Error Propagation Analysis for Quantitative Intracellular Metabolomics

Model-based analyses have become an integral part of modern metabolic engineering and systems biology in order to gain knowledge about complex and not directly observable cellular processes. For quantitative analyses, not only experimental data, but also measurement errors, play a crucial role. The...

Full description

Bibliographic Details
Main Authors: Jana Tillack, Nicole Paczia, Katharina Nöh, Wolfgang Wiechert, Stephan Noack
Format: Article
Language:English
Published: MDPI AG 2012-11-01
Series:Metabolites
Subjects:
Online Access:http://www.mdpi.com/2218-1989/2/4/1012
id doaj-6095e7c0bd6541498cb9594d9861a10c
record_format Article
spelling doaj-6095e7c0bd6541498cb9594d9861a10c2020-11-24T20:54:26ZengMDPI AGMetabolites2218-19892012-11-01241012103010.3390/metabo2041012Error Propagation Analysis for Quantitative Intracellular MetabolomicsJana TillackNicole PacziaKatharina NöhWolfgang WiechertStephan NoackModel-based analyses have become an integral part of modern metabolic engineering and systems biology in order to gain knowledge about complex and not directly observable cellular processes. For quantitative analyses, not only experimental data, but also measurement errors, play a crucial role. The total measurement error of any analytical protocol is the result of an accumulation of single errors introduced by several processing steps. Here, we present a framework for the quantification of intracellular metabolites, including error propagation during metabolome sample processing. Focusing on one specific protocol, we comprehensively investigate all currently known and accessible factors that ultimately impact the accuracy of intracellular metabolite concentration data. All intermediate steps are modeled, and their uncertainty with respect to the final concentration data is rigorously quantified. Finally, on the basis of a comprehensive metabolome dataset of Corynebacterium glutamicum, an integrated error propagation analysis for all parts of the model is conducted, and the most critical steps for intracellular metabolite quantification are detected.http://www.mdpi.com/2218-1989/2/4/1012metabolomicsquantificationintracellular metabolitesquenchingmetabolite leakageLC-MS/MSerror propagation analysisCorynebacterium glutamicum
collection DOAJ
language English
format Article
sources DOAJ
author Jana Tillack
Nicole Paczia
Katharina Nöh
Wolfgang Wiechert
Stephan Noack
spellingShingle Jana Tillack
Nicole Paczia
Katharina Nöh
Wolfgang Wiechert
Stephan Noack
Error Propagation Analysis for Quantitative Intracellular Metabolomics
Metabolites
metabolomics
quantification
intracellular metabolites
quenching
metabolite leakage
LC-MS/MS
error propagation analysis
Corynebacterium glutamicum
author_facet Jana Tillack
Nicole Paczia
Katharina Nöh
Wolfgang Wiechert
Stephan Noack
author_sort Jana Tillack
title Error Propagation Analysis for Quantitative Intracellular Metabolomics
title_short Error Propagation Analysis for Quantitative Intracellular Metabolomics
title_full Error Propagation Analysis for Quantitative Intracellular Metabolomics
title_fullStr Error Propagation Analysis for Quantitative Intracellular Metabolomics
title_full_unstemmed Error Propagation Analysis for Quantitative Intracellular Metabolomics
title_sort error propagation analysis for quantitative intracellular metabolomics
publisher MDPI AG
series Metabolites
issn 2218-1989
publishDate 2012-11-01
description Model-based analyses have become an integral part of modern metabolic engineering and systems biology in order to gain knowledge about complex and not directly observable cellular processes. For quantitative analyses, not only experimental data, but also measurement errors, play a crucial role. The total measurement error of any analytical protocol is the result of an accumulation of single errors introduced by several processing steps. Here, we present a framework for the quantification of intracellular metabolites, including error propagation during metabolome sample processing. Focusing on one specific protocol, we comprehensively investigate all currently known and accessible factors that ultimately impact the accuracy of intracellular metabolite concentration data. All intermediate steps are modeled, and their uncertainty with respect to the final concentration data is rigorously quantified. Finally, on the basis of a comprehensive metabolome dataset of Corynebacterium glutamicum, an integrated error propagation analysis for all parts of the model is conducted, and the most critical steps for intracellular metabolite quantification are detected.
topic metabolomics
quantification
intracellular metabolites
quenching
metabolite leakage
LC-MS/MS
error propagation analysis
Corynebacterium glutamicum
url http://www.mdpi.com/2218-1989/2/4/1012
work_keys_str_mv AT janatillack errorpropagationanalysisforquantitativeintracellularmetabolomics
AT nicolepaczia errorpropagationanalysisforquantitativeintracellularmetabolomics
AT katharinanoh errorpropagationanalysisforquantitativeintracellularmetabolomics
AT wolfgangwiechert errorpropagationanalysisforquantitativeintracellularmetabolomics
AT stephannoack errorpropagationanalysisforquantitativeintracellularmetabolomics
_version_ 1716794500080205824