Publication Details

Abstraction-based segmental simulation of reaction networks using adaptive memoization

HELFRICH, M.; ANDRIUSHCHENKO, R.; ČEŠKA, M.; KŘETÍNSKÝ, J.; MARTIČEK, Š.; ŠAFRÁNEK, D. Abstraction-based segmental simulation of reaction networks using adaptive memoization. BMC BIOINFORMATICS, 2024, vol. 25, no. 1, p. 1-24. ISSN: 1471-2105.
Czech title
Simulace chemických reakčních sítí pomocí abstracke a segmentace
Type
journal article
Language
English
Authors
Helfrich Martin
Andriushchenko Roman, Ing. (DITS)
Češka Milan, doc. RNDr., Ph.D. (DITS)
KŘETÍNSKÝ, J.
Martiček Štefan, Ing.
Šafránek David, doc. RNDr., Ph.D.
URL
Keywords

Reaction networks, stochastic simulation, population abstraction, memoization

Abstract

Background Stochastic models are commonly employed in the system and synthetic
biology to study the effects of stochastic fluctuations emanating from reactions
involving species with low copy-numbers. Many important models feature complex
dynamics, involving a state-space explosion, stiffness, and multimodality, that
complicate the quantitative analysis needed to understand their stochastic
behavior. Direct numerical analysis of such models is typically not feasible and
generating many simulation runs that adequately approximate the model's dynamics
may take a prohibitively long time. Results We propose a new memoization
technique that leverages a population-based abstraction and combines previously
generated parts of simulations, called segments, to generate new simulations more
efficiently while preserving the original system's dynamics and its diversity.
Our algorithm adapts online to identify the most important abstract states and
thus utilizes the available memory efficiently. Conclusion We demonstrate that in
combination with a novel fully automatic and adaptive hybrid simulation scheme,
we can speed up the generation of trajectories significantly and correctly
predict the transient behavior of complex stochastic systems.

Published
2024
Pages
1–24
Journal
BMC BIOINFORMATICS, vol. 25, no. 1, ISSN 1471-2105
DOI
UT WoS
001351556400001
EID Scopus
BibTeX
@article{BUT193584,
  author="HELFRICH, M. and ANDRIUSHCHENKO, R. and ČEŠKA, M. and KŘETÍNSKÝ, J. and MARTIČEK, Š. and ŠAFRÁNEK, D.",
  title="Abstraction-based segmental simulation of reaction networks using adaptive memoization",
  journal="BMC BIOINFORMATICS",
  year="2024",
  volume="25",
  number="1",
  pages="1--24",
  doi="10.1186/s12859-024-05966-5",
  issn="1471-2105",
  url="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05966-5"
}
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