Adaptive Aggregation of Markov Chains: Quantitative Analysis of
Chemical Reaction Networks

D 2015

Adaptive Aggregation of Markov Chains: Quantitative Analysis of Chemical Reaction Networks

ABATE, Alessandro, Milan ČEŠKA, Luboš BRIM a Marta KWIATKOWSKA

Základní údaje

Originální název

Adaptive Aggregation of Markov Chains: Quantitative Analysis of Chemical Reaction Networks

Autoři

ABATE, Alessandro (826 Velká Británie a Severní Irsko), Milan ČEŠKA (203 Česká republika, domácí), Luboš BRIM (203 Česká republika, garant, domácí) a Marta KWIATKOWSKA (826 Velká Británie a Severní Irsko)

Vydání

LNCS 9206. Berlin, 27th International Conference, CAV 2015, San Francisco, CA, USA, July 18-24, 2015, Proceedings, od s. 195-213, 19 s. 2015

Nakladatel

Springer International Publishing

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/15:00081179

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-21689-8

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-21690-4_12

UT WoS

000364182900012

Klíčová slova anglicky

continuous-time Markov chains; parameter exploration; model checking

Štítky

core_A, firank_1

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 16. 4. 2019 09:39, prof. RNDr. Luboš Brim, CSc.

Anotace

V originále

Quantitative analysis of Markov models typically proceeds through numerical methods or simulation-based evaluation. Since the state space of the models can often be large, exact or approximate state aggregation methods (such as lumping or bisimulation reduction) have been proposed to improve the scalability of the numerical schemes. However, none of the existing numerical techniques provides general, explicit bounds on the approximation error, a problem particularly relevant when the level of accuracy affects the soundness of verification results. We propose a novel numerical approach that combines the strengths of aggregation techniques (state-space reduction) with those of simulation-based approaches (automatic updates that adapt to the process dynamics). The key advantage of our scheme is that it provides rigorous precision guarantees under different measures. The new approach, which can be used in conjunction with time uniformisation techniques, is evaluated on two models of chemical reaction networks, a signalling pathway and a prokaryotic gene expression network: it demonstrates marked improvement in accuracy without performance degradation, particularly when compared to known state-space truncation techniques.

Návaznosti

GA15-11089S, projekt VaV

Název: Získávání parametrů biologických modelů pomocí techniky ověřování modelů

Investor: Grantová agentura ČR, Získávání parametrů biologických modelů pomocí techniky ověřování modelů

Citovat

ABATE, Alessandro, Milan ČEŠKA, Luboš BRIM a Marta KWIATKOWSKA. Adaptive Aggregation of Markov Chains: Quantitative Analysis of Chemical Reaction Networks. In 27th International Conference, CAV 2015, San Francisco, CA, USA, July 18-24, 2015, Proceedings. LNCS 9206. Berlin: Springer International Publishing, 2015, s. 195-213. ISBN 978-3-319-21689-8. Dostupné z: https://dx.doi.org/10.1007/978-3-319-21690-4_12.

@inproceedings{1316356,
   author = {Abate, Alessandro and Češka, Milan and Brim, Luboš and Kwiatkowska, Marta},
   address = {Berlin},
   booktitle = {27th International Conference, CAV 2015, San Francisco, CA, USA, July 18-24, 2015, Proceedings},
   doi = {http://dx.doi.org/10.1007/978-3-319-21690-4_12},
   edition = {LNCS 9206},
   keywords = {continuous-time Markov chains; parameter exploration; model checking},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Berlin},
   isbn = {978-3-319-21689-8},
   pages = {195-213},
   publisher = {Springer International Publishing},
   title = {Adaptive Aggregation of Markov Chains: Quantitative Analysis of Chemical Reaction Networks},
   url = {http://link.springer.com/chapter/10.1007%2F978-3-319-21690-4_12#},
   year = {2015}
}

TY  - JOUR
ID  - 1316356
AU  - Abate, Alessandro - Češka, Milan - Brim, Luboš - Kwiatkowska, Marta
PY  - 2015
TI  - Adaptive Aggregation of Markov Chains: Quantitative Analysis of Chemical Reaction Networks
PB  - Springer International Publishing
CY  - Berlin
SN  - 9783319216898
KW  - continuous-time Markov chains
KW  - parameter exploration
KW  - model checking
UR  - http://link.springer.com/chapter/10.1007%2F978-3-319-21690-4_12#
L2  - http://link.springer.com/chapter/10.1007%2F978-3-319-21690-4_12#
N2  - Quantitative analysis of Markov models typically proceeds through numerical methods or simulation-based evaluation. Since the state space of the models can often be large, exact or approximate state aggregation methods (such as lumping or bisimulation reduction) have been proposed to improve the scalability of the numerical schemes. However, none of the existing numerical techniques provides general, explicit bounds on the approximation error, a problem particularly relevant when the level of accuracy affects the soundness of verification results. We propose a novel numerical approach that combines the strengths of aggregation techniques (state-space reduction) with those of simulation-based approaches (automatic updates that adapt to the process dynamics). The key advantage of our scheme is that it provides rigorous precision guarantees under different measures. The new approach, which can be used in conjunction with time uniformisation techniques, is evaluated on two models of chemical reaction networks, a signalling pathway and a prokaryotic gene expression network: it demonstrates marked improvement in accuracy without performance degradation, particularly when compared to known state-space truncation techniques.
ER  -

ABATE, Alessandro, Milan ČEŠKA, Luboš BRIM a Marta KWIATKOWSKA. Adaptive Aggregation of Markov Chains: Quantitative Analysis of Chemical Reaction Networks. In \textit{27th International Conference, CAV 2015, San Francisco, CA, USA, July 18-24, 2015, Proceedings}. LNCS 9206. Berlin: Springer International Publishing, 2015, s.~195-213. ISBN~978-3-319-21689-8. Dostupné z: https://dx.doi.org/10.1007/978-3-319-21690-4\_{}12.

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