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ČEŠKA, M. KŘETÍNSKÝ, J.
Original Title
Semi-Quantitative Abstraction and Analysis of Chemical Reaction Networks
Type
conference paper
Language
English
Original Abstract
Analysis of large continuous-time stochastic systems is a computationally intensive task. In this work we focus on population models arising from chemical reaction networks (CRNs), which play a fundamental role in analysis and design of biochemical systems. Many relevant CRNs are particularly challenging for existing techniques due to complex dynamics including stochasticity, stiffness or multimodal population distributions. We propose a novel approach allowing not only to predict, but also to explain both the transient and steady-state behaviour. It focuses on qualitative description of the behaviour and aims at quantitative precision only in orders of magnitude. First we build a compact understandable model, which we then crudely analyse. As demonstrated on complex CRNs from literature, our approach reproduces the known results, but in contrast to the state-of-the-art methods, it runs with virtually no computational cost and thus offers unprecedented scalability.
Keywords
chemical reaction networks, continuous-time Markov chains, population level abstraction, semiquantitative reasoning
Authors
ČEŠKA, M.; KŘETÍNSKÝ, J.
Released
17. 4. 2019
Publisher
Springer International Publishing
Location
New York
ISBN
978-3-030-25540-4
Book
Proceedings of the 31th International Conference on Computer Aided Verification (CAV'19)
Edition
Lecture Notes of Computer Science
Pages from
475
Pages to
496
Pages count
22
BibTex
@inproceedings{BUT159968, author="ČEŠKA, M. and KŘETÍNSKÝ, J.", title="Semi-Quantitative Abstraction and Analysis of Chemical Reaction Networks", booktitle="Proceedings of the 31th International Conference on Computer Aided Verification (CAV'19)", year="2019", series="Lecture Notes of Computer Science", volume="11561", pages="475--496", publisher="Springer International Publishing", address="New York", doi="10.1007/978-3-030-25540-4\{_}28", isbn="978-3-030-25540-4" }