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Estimate hidden dynamic profiles of siRNA effect on apoptosis

BACKGROUND: For the representation of RNA interference (RNAi) dynamics, several mathematical models based on systems of ordinary differential equations (ODEs) have been proposed. These models consist of equations for each molecule that are involved in RNAi phenomena. Therefore, many real-value param...

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Autores principales: Ueda, Takanori, Tominaga, Daisuke, Araki, Noriko, Yoshikawa, Tomohiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3663715/
https://www.ncbi.nlm.nih.gov/pubmed/23496896
http://dx.doi.org/10.1186/1471-2105-14-97
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author Ueda, Takanori
Tominaga, Daisuke
Araki, Noriko
Yoshikawa, Tomohiro
author_facet Ueda, Takanori
Tominaga, Daisuke
Araki, Noriko
Yoshikawa, Tomohiro
author_sort Ueda, Takanori
collection PubMed
description BACKGROUND: For the representation of RNA interference (RNAi) dynamics, several mathematical models based on systems of ordinary differential equations (ODEs) have been proposed. These models consist of equations for each molecule that are involved in RNAi phenomena. Therefore, many real-value parameters must be optimized to identify the models. They also have many ‘hidden variables’, which cannot be observed directly through experimentation. Calculation of the values of the hidden variables is generally very difficult, if not impossible in some special cases. Identification of the ODE models is also quite difficult. RESULTS: We show that the simplified logistic Lotka–Volterra model, a well-established ODE model for biological and biochemical phenomena, can represent RNAi dynamics as a predator–prey system. Although a hidden variable exists in the model, its values can be determined and made visible as dynamic profiles of RNA-decomposing effects of siRNAs. Correlation analysis shows that the model parameters correlate highly with the total effect of the siRNA. CONCLUSIONS: The results suggest that analyses using our model are useful to estimate dynamic profiles of siRNA effects on apoptosis and to score siRNA by its effects on apoptosis, namely ‘phenotypic scoring’.
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spelling pubmed-36637152013-05-31 Estimate hidden dynamic profiles of siRNA effect on apoptosis Ueda, Takanori Tominaga, Daisuke Araki, Noriko Yoshikawa, Tomohiro BMC Bioinformatics Methodology Article BACKGROUND: For the representation of RNA interference (RNAi) dynamics, several mathematical models based on systems of ordinary differential equations (ODEs) have been proposed. These models consist of equations for each molecule that are involved in RNAi phenomena. Therefore, many real-value parameters must be optimized to identify the models. They also have many ‘hidden variables’, which cannot be observed directly through experimentation. Calculation of the values of the hidden variables is generally very difficult, if not impossible in some special cases. Identification of the ODE models is also quite difficult. RESULTS: We show that the simplified logistic Lotka–Volterra model, a well-established ODE model for biological and biochemical phenomena, can represent RNAi dynamics as a predator–prey system. Although a hidden variable exists in the model, its values can be determined and made visible as dynamic profiles of RNA-decomposing effects of siRNAs. Correlation analysis shows that the model parameters correlate highly with the total effect of the siRNA. CONCLUSIONS: The results suggest that analyses using our model are useful to estimate dynamic profiles of siRNA effects on apoptosis and to score siRNA by its effects on apoptosis, namely ‘phenotypic scoring’. BioMed Central 2013-03-15 /pmc/articles/PMC3663715/ /pubmed/23496896 http://dx.doi.org/10.1186/1471-2105-14-97 Text en Copyright © 2013 Ueda 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 cited.
spellingShingle Methodology Article
Ueda, Takanori
Tominaga, Daisuke
Araki, Noriko
Yoshikawa, Tomohiro
Estimate hidden dynamic profiles of siRNA effect on apoptosis
title Estimate hidden dynamic profiles of siRNA effect on apoptosis
title_full Estimate hidden dynamic profiles of siRNA effect on apoptosis
title_fullStr Estimate hidden dynamic profiles of siRNA effect on apoptosis
title_full_unstemmed Estimate hidden dynamic profiles of siRNA effect on apoptosis
title_short Estimate hidden dynamic profiles of siRNA effect on apoptosis
title_sort estimate hidden dynamic profiles of sirna effect on apoptosis
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3663715/
https://www.ncbi.nlm.nih.gov/pubmed/23496896
http://dx.doi.org/10.1186/1471-2105-14-97
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