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Steady-State-Preserving Simulation of Genetic Regulatory Systems

A novel family of exponential Runge-Kutta (expRK) methods are designed incorporating the stable steady-state structure of genetic regulatory systems. A natural and convenient approach to constructing new expRK methods on the base of traditional RK methods is provided. In the numerical integration of...

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Autores principales: Zhang, Ruqiang, Ehigie, Julius Osato, Hou, Xilin, You, Xiong, Yuan, Chunlu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288607/
https://www.ncbi.nlm.nih.gov/pubmed/28203268
http://dx.doi.org/10.1155/2017/2729683
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author Zhang, Ruqiang
Ehigie, Julius Osato
Hou, Xilin
You, Xiong
Yuan, Chunlu
author_facet Zhang, Ruqiang
Ehigie, Julius Osato
Hou, Xilin
You, Xiong
Yuan, Chunlu
author_sort Zhang, Ruqiang
collection PubMed
description A novel family of exponential Runge-Kutta (expRK) methods are designed incorporating the stable steady-state structure of genetic regulatory systems. A natural and convenient approach to constructing new expRK methods on the base of traditional RK methods is provided. In the numerical integration of the one-gene, two-gene, and p53-mdm2 regulatory systems, the new expRK methods are shown to be more accurate than their prototype RK methods. Moreover, for nonstiff genetic regulatory systems, the expRK methods are more efficient than some traditional exponential RK integrators in the scientific literature.
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spelling pubmed-52886072017-02-15 Steady-State-Preserving Simulation of Genetic Regulatory Systems Zhang, Ruqiang Ehigie, Julius Osato Hou, Xilin You, Xiong Yuan, Chunlu Comput Math Methods Med Research Article A novel family of exponential Runge-Kutta (expRK) methods are designed incorporating the stable steady-state structure of genetic regulatory systems. A natural and convenient approach to constructing new expRK methods on the base of traditional RK methods is provided. In the numerical integration of the one-gene, two-gene, and p53-mdm2 regulatory systems, the new expRK methods are shown to be more accurate than their prototype RK methods. Moreover, for nonstiff genetic regulatory systems, the expRK methods are more efficient than some traditional exponential RK integrators in the scientific literature. Hindawi Publishing Corporation 2017 2017-01-19 /pmc/articles/PMC5288607/ /pubmed/28203268 http://dx.doi.org/10.1155/2017/2729683 Text en Copyright © 2017 Ruqiang Zhang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Ruqiang
Ehigie, Julius Osato
Hou, Xilin
You, Xiong
Yuan, Chunlu
Steady-State-Preserving Simulation of Genetic Regulatory Systems
title Steady-State-Preserving Simulation of Genetic Regulatory Systems
title_full Steady-State-Preserving Simulation of Genetic Regulatory Systems
title_fullStr Steady-State-Preserving Simulation of Genetic Regulatory Systems
title_full_unstemmed Steady-State-Preserving Simulation of Genetic Regulatory Systems
title_short Steady-State-Preserving Simulation of Genetic Regulatory Systems
title_sort steady-state-preserving simulation of genetic regulatory systems
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288607/
https://www.ncbi.nlm.nih.gov/pubmed/28203268
http://dx.doi.org/10.1155/2017/2729683
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