Metabolomics has become a central element of systems biology research. It ultimately aims at detecting, identifying, quantifying and interpreting the occurrence and the abundance of metabolites in context of organisms’ biology. For this, various highly sophisticated experimental and theoretical approaches have been developed. Integrating different analytical techniques, for example LC-MS, GC-MS, CE-MS or H-NMR, has proven to yield a comprehensive - although not yet complete - overview of organisms’ metabolomes. Thousands of metabolic features have become experimentally accessible. Finally, this results in numerous multidimensional data arrays which need to be functionally interpreted.
During the last two decades, computer-assisted data analysis has become a cornerstone of metabolomics research. This is due to the need of algorithms, i.e. automatized and time-efficient procedures, for data handling and evaluation, structural elucidation, deconvolution steps, labelling strategies, metabolic flux analysis, network analysis, statistics, and data base search. Ultimately, algorithmic data evaluation aims at the exploitation of large computational calculation capacities. Irrespective of the area of research - human diseases, nutrition and food quality, plant and microbial biology, research in evolution of metabolic networks, developmental biology and ecology – theoretical concepts and computational approaches are needed to derive any kind of information from current metabolomics approaches.
Our Research Topic intends to bring together current and interdisciplinary research, reviews and opinions being related to any field of theoretical and computer-assisted metabolomic research approaches. We cordially encourage authors from fields of biology, chemistry, informatics, physics, and others, to submit their manuscripts related to this topic for publication within this Research Topic.
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