Abstract
We introduce LNetReduce, a tool that simplifies linear dynamic networks. Dynamic networks are represented as digraphs labeled by integer timescale orders. Such models describe deterministic or stochastic monomolecular chemical reaction networks, but also random walks on weighted protein-protein interaction networks, spreading of infectious diseases and opinion in social networks, communication in computer networks. The reduced network is obtained by graph and label rewriting rules and reproduces the full network dynamics with good approximation at all timescales. The tool is implemented in Python with a graphical user interface. We discuss applications of LNetReduce to network design and to the study of the fundamental relation between timescales and topology in complex dynamic networks.
Availability: the code, documentation and application examples are available at https://github.com/oradules/LNetReduce.
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Acknowledgements
This work was supported by Agence Nationale de la Recherche, projects ANR-17-CE40-0036 SYMBIONT and ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), and by the Ministry of Science and Higher Education of the Russian Federation (project No. 14.Y26.31.0022).
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Buffard, M., Desoeuvres, A., Naldi, A., Requilé, C., Zinovyev, A., Radulescu, O. (2021). LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales. In: Cinquemani, E., Paulevé, L. (eds) Computational Methods in Systems Biology. CMSB 2021. Lecture Notes in Computer Science(), vol 12881. Springer, Cham. https://doi.org/10.1007/978-3-030-85633-5_15
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DOI: https://doi.org/10.1007/978-3-030-85633-5_15
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