Bas Teusink obtained his PhD in biochemistry (1999) and is currently Professor in Systems Bioinformatics at the Amsterdam Institute for Molecules, Medicines and Systems, VU University Amsterdam. His group is interested in the understanding and subsequent exploitation of regulation of biological networks, metabolic networks in particular. He combines quantitative experimentation with modeling of metabolism at the molecular, genome and cellular scale.
Use of metabolomics in systems biology: from data to understanding
(Dai-ichi Tsuru, Jun 24th, 09:30)
Metabolomics data are used in the study of metabolic networks through largely two approaches, known as top-down and bottom-up systems biology. In the top-down approach, comprehensive data sets are generated to infer network or regulatory structures and to identify biomarkers. It uses inductive reasoning with statistics as its main computational method. In bottom-up systems biology, metabolomics data are used to develop and validate mechanistic models of metabolism. I will illustrate the use of metabolomics in systems biology approach to metabolism, through two studies. In the first study, we used genome-scale metabolic models to develop a better understanding of the metabolic potential of a human pathogen. Model-directed comprehensive metabolomics analysis of fermentation broth showed the excretion of metabolites suspected to be involved in host-pathogen interactions. Applying network-based medium optimisation algorithms led to a substantial simplification of growth media with over twofold higher productivity. In the second study, a detailed kinetic model of glycolysis in yeast pointed to the co-existence of two metabolic states, one functional state and a state in which the reactions in glycolysis are unbalanced and intermediates accumulate. The co-existence of two metabolic states was reflected in two metabolic subpopulations within a clonal population. Dynamic fluxes within the first five minutes after glucose addition –estimated from time-dependent metabolomics data – determine which state is reached by individual cells (Van Heerden, Science 2014). This study shows the individuality of cells within a population, and the need for single-cell metabolomics technologies to understand the biology of cellular life.
Centre for Integrative Bioinformatics, Vrije Universiteit Amsterdam (IBIVU), Netherlands Institute for Systems Biology (NISB), University of Amsterdam, Kluyver Centre for Genomics of Industrial Fermentation, Netherlands