSE13837. Homo sapiens. 60 samples. Type: Expression profiling by array.
Identification of a novel gene signature for neuroblastoma differentiation using a Boolean implication network
GEO Series GSE226935. Homo sapiens. 56 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.