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Splitting Strategy for Simulating Genetic Regulatory Networks
The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this pape...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3929534/ https://www.ncbi.nlm.nih.gov/pubmed/24624223 http://dx.doi.org/10.1155/2014/683235 |
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author | You, Xiong Liu, Xueping Musa, Ibrahim Hussein |
author_facet | You, Xiong Liu, Xueping Musa, Ibrahim Hussein |
author_sort | You, Xiong |
collection | PubMed |
description | The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions. |
format | Online Article Text |
id | pubmed-3929534 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-39295342014-03-12 Splitting Strategy for Simulating Genetic Regulatory Networks You, Xiong Liu, Xueping Musa, Ibrahim Hussein Comput Math Methods Med Research Article The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions. Hindawi Publishing Corporation 2014 2014-02-02 /pmc/articles/PMC3929534/ /pubmed/24624223 http://dx.doi.org/10.1155/2014/683235 Text en Copyright © 2014 Xiong You et al. https://creativecommons.org/licenses/by/3.0/ 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 You, Xiong Liu, Xueping Musa, Ibrahim Hussein Splitting Strategy for Simulating Genetic Regulatory Networks |
title | Splitting Strategy for Simulating Genetic Regulatory Networks |
title_full | Splitting Strategy for Simulating Genetic Regulatory Networks |
title_fullStr | Splitting Strategy for Simulating Genetic Regulatory Networks |
title_full_unstemmed | Splitting Strategy for Simulating Genetic Regulatory Networks |
title_short | Splitting Strategy for Simulating Genetic Regulatory Networks |
title_sort | splitting strategy for simulating genetic regulatory networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3929534/ https://www.ncbi.nlm.nih.gov/pubmed/24624223 http://dx.doi.org/10.1155/2014/683235 |
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