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A model comparison study of the flowering time regulatory network in Arabidopsis
BACKGROUND: Several dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accura...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3938817/ https://www.ncbi.nlm.nih.gov/pubmed/24513114 http://dx.doi.org/10.1186/1752-0509-8-15 |
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author | Wang, Charles CN Chang, Pei-Chun Ng, Ka-Lok Chang, Chun-Ming Sheu, Phillip CY Tsai, Jeffrey JP |
author_facet | Wang, Charles CN Chang, Pei-Chun Ng, Ka-Lok Chang, Chun-Ming Sheu, Phillip CY Tsai, Jeffrey JP |
author_sort | Wang, Charles CN |
collection | PubMed |
description | BACKGROUND: Several dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accurate and novel predictions, which generate testable hypotheses. Two major issues were addressed in this study. First, construction of dynamic models for gene regulatory networks requires the use of mathematic modeling that comprises equations of a large number of parameters. Second, the binding mechanism of the transcription factor with DNA is another factor that requires detailed modeling. The first issue was tackled by adopting an optimization algorithm, and the second was addressed by comparing the performance of three alternative modeling approaches, namely the S-system, the Michaelis-Menten model and the Mass-action model. The efficiencies of parameter estimation and modeling performance were calculated based on least square error (O(p)), mean relative error (MRE) and Akaike Information Criterion (AIC). RESULTS: We compared three models to describe gene regulation of the flowering transition process in Arabidopsis. The Mass-action model is the simplest and has the least parameters. It is therefore less computation-intensive with the smallest AIC value. The disadvantage, however, is that it assumes the system is simply a second order reaction which is not the case in our study. The Michaelis-Menten model also assumes the system is homogeneous and ignores the intracellular protein transport process. The S-system model has the best performance and it does describe the diffusion effects. A disadvantage of the S-system is that it involves the most parameters. The largest AIC value also implies an over-fitting may occur in parameter estimation. CONCLUSIONS: Three dynamic models were adopted to describe the dynamics of the gene regulatory network of the flowering transition process in Arabidopsis. Based on MRE, the least square error and global sensitivity analysis, the S-system has the best performance. However, the fact that it has the highest AIC suggests an over-fitting may occur in parameter estimation. The result of this study may need to be applied carefully when modeling complex gene regulatory networks. |
format | Online Article Text |
id | pubmed-3938817 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-39388172014-03-10 A model comparison study of the flowering time regulatory network in Arabidopsis Wang, Charles CN Chang, Pei-Chun Ng, Ka-Lok Chang, Chun-Ming Sheu, Phillip CY Tsai, Jeffrey JP BMC Syst Biol Research Article BACKGROUND: Several dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accurate and novel predictions, which generate testable hypotheses. Two major issues were addressed in this study. First, construction of dynamic models for gene regulatory networks requires the use of mathematic modeling that comprises equations of a large number of parameters. Second, the binding mechanism of the transcription factor with DNA is another factor that requires detailed modeling. The first issue was tackled by adopting an optimization algorithm, and the second was addressed by comparing the performance of three alternative modeling approaches, namely the S-system, the Michaelis-Menten model and the Mass-action model. The efficiencies of parameter estimation and modeling performance were calculated based on least square error (O(p)), mean relative error (MRE) and Akaike Information Criterion (AIC). RESULTS: We compared three models to describe gene regulation of the flowering transition process in Arabidopsis. The Mass-action model is the simplest and has the least parameters. It is therefore less computation-intensive with the smallest AIC value. The disadvantage, however, is that it assumes the system is simply a second order reaction which is not the case in our study. The Michaelis-Menten model also assumes the system is homogeneous and ignores the intracellular protein transport process. The S-system model has the best performance and it does describe the diffusion effects. A disadvantage of the S-system is that it involves the most parameters. The largest AIC value also implies an over-fitting may occur in parameter estimation. CONCLUSIONS: Three dynamic models were adopted to describe the dynamics of the gene regulatory network of the flowering transition process in Arabidopsis. Based on MRE, the least square error and global sensitivity analysis, the S-system has the best performance. However, the fact that it has the highest AIC suggests an over-fitting may occur in parameter estimation. The result of this study may need to be applied carefully when modeling complex gene regulatory networks. BioMed Central 2014-02-11 /pmc/articles/PMC3938817/ /pubmed/24513114 http://dx.doi.org/10.1186/1752-0509-8-15 Text en Copyright © 2014 Wang et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. |
spellingShingle | Research Article Wang, Charles CN Chang, Pei-Chun Ng, Ka-Lok Chang, Chun-Ming Sheu, Phillip CY Tsai, Jeffrey JP A model comparison study of the flowering time regulatory network in Arabidopsis |
title | A model comparison study of the flowering time regulatory network in Arabidopsis |
title_full | A model comparison study of the flowering time regulatory network in Arabidopsis |
title_fullStr | A model comparison study of the flowering time regulatory network in Arabidopsis |
title_full_unstemmed | A model comparison study of the flowering time regulatory network in Arabidopsis |
title_short | A model comparison study of the flowering time regulatory network in Arabidopsis |
title_sort | model comparison study of the flowering time regulatory network in arabidopsis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3938817/ https://www.ncbi.nlm.nih.gov/pubmed/24513114 http://dx.doi.org/10.1186/1752-0509-8-15 |
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