The University of Groningen (RUG), founded in 1614, enjoys an international reputation as one of the oldest and leading research universities in Europe. The University provides high quality education to 32000 students, and with its 3500 researchers performs research in a broad range of disciplines, with a focus on the themes Energy, Healthy Ageing and Sustainable Society. The University of Groningen is among the global elite with a classification in the top 100 of the Shanghai ARWU (#66) and the THE World University Rankings (#79). One of the spearheads of the Faculty of Science and Engineering is the cluster of biologists, chemists and physicists who aim to generate fundamental understanding of natural processes and to exploit this knowledge for novel applications.
- Prof. Matthias Heinemann
RUG has pioneered thermodynamics-based analysis of metabolic networks. RUG has identified a new thermodynamic constraint that cells need to obey when operating their metabolism. RUG will develop a biochemically high accurate combined thermodynamic/ stoichiometric model of Arabidopsis and will use this model to make computational predictions of optimal metabolic flux distributions, to evaluate pathway variants and to analyse experimental data generated in the consortium.
Matthias Heinemann’s team (the Molecular Systems Biology group) is part of the Groningen Biomolecular Science and Biotechnology Institute. The aim of the group, currently hosting three postdocs, nine PhD students, two technicians and four Master students, is to generate fundamental, system-level understanding about carbon and energy metabolism across organisms and to gain insight in how metabolism controls other cellular processes. Thus, the teams core expertise is the functioning of metabolism. To tackle the challenges, the team employs a broad range of experimental tools, ranges from biochemical protein analysis, single-cell time-lapse microscopy up to proteome and 13C metabolic flux analysis. These experimental efforts are tightly combined with computational and modelling efforts, for instance, building and exploiting kinetic models, statistics-based methods for omics data analyses, and advanced methods for metabolic modelling with thermodynamic/stoichiometric models.