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The Linear Noise Approximation for Spatially Dependent Biochemical Networks

An algorithm for computing the linear noise approximation (LNA) of the reaction–diffusion master equation (RDME) is developed and tested. The RDME is often used as a model for biochemical reaction networks. The LNA is derived for a general discretization of the spatial domain of the problem. If M is...

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Autor principal: Lötstedt, Per
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677697/
https://www.ncbi.nlm.nih.gov/pubmed/29644520
http://dx.doi.org/10.1007/s11538-018-0428-0
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author Lötstedt, Per
author_facet Lötstedt, Per
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description An algorithm for computing the linear noise approximation (LNA) of the reaction–diffusion master equation (RDME) is developed and tested. The RDME is often used as a model for biochemical reaction networks. The LNA is derived for a general discretization of the spatial domain of the problem. If M is the number of chemical species in the network and N is the number of nodes in the discretization in space, then the computational work to determine approximations of the mean and the covariances of the probability distributions is proportional to [Formula: see text] in a straightforward implementation. In our LNA algorithm, the work is proportional to [Formula: see text] . Since N usually is larger than M, this is a significant reduction. The accuracy of the approximation in the algorithm is estimated analytically and evaluated in numerical experiments.
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spelling pubmed-66776972019-08-16 The Linear Noise Approximation for Spatially Dependent Biochemical Networks Lötstedt, Per Bull Math Biol Special Issue: Gillespie and His Algorithms An algorithm for computing the linear noise approximation (LNA) of the reaction–diffusion master equation (RDME) is developed and tested. The RDME is often used as a model for biochemical reaction networks. The LNA is derived for a general discretization of the spatial domain of the problem. If M is the number of chemical species in the network and N is the number of nodes in the discretization in space, then the computational work to determine approximations of the mean and the covariances of the probability distributions is proportional to [Formula: see text] in a straightforward implementation. In our LNA algorithm, the work is proportional to [Formula: see text] . Since N usually is larger than M, this is a significant reduction. The accuracy of the approximation in the algorithm is estimated analytically and evaluated in numerical experiments. Springer US 2018-04-11 2019 /pmc/articles/PMC6677697/ /pubmed/29644520 http://dx.doi.org/10.1007/s11538-018-0428-0 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Special Issue: Gillespie and His Algorithms
Lötstedt, Per
The Linear Noise Approximation for Spatially Dependent Biochemical Networks
title The Linear Noise Approximation for Spatially Dependent Biochemical Networks
title_full The Linear Noise Approximation for Spatially Dependent Biochemical Networks
title_fullStr The Linear Noise Approximation for Spatially Dependent Biochemical Networks
title_full_unstemmed The Linear Noise Approximation for Spatially Dependent Biochemical Networks
title_short The Linear Noise Approximation for Spatially Dependent Biochemical Networks
title_sort linear noise approximation for spatially dependent biochemical networks
topic Special Issue: Gillespie and His Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677697/
https://www.ncbi.nlm.nih.gov/pubmed/29644520
http://dx.doi.org/10.1007/s11538-018-0428-0
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