Büscher et al. compared the three separation platforms that are most widely used in the analysis of intracellular metabolites: CE, GC, and LC, all in combination with a TOFMS detector [110]. The more limited coverage of GC is due to a bias in the detection of large polar molecules. This is caused by the derivatization that renders nonvolatile Inhibitors,research,lifescience,medical polar compounds amendable to gas-phase separation, but cannot be completed because of steric hindrance of the numerous silyl
groups that are necessary to modify all amino, carboxy and hydroxy groups in large molecules. According to their conclusions, for analyses on a single platform, LC provides the best combination of both versatility and robustness. If a second platform can be used, it is best complemented by GC. 5. Conclusions Metabolomics is a promising approach aimed at facilitating our understanding
of the dynamics of biological composition in living systems. Metabolites Inhibitors,research,lifescience,medical tend to be converted into highly polar compounds and are therefore difficult to separate. In this review, we discussed recent progress in the separation of biological samples. CE, GC, and HPLC are powerful tools for the separation of biological samples. Methods based on chromatographic separation coupled to MS seem optimal to meet these requirements. Inhibitors,research,lifescience,medical GC-MS needs laborious clean-up and often derivatization and it can only be applied for thermally stable compounds. CE-MS and LC-MS is a suitable alternative in many cases. These techniques will be useful to bioanalytical scientists. Acknowledgments This work was supported by a MEXT-Supported Program for the Strategic Research Foundation at Private Universities, Inhibitors,research,lifescience,medical 2008-2012. Conflict of Interest Conflict of Interest The authors declare no conflict of interest.
Genome-scale GS-1101 cell line metabolic models are essential to
bridge the gap between metabolic phenotypes and genome-derived biochemical information, as they provide a platform for the interpretation of experimental data related to metabolic states and enable the in silico Inhibitors,research,lifescience,medical experimentation of cell metabolism. The annotation and sequencing of genomes has made it possible to reconstruct genome-scale metabolic networks many for a growing number of organisms [1]. Using constraint-based methods and in silico simulation, the phenotypic functions of metabolic systems can be analysed under various environmental or physico-chemical conditions [2]. Applications of these computational methods to bacterial metabolic models have increased our understanding of bacterial evolution and metabolism [3]. Genome-scale models additionally allow for the integration of various types of high-throughput data. For example, the integration of regulatory interactions with metabolic networks has been successfully used to analyse phenotypes from gene-deletion studies and phenotypic arrays [4].