Medical Systems Biology

The aim of our research is the development and application of methods for the integrative analysis of large-scale data sets

encompassing proteomic, transcriptomic and metabolomic data. While many current approaches consider these data sets independently from each other, our aim is to bring them into context to understand biological phenomena on a systems level. To this end we employ a wide range of systems biological modeling tools that reach from the mapping of the activity of individual metabolic pathways using constraint-based modeling to individual-based modeling to translate the relevance of changes observed on a molecular level to the population level.

A guiding philosophy in our research it not only the analysis of data but the aim to derive hypotheses that can be tested experimentally. Thus, we closely collaborate with experimental partners to guide analysis, build hypotheses and derive experimental settings in which they can be tested.

Our research is focused on two main topics, computational microbiology and systems biology of aging. In the context of computational microbiology, we are interested in regulatory mechanisms that allow microbes to quickly adapt and prepare for changes in their environmental conditions with a particular focus on infection processes. In systems biology of aging, we use the comparative analysis of aging data generated in several species to understand the molecular changes that underly the aging process. There, we are particularly interested in disentangling changes that are due to the aging process itself (i.e. the deterioration of physiological processes over time) and those that are adaptive in response to aging. 


We have open PostDoc, PhD student and student assistant positions. For more information please contact