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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Simplifying complexity in metabolomics.

Katharina Eick¹, Georg Pohnert¹

¹Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstraße 8, 07743 Jena, Germany.

Chemistry & Biology

|May 23, 2015

Summary

This summary is machine-generated.

This study uses a novel multiple stimuli approach and self-organizing map analysis to understand how Streptomyces coelicolor

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Last Updated: Apr 12, 2026

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Published on: May 27, 2014

2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

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Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

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Published on: July 11, 2014

Area of Science:

Microbiology
Metabolomics
Systems Biology

Background:

Metabolomics studies microbial secondary metabolite dynamics.
Large datasets and complex experiments pose challenges.
Understanding microbial responses to stimuli is crucial.

Purpose of the Study:

To elucidate variations in the Streptomyces coelicolor metabolome.
To investigate the effects of biotic and environmental perturbations.
To present a new analytical approach for complex metabolomics data.

Main Methods:

Utilized a multiple stimuli approach.
Employed self-organizing map (SOM)-based analysis.
Analyzed metabolomic data from Streptomyces coelicolor.

Main Results:

Successfully elucidated variations in the microbial metabolome.
Identified metabolic changes in response to diverse stimuli.
Demonstrated the utility of SOMs in handling complex metabolomic datasets.

Conclusions:

The multiple stimuli and SOM approach effectively reveals microbial metabolic dynamics.
This method aids in understanding Streptomyces coelicolor's response to perturbations.
Offers a powerful tool for complex biological data analysis.