Incorporating extrinsic noise into the stochastic simulation of biochemical reactions: A comparison of approaches

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Incorporating extrinsic noise into the stochastic simulation of biochemical reactions: A comparison of approaches. / Thanh, V.H.; Marchetti, L.; Reali, F.; Priami, C.

In: Journal of Chemical Physics, Vol. 148, No. 6, 2018.

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@article{41f1bd6606014d67895f0ba40bbe2629,
title = "Incorporating extrinsic noise into the stochastic simulation of biochemical reactions: A comparison of approaches",
abstract = "The stochastic simulation algorithm (SSA) has been widely used for simulating biochemical reaction networks. SSA is able to capture the inherently intrinsic noise of the biological system, which is due to the discreteness of species population and to the randomness of their reciprocal interactions. However, SSA does not consider other sources of heterogeneity in biochemical reaction systems, which are referred to as extrinsic noise. Here, we extend two simulation approaches, namely, the integration-based method and the rejection-based method, to take extrinsic noise into account by allowing the reaction propensities to vary in time and state dependent manner. For both methods, new efficient implementations are introduced and their efficiency and applicability to biological models are investigated. Our numerical results suggest that the rejection-based method performs better than the integration-based method when the extrinsic noise is considered. {\circledC} 2018 Author(s).",
keywords = "Stochastic models, Stochastic systems, Biochemical reaction network, Biochemical reactions, Biological models, Efficient implementation, Numerical results, Simulation approach, Stochastic simulation algorithms, Stochastic simulations, Numerical methods, algorithm, biochemistry, biological model, comparative study, computer simulation, Markov chain, Algorithms, Biochemical Phenomena, Computer Simulation, Models, Biological, Stochastic Processes",
author = "V.H. Thanh and L. Marchetti and F. Reali and C. Priami",
note = "cited By 0",
year = "2018",
doi = "10.1063/1.5016338",
language = "English",
volume = "148",
journal = "Journal of Chemical Physics",
issn = "0021-9606",
publisher = "AMERICAN INSTITUTE OF PHYSICS",
number = "6",

}

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TY - JOUR

T1 - Incorporating extrinsic noise into the stochastic simulation of biochemical reactions: A comparison of approaches

AU - Thanh, V.H.

AU - Marchetti, L.

AU - Reali, F.

AU - Priami, C.

N1 - cited By 0

PY - 2018

Y1 - 2018

N2 - The stochastic simulation algorithm (SSA) has been widely used for simulating biochemical reaction networks. SSA is able to capture the inherently intrinsic noise of the biological system, which is due to the discreteness of species population and to the randomness of their reciprocal interactions. However, SSA does not consider other sources of heterogeneity in biochemical reaction systems, which are referred to as extrinsic noise. Here, we extend two simulation approaches, namely, the integration-based method and the rejection-based method, to take extrinsic noise into account by allowing the reaction propensities to vary in time and state dependent manner. For both methods, new efficient implementations are introduced and their efficiency and applicability to biological models are investigated. Our numerical results suggest that the rejection-based method performs better than the integration-based method when the extrinsic noise is considered. © 2018 Author(s).

AB - The stochastic simulation algorithm (SSA) has been widely used for simulating biochemical reaction networks. SSA is able to capture the inherently intrinsic noise of the biological system, which is due to the discreteness of species population and to the randomness of their reciprocal interactions. However, SSA does not consider other sources of heterogeneity in biochemical reaction systems, which are referred to as extrinsic noise. Here, we extend two simulation approaches, namely, the integration-based method and the rejection-based method, to take extrinsic noise into account by allowing the reaction propensities to vary in time and state dependent manner. For both methods, new efficient implementations are introduced and their efficiency and applicability to biological models are investigated. Our numerical results suggest that the rejection-based method performs better than the integration-based method when the extrinsic noise is considered. © 2018 Author(s).

KW - Stochastic models

KW - Stochastic systems, Biochemical reaction network

KW - Biochemical reactions

KW - Biological models

KW - Efficient implementation

KW - Numerical results

KW - Simulation approach

KW - Stochastic simulation algorithms

KW - Stochastic simulations, Numerical methods, algorithm

KW - biochemistry

KW - biological model

KW - comparative study

KW - computer simulation

KW - Markov chain, Algorithms

KW - Biochemical Phenomena

KW - Computer Simulation

KW - Models, Biological

KW - Stochastic Processes

U2 - 10.1063/1.5016338

DO - 10.1063/1.5016338

M3 - Article

VL - 148

JO - Journal of Chemical Physics

JF - Journal of Chemical Physics

SN - 0021-9606

IS - 6

ER -

ID: 27839782