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State Observer Design for Delayed Genetic Regulatory Networks

Genetic regulatory networks are dynamic systems which describe the interactions among gene products (mRNAs and proteins). The internal states of a genetic regulatory network consist of the concentrations of mRNA and proteins involved in it, which are very helpful in understanding its dynamic behavio...

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Autores principales: Tian, Li-Ping, Wang, Zhi-Jun, Mohammadbagheri, Amin, Wu, Fang-Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4054920/
https://www.ncbi.nlm.nih.gov/pubmed/24963341
http://dx.doi.org/10.1155/2014/761562
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author Tian, Li-Ping
Wang, Zhi-Jun
Mohammadbagheri, Amin
Wu, Fang-Xiang
author_facet Tian, Li-Ping
Wang, Zhi-Jun
Mohammadbagheri, Amin
Wu, Fang-Xiang
author_sort Tian, Li-Ping
collection PubMed
description Genetic regulatory networks are dynamic systems which describe the interactions among gene products (mRNAs and proteins). The internal states of a genetic regulatory network consist of the concentrations of mRNA and proteins involved in it, which are very helpful in understanding its dynamic behaviors. However, because of some limitations such as experiment techniques, not all internal states of genetic regulatory network can be effectively measured. Therefore it becomes an important issue to estimate the unmeasured states via the available measurements. In this study, we design a state observer to estimate the states of genetic regulatory networks with time delays from available measurements. Furthermore, based on linear matrix inequality (LMI) approach, a criterion is established to guarantee that the dynamic of estimation error is globally asymptotically stable. A gene repressillatory network is employed to illustrate the effectiveness of our design approach.
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spelling pubmed-40549202014-06-24 State Observer Design for Delayed Genetic Regulatory Networks Tian, Li-Ping Wang, Zhi-Jun Mohammadbagheri, Amin Wu, Fang-Xiang Comput Math Methods Med Research Article Genetic regulatory networks are dynamic systems which describe the interactions among gene products (mRNAs and proteins). The internal states of a genetic regulatory network consist of the concentrations of mRNA and proteins involved in it, which are very helpful in understanding its dynamic behaviors. However, because of some limitations such as experiment techniques, not all internal states of genetic regulatory network can be effectively measured. Therefore it becomes an important issue to estimate the unmeasured states via the available measurements. In this study, we design a state observer to estimate the states of genetic regulatory networks with time delays from available measurements. Furthermore, based on linear matrix inequality (LMI) approach, a criterion is established to guarantee that the dynamic of estimation error is globally asymptotically stable. A gene repressillatory network is employed to illustrate the effectiveness of our design approach. Hindawi Publishing Corporation 2014 2014-05-22 /pmc/articles/PMC4054920/ /pubmed/24963341 http://dx.doi.org/10.1155/2014/761562 Text en Copyright © 2014 Li-Ping Tian 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
Tian, Li-Ping
Wang, Zhi-Jun
Mohammadbagheri, Amin
Wu, Fang-Xiang
State Observer Design for Delayed Genetic Regulatory Networks
title State Observer Design for Delayed Genetic Regulatory Networks
title_full State Observer Design for Delayed Genetic Regulatory Networks
title_fullStr State Observer Design for Delayed Genetic Regulatory Networks
title_full_unstemmed State Observer Design for Delayed Genetic Regulatory Networks
title_short State Observer Design for Delayed Genetic Regulatory Networks
title_sort state observer design for delayed genetic regulatory networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4054920/
https://www.ncbi.nlm.nih.gov/pubmed/24963341
http://dx.doi.org/10.1155/2014/761562
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