Abstraction-based segmental simulation of reaction networks using adaptive memoization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F24%3APU154883" target="_blank" >RIV/00216305:26230/24:PU154883 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14330/24:00138792

Výsledek na webu

<a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05966-5" target="_blank" >https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05966-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12859-024-05966-5" target="_blank" >10.1186/s12859-024-05966-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Abstraction-based segmental simulation of reaction networks using adaptive memoization

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Abstraction-based segmental simulation of reaction networks using adaptive memoization

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GJ20-02328Y" target="_blank" >GJ20-02328Y: CAQtuS: Počítačem podporovaná kvantitativní syntéza</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

BMC BIOINFORMATICS

ISSN

1471-2105

e-ISSN

—

Svazek periodika

25

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

24

Strana od-do

1-24

Kód UT WoS článku

001351556400001

EID výsledku v databázi Scopus

2-s2.0-85209476640