Research

Motivation

Decades of research on metabolism, and more recently genome-based reconstructions, have produced detailed metabolic maps, which - for many organisms - include several hundreds of different intermediates and connecting enzymatic reactions. Despite this detailed knowledge on network topology, our capacity to predict metabolic responses to environmental stimuli or genetic perturbations is still very limited. The major burden is the lack of direct data on in vivo network operation to infer the regulatory mechanisms governing metabolic activity. To overcome this informational gap, our lab researches novel concepts and tools that monitors state and activity of metabolic networks. In particular, we strive to develop generally applicable approaches that can cope with technically difficult systems such as mammalian cells, complex environments, dynamic states, and heterogeneous populations.

Approaches

We pursue primarily a bottom-up, data-driven approach strategy. We start from large-scale molecular profiling of metabolic phenotypes by mass spectrometry and fluorescent microscopy. These data are then interpreted and integrated to infer the underlying principles and the (regulatory) mechanisms governing metabolic responses. Targeted, quantitative analytical methods are the employed to validate hyopthesis. More frequently, we apply genetic or ennvirnomental perturbationto verify the functional relevance of the discovered metabolic changes.

Metabolomics

We pursue a primarily data-driven approach largely based on mass spectrometry and fluorescent microscopy, which aim at collect precise information on the state and activity of metabolic networks. On all analytical platforms, we independently work on comprehensive pipelines for either high-throughput phenotyping for discovery or highly targeted, quantitative measurements for validation. These data are then interpreted and integrated to infer the underlying principles and the (regulatory) mechanisms governing metabolic responses.

HT

Link

13C Metabolic flux analysis 

Bio- and cheminformatics

In parallel, we have a strong program in software development for chemometrics, automatization, workflow management, data mining, data integration, and hypothesis testing. One important goal is to maximize speed, quality, and significance of data analysis in hundreds of independent studies per year. On the other hand, this has become strictly necessary to enable large-scale, integrative numerical analysis of omics datasets such as generated on our high-throughput analytics. Tools of general interest for a broader community are released to the public.

 

Applications

The versatility and power of our tools is suited to tackle fundamental questions in all areas where cellular metabolism is of relevance: systems biology, metabolic engineering, drug development against pathogens or cancer, toxicology, cell differentiation, nutrition, evolution, etc. These activities heavily rely on a large world-wide network of collaborations both with Academy and Industry.

Cancer cells, Skin & Metabolism, Stem Cells, Unicellular organisms

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