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Efficient and flexible implementation of Langevin simulation for gene burst production

Gene expression involves bursts of production of both mRNA and protein, and the fluctuations in their number are increased due to such bursts. The Langevin equation is an efficient and versatile means to simulate such number fluctuation. However, how to include these mRNA and protein bursts in the L...

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Autores principales: Yan, Ching-Cher Sanders, Chepyala, Surendhar Reddy, Yen, Chao-Ming, Hsu, Chao-Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5715166/
https://www.ncbi.nlm.nih.gov/pubmed/29203832
http://dx.doi.org/10.1038/s41598-017-16835-y
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author Yan, Ching-Cher Sanders
Chepyala, Surendhar Reddy
Yen, Chao-Ming
Hsu, Chao-Ping
author_facet Yan, Ching-Cher Sanders
Chepyala, Surendhar Reddy
Yen, Chao-Ming
Hsu, Chao-Ping
author_sort Yan, Ching-Cher Sanders
collection PubMed
description Gene expression involves bursts of production of both mRNA and protein, and the fluctuations in their number are increased due to such bursts. The Langevin equation is an efficient and versatile means to simulate such number fluctuation. However, how to include these mRNA and protein bursts in the Langevin equation is not intuitively clear. In this work, we estimated the variance in burst production from a general gene expression model and introduced such variation in the Langevin equation. Our approach offers different Langevin expressions for either or both transcriptional and translational bursts considered and saves computer time by including many production events at once in a short burst time. The errors can be controlled to be rather precise (<2%) for the mean and <10% for the standard deviation of the steady-state distribution. Our scheme allows for high-quality stochastic simulations with the Langevin equation for gene expression, which is useful in analysis of biological networks.
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spelling pubmed-57151662017-12-08 Efficient and flexible implementation of Langevin simulation for gene burst production Yan, Ching-Cher Sanders Chepyala, Surendhar Reddy Yen, Chao-Ming Hsu, Chao-Ping Sci Rep Article Gene expression involves bursts of production of both mRNA and protein, and the fluctuations in their number are increased due to such bursts. The Langevin equation is an efficient and versatile means to simulate such number fluctuation. However, how to include these mRNA and protein bursts in the Langevin equation is not intuitively clear. In this work, we estimated the variance in burst production from a general gene expression model and introduced such variation in the Langevin equation. Our approach offers different Langevin expressions for either or both transcriptional and translational bursts considered and saves computer time by including many production events at once in a short burst time. The errors can be controlled to be rather precise (<2%) for the mean and <10% for the standard deviation of the steady-state distribution. Our scheme allows for high-quality stochastic simulations with the Langevin equation for gene expression, which is useful in analysis of biological networks. Nature Publishing Group UK 2017-12-04 /pmc/articles/PMC5715166/ /pubmed/29203832 http://dx.doi.org/10.1038/s41598-017-16835-y Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Yan, Ching-Cher Sanders
Chepyala, Surendhar Reddy
Yen, Chao-Ming
Hsu, Chao-Ping
Efficient and flexible implementation of Langevin simulation for gene burst production
title Efficient and flexible implementation of Langevin simulation for gene burst production
title_full Efficient and flexible implementation of Langevin simulation for gene burst production
title_fullStr Efficient and flexible implementation of Langevin simulation for gene burst production
title_full_unstemmed Efficient and flexible implementation of Langevin simulation for gene burst production
title_short Efficient and flexible implementation of Langevin simulation for gene burst production
title_sort efficient and flexible implementation of langevin simulation for gene burst production
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5715166/
https://www.ncbi.nlm.nih.gov/pubmed/29203832
http://dx.doi.org/10.1038/s41598-017-16835-y
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