Theoretical Studies of Intracellular Concentration of Micro-organisms’ Metabolites

Abstract With the rapid growth of micro-organism metabolic networks, acquiring the intracellular concentration of microorganisms’ metabolites accurately in large-batch is critical to the development of metabolic engineering and synthetic biology. Complementary to the experimental methods, computatio...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hai-Feng Yang, Xiao-Nan Zhang, Yan Li, Yong-Hong Zhang, Qin Xu, Dong-Qing Wei
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fa0f6a8a5d794861a872c73459a8a880
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fa0f6a8a5d794861a872c73459a8a880
record_format	dspace
spelling	oai:doaj.org-article:fa0f6a8a5d794861a872c73459a8a8802021-12-02T12:30:12ZTheoretical Studies of Intracellular Concentration of Micro-organisms’ Metabolites10.1038/s41598-017-08793-22045-2322https://doaj.org/article/fa0f6a8a5d794861a872c73459a8a8802017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08793-2https://doaj.org/toc/2045-2322Abstract With the rapid growth of micro-organism metabolic networks, acquiring the intracellular concentration of microorganisms’ metabolites accurately in large-batch is critical to the development of metabolic engineering and synthetic biology. Complementary to the experimental methods, computational methods were used as effective assessing tools for the studies of intracellular concentrations of metabolites. In this study, the dataset of 130 metabolites from E. coli and S. cerevisiae with available experimental concentrations were utilized to develop a SVM model of the negative logarithm of the concentration (-logC). In this statistic model, in addition to common descriptors of molecular properties, two special types of descriptors including metabolic network topologic descriptors and metabolic pathway descriptors were included. All 1997 descriptors were finally reduced into 14 by variable selections including genetic algorithm (GA). The model was evaluated through internal validations by 10-fold and leave-one-out (LOO) cross-validation, as well as external validations by predicting -logC values of the test set. The developed SVM model is robust and has a strong predictive potential (n = 91, m = 14, R2 = 0.744, RMSE = 0.730, Q2 = 0.57; R2 p = 0.59, RMSEp = 0.702, Q2 p = 0.58). An effective tool could be provided by this analysis for the large-batch prediction of the intracellular concentrations of the micro-organisms’ metabolites.Hai-Feng YangXiao-Nan ZhangYan LiYong-Hong ZhangQin XuDong-Qing WeiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hai-Feng Yang Xiao-Nan Zhang Yan Li Yong-Hong Zhang Qin Xu Dong-Qing Wei Theoretical Studies of Intracellular Concentration of Micro-organisms’ Metabolites
description	Abstract With the rapid growth of micro-organism metabolic networks, acquiring the intracellular concentration of microorganisms’ metabolites accurately in large-batch is critical to the development of metabolic engineering and synthetic biology. Complementary to the experimental methods, computational methods were used as effective assessing tools for the studies of intracellular concentrations of metabolites. In this study, the dataset of 130 metabolites from E. coli and S. cerevisiae with available experimental concentrations were utilized to develop a SVM model of the negative logarithm of the concentration (-logC). In this statistic model, in addition to common descriptors of molecular properties, two special types of descriptors including metabolic network topologic descriptors and metabolic pathway descriptors were included. All 1997 descriptors were finally reduced into 14 by variable selections including genetic algorithm (GA). The model was evaluated through internal validations by 10-fold and leave-one-out (LOO) cross-validation, as well as external validations by predicting -logC values of the test set. The developed SVM model is robust and has a strong predictive potential (n = 91, m = 14, R2 = 0.744, RMSE = 0.730, Q2 = 0.57; R2 p = 0.59, RMSEp = 0.702, Q2 p = 0.58). An effective tool could be provided by this analysis for the large-batch prediction of the intracellular concentrations of the micro-organisms’ metabolites.
format	article
author	Hai-Feng Yang Xiao-Nan Zhang Yan Li Yong-Hong Zhang Qin Xu Dong-Qing Wei
author_facet	Hai-Feng Yang Xiao-Nan Zhang Yan Li Yong-Hong Zhang Qin Xu Dong-Qing Wei
author_sort	Hai-Feng Yang
title	Theoretical Studies of Intracellular Concentration of Micro-organisms’ Metabolites
title_short	Theoretical Studies of Intracellular Concentration of Micro-organisms’ Metabolites
title_full	Theoretical Studies of Intracellular Concentration of Micro-organisms’ Metabolites
title_fullStr	Theoretical Studies of Intracellular Concentration of Micro-organisms’ Metabolites
title_full_unstemmed	Theoretical Studies of Intracellular Concentration of Micro-organisms’ Metabolites
title_sort	theoretical studies of intracellular concentration of micro-organisms’ metabolites
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/fa0f6a8a5d794861a872c73459a8a880
work_keys_str_mv	AT haifengyang theoreticalstudiesofintracellularconcentrationofmicroorganismsmetabolites AT xiaonanzhang theoreticalstudiesofintracellularconcentrationofmicroorganismsmetabolites AT yanli theoreticalstudiesofintracellularconcentrationofmicroorganismsmetabolites AT yonghongzhang theoreticalstudiesofintracellularconcentrationofmicroorganismsmetabolites AT qinxu theoreticalstudiesofintracellularconcentrationofmicroorganismsmetabolites AT dongqingwei theoreticalstudiesofintracellularconcentrationofmicroorganismsmetabolites
_version_	1718394433068597248

Theoretical Studies of Intracellular Concentration of Micro-organisms’ Metabolites

Ejemplares similares