Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data
Recent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been exp...
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doaj-05ba24ae375d49e6ac0ad25e22b267032020-11-24T23:29:43ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2017-12-01810.3389/fpls.2017.02152312638Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics DataKevin Schwahn0Kevin Schwahn1Romina Beleggia2Nooshin Omranian3Nooshin Omranian4Nooshin Omranian5Zoran Nikoloski6Zoran Nikoloski7Zoran Nikoloski8Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, GermanyBioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, GermanyConsiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria, Centro di Ricerca per la Cerealicoltura e le Colture Industriali (CREA-CI), Foggia, ItalySystems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, GermanyBioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, GermanyCenter of Plant Systems Biology and Biotechnology, Plovdiv, BulgariaSystems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, GermanyBioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, GermanyCenter of Plant Systems Biology and Biotechnology, Plovdiv, BulgariaRecent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been explored with regression-based approaches that only capture linear relationships, rendering it difficult to determine the extent to which the data reflect the underlying reaction rates and their couplings. Here we propose an approach termed Stoichiometric Correlation Analysis (SCA) based on correlation between positive linear combinations of log-transformed metabolic profiles. The log-transformation is due to the evidence that metabolic networks can be modeled by mass action law and kinetics derived from it. Unlike the existing approaches which establish a relation between pairs of metabolites, SCA facilitates the discovery of higher-order dependence between more than two metabolites. By using a paradigmatic model of the tricarboxylic acid cycle we show that the higher-order dependence reflects the coupling of concentration of reactant complexes, capturing the subtle difference between the employed enzyme kinetics. Using time-resolved metabolic profiles from Arabidopsis thaliana and Escherichia coli, we show that SCA can be used to quantify the difference in coupling of reactant complexes, and hence, reaction rates, underlying the stringent response in these model organisms. By using SCA with data from natural variation of wild and domesticated wheat and tomato accession, we demonstrate that the domestication is accompanied by loss of such couplings, in these species. Therefore, application of SCA to metabolomics data from natural variation in wild and domesticated populations provides a mechanistic way to understanding domestication and its relation to metabolic networks.http://journal.frontiersin.org/article/10.3389/fpls.2017.02152/fullmetabolismsystems biologymaximal correlationcorrelation analysisdomestication |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kevin Schwahn Kevin Schwahn Romina Beleggia Nooshin Omranian Nooshin Omranian Nooshin Omranian Zoran Nikoloski Zoran Nikoloski Zoran Nikoloski |
spellingShingle |
Kevin Schwahn Kevin Schwahn Romina Beleggia Nooshin Omranian Nooshin Omranian Nooshin Omranian Zoran Nikoloski Zoran Nikoloski Zoran Nikoloski Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data Frontiers in Plant Science metabolism systems biology maximal correlation correlation analysis domestication |
author_facet |
Kevin Schwahn Kevin Schwahn Romina Beleggia Nooshin Omranian Nooshin Omranian Nooshin Omranian Zoran Nikoloski Zoran Nikoloski Zoran Nikoloski |
author_sort |
Kevin Schwahn |
title |
Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data |
title_short |
Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data |
title_full |
Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data |
title_fullStr |
Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data |
title_full_unstemmed |
Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data |
title_sort |
stoichiometric correlation analysis: principles of metabolic functionality from metabolomics data |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Plant Science |
issn |
1664-462X |
publishDate |
2017-12-01 |
description |
Recent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been explored with regression-based approaches that only capture linear relationships, rendering it difficult to determine the extent to which the data reflect the underlying reaction rates and their couplings. Here we propose an approach termed Stoichiometric Correlation Analysis (SCA) based on correlation between positive linear combinations of log-transformed metabolic profiles. The log-transformation is due to the evidence that metabolic networks can be modeled by mass action law and kinetics derived from it. Unlike the existing approaches which establish a relation between pairs of metabolites, SCA facilitates the discovery of higher-order dependence between more than two metabolites. By using a paradigmatic model of the tricarboxylic acid cycle we show that the higher-order dependence reflects the coupling of concentration of reactant complexes, capturing the subtle difference between the employed enzyme kinetics. Using time-resolved metabolic profiles from Arabidopsis thaliana and Escherichia coli, we show that SCA can be used to quantify the difference in coupling of reactant complexes, and hence, reaction rates, underlying the stringent response in these model organisms. By using SCA with data from natural variation of wild and domesticated wheat and tomato accession, we demonstrate that the domestication is accompanied by loss of such couplings, in these species. Therefore, application of SCA to metabolomics data from natural variation in wild and domesticated populations provides a mechanistic way to understanding domestication and its relation to metabolic networks. |
topic |
metabolism systems biology maximal correlation correlation analysis domestication |
url |
http://journal.frontiersin.org/article/10.3389/fpls.2017.02152/full |
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