Description of organisation
The Medical Systems Biology research at the UMCG is embedded in the Pediatrics department as well as in the Systems Biology Center for Systems Biology and Ageing. The goal of our research programme is to develop validated and predictive dynamic computer models of energy metabolism and its regulation to understand and target inborn and acquired metabolic diseases, as well as age-related pathology which is often caused by underlying metabolic aberrations. We aim at Personalized Medicine at different levels. In clinically relevant research we focus on the mechanisms underlying the different disease outcomes between individuals carrying the same genetic mutation and aim at a model-assisted, personalized treatment. In collaboration with the 30-years follow-up cohort study LifeLines we aim at identification of biomarkers that confer risks of metabolic derangements and at a computer-model driven, personalized diet and lifestyle advise.
The strength of the UMCG is the presence of the complete spectrum of systems biology, (bio) informatics, statistics, data generation and biobanking. The research encompasses patients and healthy cohorts as well as fundamental biology on a broad range of model organisms.
Previous experience
Barbara Bakker is a professor in Medical Systems Biology and member of the management team of the Systems Biology Centre for Energy Metabolism and Ageing. Her research into network based drug design led to (1) identification of drug targets that confer selective vulnarability of parasites as opposed to their host; (2) understanding of emergent behaviour of metabolic networks; (3) understanding of the interplay between metabolic pathways and gene-expression regulation. The group works on complex metabolic networks in mammalian and parasitic diseases. Methodologies include metabolomics, fluxomics, proteomics, dynamic modelling, metabolic control analysis, biology of animals, tissues and cells
Profile of staff members
Barbara Bakker develops computational models for inborn and acquired metabolic diseases, notably in fat and carbohydrate metabolism. The group develops ODE models and generates relevant data with respect to enzyme kinetics, metabolite fluxes and concentrations. The overarching aim is to identify (personalized) drug targets and predict the effect of (personalized) dietary interventions.
Bert Groen investigates lipoprotein, glucose and cholesterol metabolism and metabolic disease in mouse and man. The group uses a broad range of biochemical and physiological techniques and notably has developed MS-based, model-assisted flux analysis with stable isotope-labelled metabolites in mouse and man.
Rainer Bischoff focuses on the analysis of highly complex mixtures with emphasis on biomarker discovery and validation. This comprises developing novel sample preparation, separation and mass spectrometry methods as well as data processing algorithms. As part of the Systems Biology Center for Energy Metabolism and Ageing we generate proteomics and metabolomics data.
Ernst Wit develops statistical methodologies for designing biomedical experiments, and for modelling biomedical models, such as kinetic models, metabolic flux models and gene regulatory network models. The overarching focus is to develop methodologies that connect the data generation with the mathematical models to describe the biological processes.
Lude Franke develops statistical methods to understand the downstream molecular mechanisms of genetic variants that cause disease. This can be achieved by integration of large datasets, quantitative trait locus mapping and development of new methos. The overall aim is to understand precisely through which mechanisms genetic variants ultimately cause disease.
Webpage
http://www.rug.nl/fwn/onderzoek/programmas/biologie/systemsbiology
Five recent publications relevant to the project
Van Eunen, K., Kiewiet, J., Westerhoff, H.V. and Bakker, B.M. (2012) Testing biochemistry revisited: how in vivo metabolism can be understood from in vitro enzyme kinetics. PLoS Comp. Biol. 8: e1002483.
Haanstra, J.R., Kerkhoven, E.J., Van Tuijl, A., Blits, M., Wurst, M., Van Nuland, R., Albert, M.J., Michels, P.A.M., Bouwman, J., Clayton, C., Westerhoff, H.V. and Bakker, B.M. (2011) A domino effect in drug action: from metabolic assault towards parasite differentiation. Mol. Microbiol. 79, 94-108.
E. Wit, R. Khanin, and V. Vinciotti (2006) Reconstructing repressor protein levels from expression of gene targets in E. Coli.. Proceedings of the National Academy of Sciences, 103(49):18592–18596.
Hoekman, B., Breitling, R., Suits, F., Bischoff, R., and Horvatovich, P. (2012) msCompare: a framework for quantitative analysis of label-free LC-MS data for comparative biomarker studies. Mol Cell Proteomics 11: M111.015974. doi: 10.1074/mcp.M111.015974.
Fu J, Wolfs MG, …, Franke L. Unraveling the regulatory mechanisms underlying tissue-dependent genetic variation of gene expression. PLoS Genet. 2012 Jan;8(1):e1002431
