Biochemical networks with simulation-based estimations of dynamical properties

<p>This datasets collection was first introduced in the article:&nbsp;</p> <p><a href="https://academic.oup.com/bioinformatics/article/39/11/btad678/7407341" target="_blank" rel="noopener">Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs</a>. (Bioinformatics 2023)</p> <p>&nbsp;</p> <p>The collection contains three datasets that contain information about three dynamical properties computed on a set of 483 biochemical pathways downloaded from the BioModels database. The three dynamical properties are:</p> <ul> <li>robustness</li> <li>sensitivity</li> <li>monotonicity</li> </ul> <p>The files are organized as follows:</p> <ol> <li>The `pathways` directory contains 483 files in .dot format for each biochemical pathway&nbsp;downloaded from the BioModels database (May 2021), represented in Petri net&nbsp;format (see <a href="https://doi.org/10.5220/0008964700320043">this article</a> for the exact definition). The file name is the ID of the pathway in the BioModels database.</li> <li>The other folders contain one .csv file for each property. A single .csv file contains 4 columns: <ol> <li>`PathwayID`: the ID of the Pathway in the BioModels database</li> <li>`Input`: the input molecular species on which the property has been assessed</li> <li>`Output`: the output molecular species on which the property has been assessed</li> <li>`Property`: the value of the property assessed with numerical simulations on the pathway for that particular input/output species pair.</li> </ol> </li> <li>The `loader.py` file is an optional script that allows to use the data&nbsp;in python. The script&nbsp;requires that the&nbsp;libraries `networkx`,&nbsp;`pandas`, and `pydot` are installed in the target machine.</li> </ol>