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Research Interest

  • Systems biology of metabolic disease and diabetes.
  • Computational analysis of multi-omics data.
  • Linking molecular data to metabolic phenotypes.


Exploiting indirect calorimetry to understand individual differences in the homeostatic control of energy balance

Indirect Calorimetry is used to estimate energy expenditure in living organisms. By simultaneously monitoring O2 consumption and CO2 production, as well as body temperature, locomotor activity, and food intake of individual animals, the overall energy consumption, as well as oxidative substrate preference can be estimated. However, in order to tap the full diagnostic potential or these high-resolution multivariate longitudinal data, novel sophisticated statistical methods are required. We are currently developing new tools to analyze and compare individuals or categorical groups, including disease models, in order to extract comparable features. In close collaboration with  IDO researchers, we aim to optimize these tools and methods in order to link identified features with metabolic disease-related phenotypes. We will additionally explore multi-omics data (transcriptomics, proteomics, metabolomics) to identify new molecular signatures that chart individual metabolic disease progression and susceptibility. Our results will help to develop novel predictive tools and strategies for a personalized prevention and therapy of metabolic disease.

For this highly interdisciplinary project, a strong background in physics, statistics and computational data analysis is essential, as well as a genuine interest in energy metabolism.

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