Abstraction-based segmental simulation of reaction networks using adaptive memoization

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Authors

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

Year of publication 2024
Type Article in Periodical
Magazine / Source BMC Bioinformatics
MU Faculty or unit

Faculty of Informatics

Citation
web https://link.springer.com/article/10.1186/s12859-024-05966-5
Doi http://dx.doi.org/10.1186/s12859-024-05966-5
Keywords Reaction networks; Stochastic simulation; Population abstraction; Memoization
Description 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.
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