Abstraction-based segmental simulation of reaction networks using adaptive memoization

Result code in 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>
Alternative codes found
RIV/00216224:14330/24:00138792
Result on the web
<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>

Result language
angličtina
Original language name
Abstraction-based segmental simulation of reaction networks using adaptive memoization
Original language description
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.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GJ20-02328Y" target="_blank" >GJ20-02328Y: CAQtuS: Computer-Aided Quantitative Synthesis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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