Efficient anticorrelated variance reduction for stochastic simulation of biochemical reactions

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Efficient anticorrelated variance reduction for stochastic simulation of biochemical reactions. / Vo, Thanh.

julkaisussa: IET SYSTEMS BIOLOGY, 09.2018.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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@article{242b7c2b803a451a83098d01b7fed2d1,
title = "Efficient anticorrelated variance reduction for stochastic simulation of biochemical reactions",
abstract = "We investigate the computational challenge of improving the accuracy of the stochastic simulation estimation by inducing negative correlation through the anticorrelated variance reduction technique. A direct application of the technique to the stochastic simulation algorithm (SSA), employing the inverse transformation, is not efficient for simulating large networks because its computational cost is similar to the sum of independent simulation runs. We propose in this study a new algorithm that employs the propensity bounds of reactions, introduced recently in their rejection-based SSA, to correlate and synchronise the trajectories during the simulation. The selection of reaction firings by our approach is exact due to the rejection-based mechanism. In addition, by applying the anticorrelated variance technique to select reaction firings, our approach can induce substantial correlation between realisations, hence reducing the variance of the estimator. The computational advantage of our rejection-based approach in comparison with the traditional inverse transformation is that it only needs to maintain a single data structure storing propensity bounds of reactions, which is updated infrequently, hence achieving better performance.",
keywords = "Stochastic processes, computational biology, Stochastic simulation algorithms",
author = "Thanh Vo",
year = "2018",
month = "9",
doi = "10.1049/iet-syb.2018.5035",
language = "English",
journal = "IET SYSTEMS BIOLOGY",
issn = "1751-8849",
publisher = "Institution of Engineering and Technology",

}

RIS - Lataa

TY - JOUR

T1 - Efficient anticorrelated variance reduction for stochastic simulation of biochemical reactions

AU - Vo, Thanh

PY - 2018/9

Y1 - 2018/9

N2 - We investigate the computational challenge of improving the accuracy of the stochastic simulation estimation by inducing negative correlation through the anticorrelated variance reduction technique. A direct application of the technique to the stochastic simulation algorithm (SSA), employing the inverse transformation, is not efficient for simulating large networks because its computational cost is similar to the sum of independent simulation runs. We propose in this study a new algorithm that employs the propensity bounds of reactions, introduced recently in their rejection-based SSA, to correlate and synchronise the trajectories during the simulation. The selection of reaction firings by our approach is exact due to the rejection-based mechanism. In addition, by applying the anticorrelated variance technique to select reaction firings, our approach can induce substantial correlation between realisations, hence reducing the variance of the estimator. The computational advantage of our rejection-based approach in comparison with the traditional inverse transformation is that it only needs to maintain a single data structure storing propensity bounds of reactions, which is updated infrequently, hence achieving better performance.

AB - We investigate the computational challenge of improving the accuracy of the stochastic simulation estimation by inducing negative correlation through the anticorrelated variance reduction technique. A direct application of the technique to the stochastic simulation algorithm (SSA), employing the inverse transformation, is not efficient for simulating large networks because its computational cost is similar to the sum of independent simulation runs. We propose in this study a new algorithm that employs the propensity bounds of reactions, introduced recently in their rejection-based SSA, to correlate and synchronise the trajectories during the simulation. The selection of reaction firings by our approach is exact due to the rejection-based mechanism. In addition, by applying the anticorrelated variance technique to select reaction firings, our approach can induce substantial correlation between realisations, hence reducing the variance of the estimator. The computational advantage of our rejection-based approach in comparison with the traditional inverse transformation is that it only needs to maintain a single data structure storing propensity bounds of reactions, which is updated infrequently, hence achieving better performance.

KW - Stochastic processes

KW - computational biology

KW - Stochastic simulation algorithms

UR - https://digital-library.theiet.org/content/journals/10.1049/iet-syb.2018.5035

U2 - 10.1049/iet-syb.2018.5035

DO - 10.1049/iet-syb.2018.5035

M3 - Article

JO - IET SYSTEMS BIOLOGY

JF - IET SYSTEMS BIOLOGY

SN - 1751-8849

ER -

ID: 31145254