LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales

Marion Buffard; Aurélien Desoeuvres; Aurélien Naldi; Clément Requilé; Andrei Zinovyev; Ovidiu Radulescu

doi:10.1007/978-3-030-85633-5_15

LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales

Marion Buffard, Aurélien Desoeuvres, Aurélien Naldi, Clément Requilé, Andrei Zinovyev, Ovidiu Radulescu^*

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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.

Original language	English
Title of host publication	Computational Methods in Systems Biology - 19th International Conference, CMSB 2021, Proceedings
Editors	Eugenio Cinquemani, Loïc Paulevé
Number of pages	7
Publisher	Springer
Publication date	2021
Pages	238-244
ISBN (Print)	9783030856328
DOIs	https://doi.org/10.1007/978-3-030-85633-5_15
Publication status	Published - 2021
Externally published	Yes
Event	19th International Conference on Computational Methods in Systems Biology, CMSB 2021 - Virtual, Online Duration: 22 Sept 2021 → 24 Sept 2021

Conference

Conference	19th International Conference on Computational Methods in Systems Biology, CMSB 2021
City	Virtual, Online
Period	22/09/2021 → 24/09/2021

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12881 LNBI
ISSN	0302-9743

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Access to Document

10.1007/978-3-030-85633-5_15

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

Buffard, M., Desoeuvres, A., Naldi, A., Requilé, C., Zinovyev, A., & Radulescu, O. (2021). LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales. In E. Cinquemani, & L. Paulevé (Eds.), Computational Methods in Systems Biology - 19th International Conference, CMSB 2021, Proceedings (pp. 238-244). Springer. https://doi.org/10.1007/978-3-030-85633-5_15

Buffard, Marion ; Desoeuvres, Aurélien ; Naldi, Aurélien et al. / LNetReduce : Tool for Reducing Linear Dynamic Networks with Separated Timescales. Computational Methods in Systems Biology - 19th International Conference, CMSB 2021, Proceedings. editor / Eugenio Cinquemani ; Loïc Paulevé. Springer, 2021. pp. 238-244 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12881 LNBI).

@inproceedings{c0788496a71745baa59a71395e09a41d,

title = "LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales",

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.",

author = "Marion Buffard and Aur{\'e}lien Desoeuvres and Aur{\'e}lien Naldi and Cl{\'e}ment Requil{\'e} and Andrei Zinovyev and Ovidiu Radulescu",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 19th International Conference on Computational Methods in Systems Biology, CMSB 2021 ; Conference date: 22-09-2021 Through 24-09-2021",

year = "2021",

doi = "10.1007/978-3-030-85633-5_15",

language = "English",

isbn = "9783030856328",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "238--244",

editor = "Eugenio Cinquemani and Lo{\"i}c Paulev{\'e}",

booktitle = "Computational Methods in Systems Biology - 19th International Conference, CMSB 2021, Proceedings",

address = "Germany",

}

Buffard, M, Desoeuvres, A, Naldi, A, Requilé, C, Zinovyev, A & Radulescu, O 2021, LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales. in E Cinquemani & L Paulevé (eds), Computational Methods in Systems Biology - 19th International Conference, CMSB 2021, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12881 LNBI, pp. 238-244, 19th International Conference on Computational Methods in Systems Biology, CMSB 2021, Virtual, Online, 22/09/2021. https://doi.org/10.1007/978-3-030-85633-5_15

LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales. / Buffard, Marion; Desoeuvres, Aurélien; Naldi, Aurélien et al.
Computational Methods in Systems Biology - 19th International Conference, CMSB 2021, Proceedings. ed. / Eugenio Cinquemani; Loïc Paulevé. Springer, 2021. p. 238-244 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12881 LNBI).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - LNetReduce

T2 - 19th International Conference on Computational Methods in Systems Biology, CMSB 2021

AU - Buffard, Marion

AU - Desoeuvres, Aurélien

AU - Naldi, Aurélien

AU - Requilé, Clément

AU - Zinovyev, Andrei

AU - Radulescu, Ovidiu

PY - 2021

Y1 - 2021

N2 - 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.

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 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.

UR - http://www.scopus.com/inward/record.url?scp=85116062070&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-85633-5_15

DO - 10.1007/978-3-030-85633-5_15

M3 - Article in proceeding

AN - SCOPUS:85116062070

SN - 9783030856328

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 238

EP - 244

BT - Computational Methods in Systems Biology - 19th International Conference, CMSB 2021, Proceedings

A2 - Cinquemani, Eugenio

A2 - Paulevé, Loïc

PB - Springer

Y2 - 22 September 2021 through 24 September 2021

ER -

Buffard M, Desoeuvres A, Naldi A, Requilé C, Zinovyev A, Radulescu O. LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales. In Cinquemani E, Paulevé L, editors, Computational Methods in Systems Biology - 19th International Conference, CMSB 2021, Proceedings. Springer. 2021. p. 238-244. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12881 LNBI). doi: 10.1007/978-3-030-85633-5_15

LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales

Abstract

Conference

Bibliographical note

Access to Document

AUB Link

Other files and links

Fingerprint

Cite this