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LNetReduce: tool for reducing linear dynamicnetworks with separated time scales

Buffard, M.; Desoeuvres, A.; Naldi, A.; Requilé, C.; Zinovyev, A.; Radulescu, O.

2021-05-12 systems biology
10.1101/2021.05.11.443578 bioRxiv
Show 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. Availabilitythe code and application examples are available at https://github.com/oradules/LNetReduce.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.