Publication Details

Abstraction-based segmental simulation of reaction networks using adaptive memoization

HELFRICH Martin, ANDRIUSHCHENKO Roman, ČEŠKA Milan, KŘETÍNSKÝ Jan, MARTIČEK Štefan and ŠAFRÁNEK David. Abstraction-based segmental simulation of reaction networks using adaptive memoization. BMC Bioinformatics, vol. 25, no. 1, 2024, pp. 1-24. ISSN 1471-2105. Available from: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05966-5
Czech title
Simulace chemických reakčních sítí pomocí abstracke a segmentace
Type
journal article
Language
english
Authors
Helfrich Martin (TUM)
Andriushchenko Roman, Ing. (DITS FIT BUT)
Češka Milan, doc. RNDr., Ph.D. (DITS FIT BUT)
Křetínský Jan (TUM)
Martiček Štefan, Ing. (DITS FIT BUT)
Šafránek David, doc. Mgr., Ph.D. (FI MUNI)
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
Publisher
Springer Science+Business Media B.V.
DOI
10.1186/s12859-024-05966-5
UT WoS
001351556400001
EID Scopus
2-s2.0-85209476640
BibTeX 
@ARTICLE{FITPUB13314,
   author = "Martin Helfrich and Roman Andriushchenko and Milan \v{C}e\v{s}ka and Jan K\v{r}et\'{i}nsk\'{y} and \v{S}tefan Marti\v{c}ek and David \v{S}afr\'{a}nek",
   title = "Abstraction-based segmental simulation of reaction networks using adaptive memoization",
   pages = "1--24",
   journal = "BMC Bioinformatics",
   volume = 25,
   number = 1,
   year = 2024,
   ISSN = "1471-2105",
   doi = "10.1186/s12859-024-05966-5",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/13314"
}
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