Publication Details
Abstraction-based segmental simulation of reaction networks using adaptive memoization
Andriushchenko Roman, Ing. (DITS)
Češka Milan, doc. RNDr., Ph.D. (DITS)
KŘETÍNSKÝ, J.
Martiček Štefan, Ing.
Šafránek David, doc. RNDr., Ph.D.
Reaction networks, stochastic simulation, population abstraction, memoization
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.
@article{BUT193584,
author="HELFRICH, M. and ANDRIUSHCHENKO, R. and ČEŠKA, M. and KŘETÍNSKÝ, J. and MARTIČEK, Š. and ŠAFRÁNEK, D.",
title="Abstraction-based segmental simulation of reaction networks using adaptive memoization",
journal="BMC BIOINFORMATICS",
year="2024",
volume="25",
number="1",
pages="1--24",
doi="10.1186/s12859-024-05966-5",
issn="1471-2105",
url="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05966-5"
}