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

Quantification of Microbial Phenotypes

Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane 4072, Australia
Centre for Microbial Electrochemical Systems (CEMES), The University of Queensland, Brisbane 4072, Australia
Advanced Water Management Centre (AWMC), The University of Queensland, Brisbane 4072, Australia
Author to whom correspondence should be addressed.
Academic Editor: Peter Meikle
Metabolites 2016, 6(4), 45;
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)
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
Show Figures

Figure 1

MDPI and ACS Style

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

AMA Style

Martínez VS, Krömer JO. Quantification of Microbial Phenotypes. Metabolites. 2016; 6(4):45.

Chicago/Turabian Style

Martínez, Verónica S., and Jens O. Krömer 2016. "Quantification of Microbial Phenotypes" Metabolites 6, no. 4: 45.

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

Article Access Map by Country/Region

Back to TopTop