Publication detail

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

ČEŠKA, M. CHAU, C. KŘETÍNSKÝ, J.

Original Title

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

Type

conference paper

Language

English

Original 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.

Keywords

probabilistic verification, population Markov chains, abstraction

Authors

ČEŠKA, M.; CHAU, C.; KŘETÍNSKÝ, J.

Released

14. 7. 2020

Publisher

Springer Verlag

Location

Cham

ISBN

978-3-030-53287-1

Book

International Conference on Computer Aided Verification

Edition

Lecture Notes in Computer Science

Pages from

653

Pages to

666

Pages count

14

URL

https://link.springer.com/chapter/10.1007/978-3-030-53288-8_32

BibTex

@inproceedings{BUT168142,
  author="ČEŠKA, M. and CHAU, C. and KŘETÍNSKÝ, J.",
  title="SeQuaiA: A Scalable Tool for Semi-Quantitative Analysis of Chemical Reaction Networks",
  booktitle="International Conference on Computer Aided Verification",
  year="2020",
  series="Lecture Notes in Computer Science",
  volume="12224",
  pages="653--666",
  publisher="Springer Verlag",
  address="Cham",
  doi="10.1007/978-3-030-53288-8\{_}32",
  isbn="978-3-030-53287-1",
  url="https://link.springer.com/chapter/10.1007/978-3-030-53288-8_32"
}

Documents

Ceska2020_Chapter_SeQuaiAAScalableToolForSemi-Qu.pdf