Next Article in Journal
Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems
Next Article in Special Issue
Extracellular Microbial Metabolomics: The State of the Art
Previous Article in Journal
The Metabolic Implications of Glucocorticoids in a High-Fat Diet Setting and the Counter-Effects of Exercise
Previous Article in Special Issue
Quantitative Metabolomics and Instationary 13C-Metabolic Flux Analysis Reveals Impact of Recombinant Protein Production on Trehalose and Energy Metabolism in Pichia pastoris
Article Menu

Export Article

Open AccessReview
Metabolites 2016, 6(4), 45; doi:10.3390/metabo6040045

Quantification of Microbial Phenotypes

1
Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane 4072, Australia
2
Centre for Microbial Electrochemical Systems (CEMES), The University of Queensland, Brisbane 4072, Australia
3
Advanced Water Management Centre (AWMC), The University of Queensland, Brisbane 4072, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Peter Meikle
Received: 31 October 2016 / Revised: 5 December 2016 / Accepted: 6 December 2016 / Published: 9 December 2016
(This article belongs to the Special Issue Microbial Metabolomics Volume 2)
View Full-Text   |   Download PDF [1476 KB, uploaded 9 December 2016]   |  

Abstract

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis. View Full-Text
Keywords: metabolomics; 13C fluxomics; thermodynamics-based network analysis metabolomics; 13C fluxomics; thermodynamics-based network analysis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Martínez, V.S.; Krömer, J.O. Quantification of Microbial Phenotypes. Metabolites 2016, 6, 45.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Metabolites EISSN 2218-1989 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top