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LNetReduce: tool for reducing linear dynamic networks with separated time scales

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 time scales. 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 time scales and topology in complex dynamic networks.
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Contributor : Andrei Zinovyev Connect in order to contact the contributor
Submitted on : Friday, May 14, 2021 - 11:32:12 PM
Last modification on : Monday, April 18, 2022 - 10:40:38 PM
Long-term archiving on: : Sunday, August 15, 2021 - 6:33:08 PM


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Marion Buffard, Aurélien Desoeuvres, Aurélien Naldi, Clément Requilé, Andrei Zinovyev, et al.. LNetReduce: tool for reducing linear dynamic networks with separated time scales. Computational Methods in Systems Biology, Sep 2021, Bordeaux, France. ⟨10.1007/978-3-030-85633-5_15⟩. ⟨hal-03226633⟩



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