Adaptive Models for Gene Networks

Biological systems are often treated as time-invariant by computational models that use fixed parameter values. In this study, we demonstrate that the behavior of the p53-MDM2 gene network in individual cells can be tracked using adaptive filtering algorithms and the resulting time-variant models ca...

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Detalles Bibliográficos
Autores principales: Shin, Yong-Jun, Sayed, Ali H., Shen, Xiling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3280989/
https://www.ncbi.nlm.nih.gov/pubmed/22359614
http://dx.doi.org/10.1371/journal.pone.0031657
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author Shin, Yong-Jun
Sayed, Ali H.
Shen, Xiling
author_facet Shin, Yong-Jun
Sayed, Ali H.
Shen, Xiling
author_sort Shin, Yong-Jun
collection PubMed
description Biological systems are often treated as time-invariant by computational models that use fixed parameter values. In this study, we demonstrate that the behavior of the p53-MDM2 gene network in individual cells can be tracked using adaptive filtering algorithms and the resulting time-variant models can approximate experimental measurements more accurately than time-invariant models. Adaptive models with time-variant parameters can help reduce modeling complexity and can more realistically represent biological systems.
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spelling pubmed-32809892012-02-22 Adaptive Models for Gene Networks Shin, Yong-Jun Sayed, Ali H. Shen, Xiling PLoS One Research Article Biological systems are often treated as time-invariant by computational models that use fixed parameter values. In this study, we demonstrate that the behavior of the p53-MDM2 gene network in individual cells can be tracked using adaptive filtering algorithms and the resulting time-variant models can approximate experimental measurements more accurately than time-invariant models. Adaptive models with time-variant parameters can help reduce modeling complexity and can more realistically represent biological systems. Public Library of Science 2012-02-16 /pmc/articles/PMC3280989/ /pubmed/22359614 http://dx.doi.org/10.1371/journal.pone.0031657 Text en Shin et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Shin, Yong-Jun
Sayed, Ali H.
Shen, Xiling
Adaptive Models for Gene Networks
title Adaptive Models for Gene Networks
title_full Adaptive Models for Gene Networks
title_fullStr Adaptive Models for Gene Networks
title_full_unstemmed Adaptive Models for Gene Networks
title_short Adaptive Models for Gene Networks
title_sort adaptive models for gene networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3280989/
https://www.ncbi.nlm.nih.gov/pubmed/22359614
http://dx.doi.org/10.1371/journal.pone.0031657
work_keys_str_mv AT shinyongjun adaptivemodelsforgenenetworks
AT sayedalih adaptivemodelsforgenenetworks
AT shenxiling adaptivemodelsforgenenetworks

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