Neural network aided approximation and parameter inference of non-Markovian models of gene expression

Cells are complex systems that make decisions biologists struggle to understand. Here, the authors use neural networks to approximate the solution of mathematical models that capture the history and randomness of biochemical processes in order to understand the principles of transcription control.

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Autores principales:	Qingchao Jiang, Xiaoming Fu, Shifu Yan, Runlai Li, Wenli Du, Zhixing Cao, Feng Qian, Ramon Grima
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4bc73034fff748fc9da54f683d9b7be8
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spelling	oai:doaj.org-article:4bc73034fff748fc9da54f683d9b7be82021-12-02T16:58:08ZNeural network aided approximation and parameter inference of non-Markovian models of gene expression10.1038/s41467-021-22919-12041-1723https://doaj.org/article/4bc73034fff748fc9da54f683d9b7be82021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22919-1https://doaj.org/toc/2041-1723Cells are complex systems that make decisions biologists struggle to understand. Here, the authors use neural networks to approximate the solution of mathematical models that capture the history and randomness of biochemical processes in order to understand the principles of transcription control.Qingchao JiangXiaoming FuShifu YanRunlai LiWenli DuZhixing CaoFeng QianRamon GrimaNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Qingchao Jiang Xiaoming Fu Shifu Yan Runlai Li Wenli Du Zhixing Cao Feng Qian Ramon Grima Neural network aided approximation and parameter inference of non-Markovian models of gene expression
description	Cells are complex systems that make decisions biologists struggle to understand. Here, the authors use neural networks to approximate the solution of mathematical models that capture the history and randomness of biochemical processes in order to understand the principles of transcription control.
format	article
author	Qingchao Jiang Xiaoming Fu Shifu Yan Runlai Li Wenli Du Zhixing Cao Feng Qian Ramon Grima
author_facet	Qingchao Jiang Xiaoming Fu Shifu Yan Runlai Li Wenli Du Zhixing Cao Feng Qian Ramon Grima
author_sort	Qingchao Jiang
title	Neural network aided approximation and parameter inference of non-Markovian models of gene expression
title_short	Neural network aided approximation and parameter inference of non-Markovian models of gene expression
title_full	Neural network aided approximation and parameter inference of non-Markovian models of gene expression
title_fullStr	Neural network aided approximation and parameter inference of non-Markovian models of gene expression
title_full_unstemmed	Neural network aided approximation and parameter inference of non-Markovian models of gene expression
title_sort	neural network aided approximation and parameter inference of non-markovian models of gene expression
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4bc73034fff748fc9da54f683d9b7be8
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Neural network aided approximation and parameter inference of non-Markovian models of gene expression

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