SeQuaiA: A Scalable Tool for Semi-Quantitative Analysis of Chemical Reaction Networks

Milan Češka; Calvin Chau; Jan Křetínský

doi:10.1007/978-3-030-53288-8_32

SeQuaiA: A Scalable Tool for Semi-Quantitative Analysis of Chemical Reaction Networks

Milan Češka, Calvin Chau, Jan Křetínský

Informatics 7 - Associate Professorship of Formal Methods for Software Reliability

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Chemical reaction networks (CRNs) play a fundamental role in analysis and design of biochemical systems. They induce continuous-time stochastic systems, whose analysis is a computationally intensive task. We present a tool that implements the recently proposed semi-quantitative analysis of CRN. Compared to the proposed theory, the tool implements the analysis so that it is more flexible and more precise. Further, its GUI offers a wide range of visualization procedures that facilitate the interpretation of the analysis results as well as guidance to refine the analysis. Finally, we define and implement a new notion of “mean” simulations, summarizing the typical behaviours of the system in a way directly comparable to standard simulations produced by other tools.

Original language	English
Title of host publication	Computer Aided Verification - 32nd International Conference, CAV 2020, Proceedings
Editors	Shuvendu K. Lahiri, Chao Wang
Publisher	Springer
Pages	653-666
Number of pages	14
ISBN (Print)	9783030532871
DOIs	https://doi.org/10.1007/978-3-030-53288-8_32
State	Published - 2020
Event	32nd International Conference on Computer Aided Verification, CAV 2020 - Los Angeles, United States Duration: 21 Jul 2020 → 24 Jul 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12224 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	32nd International Conference on Computer Aided Verification, CAV 2020
Country/Territory	United States
City	Los Angeles
Period	21/07/20 → 24/07/20

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10.1007/978-3-030-53288-8_32

Cite this

Češka, M., Chau, C., & Křetínský, J. (2020). SeQuaiA: A Scalable Tool for Semi-Quantitative Analysis of Chemical Reaction Networks. In S. K. Lahiri, & C. Wang (Eds.), Computer Aided Verification - 32nd International Conference, CAV 2020, Proceedings (pp. 653-666). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12224 LNCS). Springer. https://doi.org/10.1007/978-3-030-53288-8_32

Češka, Milan ; Chau, Calvin ; Křetínský, Jan. / SeQuaiA : A Scalable Tool for Semi-Quantitative Analysis of Chemical Reaction Networks. Computer Aided Verification - 32nd International Conference, CAV 2020, Proceedings. editor / Shuvendu K. Lahiri ; Chao Wang. Springer, 2020. pp. 653-666 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "SeQuaiA: A Scalable Tool for Semi-Quantitative Analysis of Chemical Reaction Networks",

abstract = "Chemical reaction networks (CRNs) play a fundamental role in analysis and design of biochemical systems. They induce continuous-time stochastic systems, whose analysis is a computationally intensive task. We present a tool that implements the recently proposed semi-quantitative analysis of CRN. Compared to the proposed theory, the tool implements the analysis so that it is more flexible and more precise. Further, its GUI offers a wide range of visualization procedures that facilitate the interpretation of the analysis results as well as guidance to refine the analysis. Finally, we define and implement a new notion of “mean” simulations, summarizing the typical behaviours of the system in a way directly comparable to standard simulations produced by other tools.",

author = "Milan {\v C}e{\v s}ka and Calvin Chau and Jan K{\v r}et{\'i}nsk{\'y}",

note = "Publisher Copyright: {\textcopyright} 2020, The Author(s).; 32nd International Conference on Computer Aided Verification, CAV 2020 ; Conference date: 21-07-2020 Through 24-07-2020",

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doi = "10.1007/978-3-030-53288-8_32",

language = "English",

isbn = "9783030532871",

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

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Češka, M, Chau, C & Křetínský, J 2020, SeQuaiA: A Scalable Tool for Semi-Quantitative Analysis of Chemical Reaction Networks. in SK Lahiri & C Wang (eds), Computer Aided Verification - 32nd International Conference, CAV 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12224 LNCS, Springer, pp. 653-666, 32nd International Conference on Computer Aided Verification, CAV 2020, Los Angeles, United States, 21/07/20. https://doi.org/10.1007/978-3-030-53288-8_32

SeQuaiA: A Scalable Tool for Semi-Quantitative Analysis of Chemical Reaction Networks. / Češka, Milan; Chau, Calvin; Křetínský, Jan.
Computer Aided Verification - 32nd International Conference, CAV 2020, Proceedings. ed. / Shuvendu K. Lahiri; Chao Wang. Springer, 2020. p. 653-666 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12224 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - SeQuaiA

T2 - 32nd International Conference on Computer Aided Verification, CAV 2020

AU - Češka, Milan

AU - Chau, Calvin

AU - Křetínský, Jan

PY - 2020

Y1 - 2020

N2 - Chemical reaction networks (CRNs) play a fundamental role in analysis and design of biochemical systems. They induce continuous-time stochastic systems, whose analysis is a computationally intensive task. We present a tool that implements the recently proposed semi-quantitative analysis of CRN. Compared to the proposed theory, the tool implements the analysis so that it is more flexible and more precise. Further, its GUI offers a wide range of visualization procedures that facilitate the interpretation of the analysis results as well as guidance to refine the analysis. Finally, we define and implement a new notion of “mean” simulations, summarizing the typical behaviours of the system in a way directly comparable to standard simulations produced by other tools.

AB - Chemical reaction networks (CRNs) play a fundamental role in analysis and design of biochemical systems. They induce continuous-time stochastic systems, whose analysis is a computationally intensive task. We present a tool that implements the recently proposed semi-quantitative analysis of CRN. Compared to the proposed theory, the tool implements the analysis so that it is more flexible and more precise. Further, its GUI offers a wide range of visualization procedures that facilitate the interpretation of the analysis results as well as guidance to refine the analysis. Finally, we define and implement a new notion of “mean” simulations, summarizing the typical behaviours of the system in a way directly comparable to standard simulations produced by other tools.

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U2 - 10.1007/978-3-030-53288-8_32

DO - 10.1007/978-3-030-53288-8_32

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SN - 9783030532871

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

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BT - Computer Aided Verification - 32nd International Conference, CAV 2020, Proceedings

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Češka M, Chau C, Křetínský J. SeQuaiA: A Scalable Tool for Semi-Quantitative Analysis of Chemical Reaction Networks. In Lahiri SK, Wang C, editors, Computer Aided Verification - 32nd International Conference, CAV 2020, Proceedings. Springer. 2020. p. 653-666. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-53288-8_32

SeQuaiA: A Scalable Tool for Semi-Quantitative Analysis of Chemical Reaction Networks

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