Special Issue "Lipidomics"
A special issue of Metabolites (ISSN 2218-1989).
Deadline for manuscript submissions: closed (25 December 2011)
Prof. Dr. Dietrich A. Volmer (Website)
Institute for Bioanalytical Chemistry, Department of Chemistry, Saarland University, 66123 Saarbrücken, Germany
Phone: +49 681 302 3433
Interests: metabolomics; lipidomics; proteomics; analytical method development; structure elucidation; quantitative mass spectrometry; ionization mechanisms and gas-phase ion chemistry
Lipids play essential roles in biological systems and many diseases involve changes in lipid metabolism and perturbed lipid pathways. Even though lipid analysis is widely applied in the biological sciences, lipidomics, the detailed and comprehensive analysis of lipids in a cell or tissue, is just at the beginning of its evolution. The chemical complexity of lipids, the difficulties of studying lipids in their biological framework, and the requirement to relate identity and modulation of bioactive lipids with function of genes and proteins require sophisticated high resolution analytical tools for their structural and quantitative analysis. Importantly, lipidomics techniques are increasingly used in studies of biological function, and for diagnostic and prognostic purposes (biomarkers). A considerable amount of research effort goes into data crunching and data mining of lipidomics datasets, which is a key to successful comprehensive exploration of lipidomics data as a function of treatment, genotype or phenotype.
Therefore, this special issue of Metabolites will be dedicated to publishing current advances on bioanalytical techniques, discovery and characterization of novel lipid biomarkers, functional lipidomics, clinical applications of lipidomics, and biocomputational approaches to data-mining and databases of lipidomics datasets.
Prof. Dr. Dietrich Volmer
- lipid metabolism
- lipid profiling
- lipid fingerprints
- global profiling of lipids
- metabolic phenotype
- lipid biomarkers
- mass spectrometry
- untargeted lipidomics
- targeted lipidomics
- data mining
- clinical lipidomics