Steady-State-Preserving Simulation of Genetic Regulatory Systems

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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Main Authors: Ruqiang Zhang, Julius Osato Ehigie, Xilin Hou, Xiong You, Chunlu Yuan
Format: Article
Language:English
Published: Hindawi Limited 2017-01-01
Series:Computational and Mathematical Methods in Medicine
Online Access:http://dx.doi.org/10.1155/2017/2729683
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spelling doaj-2e8001c8c94246b1b3e6546e0709f9292020-11-24T23:20:27ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182017-01-01201710.1155/2017/27296832729683Steady-State-Preserving Simulation of Genetic Regulatory SystemsRuqiang Zhang0Julius Osato Ehigie1Xilin Hou2Xiong You3Chunlu Yuan4College of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaA 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.http://dx.doi.org/10.1155/2017/2729683
collection DOAJ
language English
format Article
sources DOAJ
author Ruqiang Zhang
Julius Osato Ehigie
Xilin Hou
Xiong You
Chunlu Yuan
spellingShingle Ruqiang Zhang
Julius Osato Ehigie
Xilin Hou
Xiong You
Chunlu Yuan
Steady-State-Preserving Simulation of Genetic Regulatory Systems
Computational and Mathematical Methods in Medicine
author_facet Ruqiang Zhang
Julius Osato Ehigie
Xilin Hou
Xiong You
Chunlu Yuan
author_sort Ruqiang Zhang
title Steady-State-Preserving Simulation of Genetic Regulatory Systems
title_short 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_sort steady-state-preserving simulation of genetic regulatory systems
publisher Hindawi Limited
series Computational and Mathematical Methods in Medicine
issn 1748-670X
1748-6718
publishDate 2017-01-01
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.
url http://dx.doi.org/10.1155/2017/2729683
work_keys_str_mv AT ruqiangzhang steadystatepreservingsimulationofgeneticregulatorysystems
AT juliusosatoehigie steadystatepreservingsimulationofgeneticregulatorysystems
AT xilinhou steadystatepreservingsimulationofgeneticregulatorysystems
AT xiongyou steadystatepreservingsimulationofgeneticregulatorysystems
AT chunluyuan steadystatepreservingsimulationofgeneticregulatorysystems
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