Identification of a metabolic reaction network from time-series data of metabolite concentrations.

Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among th...

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Autores principales:	Kansuporn Sriyudthsak, Fumihide Shiraishi, Masami Yokota Hirai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ad8a4f9bc0444178a31030dc9111de05
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spelling	oai:doaj.org-article:ad8a4f9bc0444178a31030dc9111de052021-11-18T08:02:04ZIdentification of a metabolic reaction network from time-series data of metabolite concentrations.1932-620310.1371/journal.pone.0051212https://doaj.org/article/ad8a4f9bc0444178a31030dc9111de052013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23326311/?tool=EBIhttps://doaj.org/toc/1932-6203Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the metabolic profiles. However, these analyses cannot simultaneously carry out identification of metabolic reaction networks and prediction of dynamic behaviour of metabolites in the networks. The present study, therefore, proposes a new approach consisting of a combination of statistical technique and mathematical modelling approach to identify and predict a probable metabolic reaction network from time-series data of metabolite concentrations and simultaneously construct its mathematical model. Firstly, regression functions are fitted to experimental data by the locally estimated scatter plot smoothing method. Secondly, the fitted result is analysed by the bivariate Granger causality test to determine which metabolites cause the change in other metabolite concentrations and remove less related metabolites. Thirdly, S-system equations are formed by using the remaining metabolites within the framework of biochemical systems theory. Finally, parameters including rate constants and kinetic orders are estimated by the Levenberg-Marquardt algorithm. The estimation is iterated by setting insignificant kinetic orders at zero, i.e., removing insignificant metabolites. Consequently, a reaction network structure is identified and its mathematical model is obtained. Our approach is validated using a generic inhibition and activation model and its practical application is tested using a simplified model of the glycolysis of Lactococcus lactis MG1363, for which actual time-series data of metabolite concentrations are available. The results indicate the usefulness of our approach and suggest a probable pathway for the production of lactate and acetate. The results also indicate that the approach pinpoints a probable strong inhibition of lactate on the glycolysis pathway.Kansuporn SriyudthsakFumihide ShiraishiMasami Yokota HiraiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 1, p e51212 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kansuporn Sriyudthsak Fumihide Shiraishi Masami Yokota Hirai Identification of a metabolic reaction network from time-series data of metabolite concentrations.
description	Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the metabolic profiles. However, these analyses cannot simultaneously carry out identification of metabolic reaction networks and prediction of dynamic behaviour of metabolites in the networks. The present study, therefore, proposes a new approach consisting of a combination of statistical technique and mathematical modelling approach to identify and predict a probable metabolic reaction network from time-series data of metabolite concentrations and simultaneously construct its mathematical model. Firstly, regression functions are fitted to experimental data by the locally estimated scatter plot smoothing method. Secondly, the fitted result is analysed by the bivariate Granger causality test to determine which metabolites cause the change in other metabolite concentrations and remove less related metabolites. Thirdly, S-system equations are formed by using the remaining metabolites within the framework of biochemical systems theory. Finally, parameters including rate constants and kinetic orders are estimated by the Levenberg-Marquardt algorithm. The estimation is iterated by setting insignificant kinetic orders at zero, i.e., removing insignificant metabolites. Consequently, a reaction network structure is identified and its mathematical model is obtained. Our approach is validated using a generic inhibition and activation model and its practical application is tested using a simplified model of the glycolysis of Lactococcus lactis MG1363, for which actual time-series data of metabolite concentrations are available. The results indicate the usefulness of our approach and suggest a probable pathway for the production of lactate and acetate. The results also indicate that the approach pinpoints a probable strong inhibition of lactate on the glycolysis pathway.
format	article
author	Kansuporn Sriyudthsak Fumihide Shiraishi Masami Yokota Hirai
author_facet	Kansuporn Sriyudthsak Fumihide Shiraishi Masami Yokota Hirai
author_sort	Kansuporn Sriyudthsak
title	Identification of a metabolic reaction network from time-series data of metabolite concentrations.
title_short	Identification of a metabolic reaction network from time-series data of metabolite concentrations.
title_full	Identification of a metabolic reaction network from time-series data of metabolite concentrations.
title_fullStr	Identification of a metabolic reaction network from time-series data of metabolite concentrations.
title_full_unstemmed	Identification of a metabolic reaction network from time-series data of metabolite concentrations.
title_sort	identification of a metabolic reaction network from time-series data of metabolite concentrations.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/ad8a4f9bc0444178a31030dc9111de05
work_keys_str_mv	AT kansupornsriyudthsak identificationofametabolicreactionnetworkfromtimeseriesdataofmetaboliteconcentrations AT fumihideshiraishi identificationofametabolicreactionnetworkfromtimeseriesdataofmetaboliteconcentrations AT masamiyokotahirai identificationofametabolicreactionnetworkfromtimeseriesdataofmetaboliteconcentrations
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Identification of a metabolic reaction network from time-series data of metabolite concentrations.

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