Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: You, Xiong, Liu, Xueping, Musa, Ibrahim Hussein
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
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
_version_ 1782304402698141696
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
work_keys_str_mv AT youxiong splittingstrategyforsimulatinggeneticregulatorynetworks
AT liuxueping splittingstrategyforsimulatinggeneticregulatorynetworks
AT musaibrahimhussein splittingstrategyforsimulatinggeneticregulatorynetworks