Biology Reference
In-Depth Information
Absolute-quantitative data are a prerequisite for all thermodynamic analysis
and for estimations of
in vivo
kinetic parameters such as binding and dissociations
constants. The thermodynamic driving force constrains the direction of metabolic
flux and is a function of absolute metabolite concentrations. Thermodynamic
analysis provides a feasibility and consistency check of the data for metabolic
fluxes and metabolite concentrations and reveals sites of active metabolite
regulation (
K¨mmel
et al.
,2006
). Further, absolute metabolite concentrations
facilitate the identification of
in vivo
kinetic properties of catalytic enzymes,
by dynamic models that sum up kinetic rate laws in differential equations.
A mechanistic kinetic model was used to infer
in vivo
kinetic parameters of
central carbon metabolism of
E. coli
(
Chassagnole
et al.
, 2002
). Other models
could additionally describe the function of metabolic modules, such as those used
for the kinetic models of sphingolipid metabolism in yeast (
Alvarez-Vasquez
et al.
, 2005
) and ammonia assimilation in
E. coli
(
Yuan
et al.
, 2009
). The scope
of future studies is to dissect the different levels of metabolite regulation that can
occur as a result of the occupation of the active site (
Bennett
et al.
, 2009; Fendt
et al.
, 2010
) or of various allosteric metabolite-protein interactions (
Gerosa and
Sauer, 2011
).
7
OUTLOOK
In the upcoming years, we anticipate that an increasing number of laboratories
will engage in metabolomics. The commercial availability of sampling devices,
decreasing prices of analytical devices of sufficient sensitivity and availability of
easy-to-use software solutions will further facilitate this process. To assist existing
newcomers to the field, in this compilation of the current state-of-the-art methods,
we have focused on completeness and on the practical aspects for the various stages
of a metabolomics experiment.
The amount of metabolomics data will increase not only in the number of
experiments performed but also in the amount of data generated per experiment.
Improvements in sensitivity of mass spectrometers will provide access to metabolites
that are below the current limit of quantification. Furthermore, ultrahigh-throughput
analysis dramatically increases the number of samples that can be measured (
Fuhrer
et al.
, 2011
). First steps have already been taken to add another dimension of
complexity by quantifying metabolites with single-cell resolution (
Amantonico
et al.
, 2010; Heinemann and Zenobi, 2011
).
The increasing amount and depth of metabolomics data will intensify the need for
standardized data handling and centralized data storage, similar to what is already
available for genome sequences and transcriptomics. The combined study of multi-
ple levels of large-scale data such as metabolomics, proteomics and transcriptomics
then enables biological insights that are not be accessible through the study of any
one of these levels alone (
Fendt
et al.
, 2010; Buescher
et al.
, 2012
).
