Abstraction-based segmental simulation of reaction networks
using adaptive memoization

J 2024

Abstraction-based segmental simulation of reaction networks using adaptive memoization

HELFRICH, Martin; Roman ANDRIUSHCHENKO; Milan ČEŠKA; Jan KŘETÍNSKÝ; Štefan MARTIČEK et al.

Základní údaje

Originální název

Abstraction-based segmental simulation of reaction networks using adaptive memoization

Autoři

HELFRICH, Martin; Roman ANDRIUSHCHENKO; Milan ČEŠKA; Jan KŘETÍNSKÝ; Štefan MARTIČEK a David ŠAFRÁNEK

Vydání

BMC Bioinformatics, 2024, 1471-2105

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.300

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/24:00138792

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

Reaction networks; Stochastic simulation; Population abstraction; Memoization

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 31. 3. 2025 17:44, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

MUNI/I/1757/2021, interní kód MU

Název: MUNI Award in Science and Humanities (Akronym: Křetínský)

Investor: Masarykova univerzita, MUNI Award in Science and Humanities, MASH - MUNI Award in Science and Humanities

Citovat

HELFRICH, Martin; Roman ANDRIUSHCHENKO; Milan ČEŠKA; Jan KŘETÍNSKÝ; Štefan MARTIČEK a David ŠAFRÁNEK. Abstraction-based segmental simulation of reaction networks using adaptive memoization. BMC Bioinformatics. 2024, roč. 25, č. 350, s. 1-24. ISSN 1471-2105. Dostupné z: https://doi.org/10.1186/s12859-024-05966-5.

@article{2475007,
   author = {Helfrich, Martin and Andriushchenko, Roman and Češka, Milan and Křetínský, Jan and Martiček, Štefan and Šafránek, David},
   article_number = {350},
   doi = {https://doi.org/10.1186/s12859-024-05966-5},
   keywords = {Reaction networks; Stochastic simulation; Population abstraction; Memoization},
   language = {eng},
   issn = {1471-2105},
   journal = {BMC Bioinformatics},
   title = {Abstraction-based segmental simulation of reaction networks using adaptive memoization},
   url = {https://link.springer.com/article/10.1186/s12859-024-05966-5},
   volume = {25},
   year = {2024}
}

TY  - JOUR
ID  - 2475007
AU  - Helfrich, Martin - Andriushchenko, Roman - Češka, Milan - Křetínský, Jan - Martiček, Štefan - Šafránek, David
PY  - 2024
TI  - Abstraction-based segmental simulation of reaction networks using adaptive memoization
JF  - BMC Bioinformatics
VL  - 25
IS  - 350
SP  - 1-24
EP  - 1-24
SN  - 14712105
KW  - Reaction networks
KW  - Stochastic simulation
KW  - Population abstraction
KW  - Memoization
UR  - https://link.springer.com/article/10.1186/s12859-024-05966-5
N2  - 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.
ER  -

HELFRICH, Martin; Roman ANDRIUSHCHENKO; Milan ČEŠKA; Jan KŘETÍNSKÝ; Štefan MARTIČEK a David ŠAFRÁNEK. Abstraction-based segmental simulation of reaction networks using adaptive memoization. \textit{BMC Bioinformatics}. 2024, roč.~25, č.~350, s.~1-24. ISSN~1471-2105. Dostupné z: https://doi.org/10.1186/s12859-024-05966-5.

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