Semi-quantitative Abstraction and Analysis of Chemical Reaction Networks

Milan Češka, Jan Křetínský

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publicationComputer Aided Verification - 31st International Conference, CAV 2019, Proceedings
EditorsIsil Dillig, Serdar Tasiran
PublisherSpringer Verlag
Pages475-496
Number of pages22
ISBN (Print)9783030255398
DOIs
StatePublished - 2019
Event31st International Conference on Computer Aided Verification, CAV 2019 - New York City, United States
Duration: 15 Jul 201918 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11561 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference31st International Conference on Computer Aided Verification, CAV 2019
Country/TerritoryUnited States
CityNew York City
Period15/07/1918/07/19

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