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A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network

BACKGROUND: Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biolo...

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Autores principales: Sriyudthsak, Kansuporn, Sawada, Yuji, Chiba, Yukako, Yamashita, Yui, Kanaya, Shigehiko, Onouchi, Hitoshi, Fujiwara, Toru, Naito, Satoshi, Voit, Ebernard O, Shiraishi, Fumihide, Hirai, Masami Yokota
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305983/
https://www.ncbi.nlm.nih.gov/pubmed/25559748
http://dx.doi.org/10.1186/1752-0509-8-S5-S4
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author Sriyudthsak, Kansuporn
Sawada, Yuji
Chiba, Yukako
Yamashita, Yui
Kanaya, Shigehiko
Onouchi, Hitoshi
Fujiwara, Toru
Naito, Satoshi
Voit, Ebernard O
Shiraishi, Fumihide
Hirai, Masami Yokota
author_facet Sriyudthsak, Kansuporn
Sawada, Yuji
Chiba, Yukako
Yamashita, Yui
Kanaya, Shigehiko
Onouchi, Hitoshi
Fujiwara, Toru
Naito, Satoshi
Voit, Ebernard O
Shiraishi, Fumihide
Hirai, Masami Yokota
author_sort Sriyudthsak, Kansuporn
collection PubMed
description BACKGROUND: Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biological fluctuations. The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons. RESULTS: We show here a streamlined approach for the construction of a coarse model that allows us to set up dynamic models with minimal input information. The approach uses a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of biochemical systems theory (BST) with rate constants of 1, normal kinetic orders of 1, and -0.5 and 0.5 for inhibitory and activating effects, named Unity (U)-system. The U-system model does not necessarily fit all data well but is often sufficient for predicting metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme. The U-system approach was validated with small, generic systems and implemented to model a large-scale metabolic reaction network of a higher plant, Arabidopsis. The dynamic behaviors obtained by predictive simulations agreed with actually available metabolomic time-series data, identified probable errors in the experimental datasets, and estimated probable behavior of unmeasurable metabolites in a qualitative manner. The model could also predict metabolic responses of Arabidopsis with altered network structures due to genetic modification. CONCLUSIONS: The U-system approach can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network. Thus, it can be a useful first-line tool of data analysis, model diagnostics and aid the design of next-step experiments.
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spelling pubmed-43059832015-02-12 A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network Sriyudthsak, Kansuporn Sawada, Yuji Chiba, Yukako Yamashita, Yui Kanaya, Shigehiko Onouchi, Hitoshi Fujiwara, Toru Naito, Satoshi Voit, Ebernard O Shiraishi, Fumihide Hirai, Masami Yokota BMC Syst Biol Research BACKGROUND: Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biological fluctuations. The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons. RESULTS: We show here a streamlined approach for the construction of a coarse model that allows us to set up dynamic models with minimal input information. The approach uses a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of biochemical systems theory (BST) with rate constants of 1, normal kinetic orders of 1, and -0.5 and 0.5 for inhibitory and activating effects, named Unity (U)-system. The U-system model does not necessarily fit all data well but is often sufficient for predicting metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme. The U-system approach was validated with small, generic systems and implemented to model a large-scale metabolic reaction network of a higher plant, Arabidopsis. The dynamic behaviors obtained by predictive simulations agreed with actually available metabolomic time-series data, identified probable errors in the experimental datasets, and estimated probable behavior of unmeasurable metabolites in a qualitative manner. The model could also predict metabolic responses of Arabidopsis with altered network structures due to genetic modification. CONCLUSIONS: The U-system approach can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network. Thus, it can be a useful first-line tool of data analysis, model diagnostics and aid the design of next-step experiments. BioMed Central 2014-12-12 /pmc/articles/PMC4305983/ /pubmed/25559748 http://dx.doi.org/10.1186/1752-0509-8-S5-S4 Text en Copyright © 2014 Sriyudthsak et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Sriyudthsak, Kansuporn
Sawada, Yuji
Chiba, Yukako
Yamashita, Yui
Kanaya, Shigehiko
Onouchi, Hitoshi
Fujiwara, Toru
Naito, Satoshi
Voit, Ebernard O
Shiraishi, Fumihide
Hirai, Masami Yokota
A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network
title A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network
title_full A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network
title_fullStr A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network
title_full_unstemmed A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network
title_short A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network
title_sort u-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305983/
https://www.ncbi.nlm.nih.gov/pubmed/25559748
http://dx.doi.org/10.1186/1752-0509-8-S5-S4
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