Literature DB >> 24708998

Quantitative metabolomics: a phantom?

Stephan Noack1, Wolfgang Wiechert2.   

Abstract

'Mass specs are precise but biology is not!' is a frequently heard argument when quantitative experimental data do not fit into the overall picture. The problem with this opinion is that the significance of measured biological data becomes a matter of gut feeling. Doubtlessly, the measurement precision of modern mass spectrometers is far better than the reproducibility of biological experiments. However, precisely for this reason, technical reproduction of mass spectrometric measurements neither characterizes the whole experiment from cell cultivation to producing biological data nor says anything about systematic errors in the overall measurement procedure. Taking quantitative metabolomics as a fruitful example, we deal with the question of why it is so difficult to say something precise about imprecision in biology.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Keywords:  intracellular metabolites; mass spectrometry; metabolomics; quantification

Mesh:

Year:  2014        PMID: 24708998     DOI: 10.1016/j.tibtech.2014.03.006

Source DB:  PubMed          Journal:  Trends Biotechnol        ISSN: 0167-7799            Impact factor:   19.536


  14 in total

1.  A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing.

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Journal:  J Vis Exp       Date:  2017-02-04       Impact factor: 1.355

2.  Metabolite Profiling and Stable Isotope Tracing in Sorted Subpopulations of Mammalian Cells.

Authors:  Irena Roci; Hector Gallart-Ayala; Angelika Schmidt; Jeramie Watrous; Mohit Jain; Craig E Wheelock; Roland Nilsson
Journal:  Anal Chem       Date:  2016-02-17       Impact factor: 6.986

3.  Extraction parameters for metabolomics from cultured cells.

Authors:  Zheng Ser; Xiaojing Liu; Ngoc Nu Tang; Jason W Locasale
Journal:  Anal Biochem       Date:  2015-01-19       Impact factor: 3.365

4.  Estimating relative changes of metabolic fluxes.

Authors:  Lei Huang; Dongsung Kim; Xiaojing Liu; Christopher R Myers; Jason W Locasale
Journal:  PLoS Comput Biol       Date:  2014-11-20       Impact factor: 4.475

5.  Concepts, challenges, and successes in modeling thermodynamics of metabolism.

Authors:  William R Cannon
Journal:  Front Bioeng Biotechnol       Date:  2014-11-26

6.  Bioprocess automation on a Mini Pilot Plant enables fast quantitative microbial phenotyping.

Authors:  Simon Unthan; Andreas Radek; Wolfgang Wiechert; Marco Oldiges; Stephan Noack
Journal:  Microb Cell Fact       Date:  2015-03-11       Impact factor: 5.328

Review 7.  Abiotic Stress Responses and Microbe-Mediated Mitigation in Plants: The Omics Strategies.

Authors:  Kamlesh K Meena; Ajay M Sorty; Utkarsh M Bitla; Khushboo Choudhary; Priyanka Gupta; Ashwani Pareek; Dhananjaya P Singh; Ratna Prabha; Pramod K Sahu; Vijai K Gupta; Harikesh B Singh; Kishor K Krishanani; Paramjit S Minhas
Journal:  Front Plant Sci       Date:  2017-02-09       Impact factor: 5.753

8.  The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis.

Authors:  Martin Beyß; Salah Azzouzi; Michael Weitzel; Wolfgang Wiechert; Katharina Nöh
Journal:  Front Microbiol       Date:  2019-05-24       Impact factor: 5.640

9.  Microfluidic Irreversible Electroporation-A Versatile Tool to Extract Intracellular Contents of Bacteria and Yeast.

Authors:  Alexander Rockenbach; Suresh Sudarsan; Judith Berens; Michael Kosubek; Jaroslav Lazar; Philipp Demling; René Hanke; Philip Mennicken; Birgitta E Ebert; Lars M Blank; Uwe Schnakenberg
Journal:  Metabolites       Date:  2019-09-30

10.  Microscale Quantitative Analysis of Polyhydroxybutyrate in Prokaryotes Using IDMS.

Authors:  Mariana Itzel Velasco Alvarez; Angela Ten Pierick; Patricia T N van Dam; Reza Maleki Seifar; Mark C M van Loosdrecht; S Aljoscha Wahl
Journal:  Metabolites       Date:  2017-05-17
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