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Updated: Apr 27, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Error propagation analysis for quantitative intracellular metabolomics.

Jana Tillack¹, Nicole Paczia², Katharina Nöh³

¹Institute of Bio-and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany. j.tillack@fz-juelich.de.

|June 25, 2014

Summary

This summary is machine-generated.

This study introduces a framework to quantify intracellular metabolites and their measurement errors during sample processing. It identifies critical steps impacting accuracy in metabolic engineering and systems biology.

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Area of Science:

Metabolic Engineering
Systems Biology
Analytical Chemistry

Background:

Model-based analyses are crucial for understanding complex cellular processes in metabolic engineering and systems biology.
Accurate quantitative analysis relies heavily on experimental data and understanding measurement errors.
Total measurement error stems from cumulative errors across multiple sample processing steps.

Purpose of the Study:

To present a framework for quantifying intracellular metabolites, incorporating error propagation during metabolome sample processing.
To investigate factors impacting the accuracy of intracellular metabolite concentration data within a specific protocol.
To identify critical steps in intracellular metabolite quantification.

Main Methods:

Developing a framework for error propagation analysis in metabolomics.
Modeling intermediate steps in sample processing and quantifying their uncertainty.
Applying integrated error propagation analysis to a Corynebacterium glutamicum metabolome dataset.

Main Results:

A comprehensive framework for quantifying intracellular metabolites with error propagation was established.
All known factors influencing intracellular metabolite concentration accuracy were investigated.
The most critical steps affecting intracellular metabolite quantification accuracy were identified.

Conclusions:

The developed framework enables rigorous quantification of intracellular metabolites and their associated errors.
Understanding and controlling error propagation is essential for accurate metabolome data.
This work provides insights into optimizing protocols for precise metabolite measurements in microbial systems.