PT  - JOURNAL ARTICLE
AU  - Marion Buffard
AU  - Aurélien Desoeuvres
AU  - Aurélien Naldi
AU  - Clément Requilé
AU  - Andrei Zinovyev
AU  - Ovidiu Radulescu
TI  - LNetReduce: tool for reducing linear dynamic networks with separated time scales
AID  - 10.1101/2021.05.11.443578
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.11.443578
4099  - http://biorxiv.org/content/early/2021/05/12/2021.05.11.443578.short
4100  - http://biorxiv.org/content/early/2021/05/12/2021.05.11.443578.full
AB  - 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.Availability the code and application examples are available at https://github.com/oradules/LNetReduce.Competing Interest StatementThe authors have declared no competing interest.