BIMSB Proteomics / Metabolomics

Overview

Within the past decades biochemical data of single processes, metabolic and signaling pathways were collected and advances in technology led to improvements of sensitivity and resolution of bioanalytical techniques. These achievements build the bases for the so called ‘genome wide biochemistry’. High throughput techniques are the tool for large scale ‘-omics’ studies allowing the obtainment of a nearly complete picture of a determinate cell state, concerning its metabolites, proteins and transcripts. However, a single level study of a living organism cannot give a complete understanding of the mechanisms regulating biological functions. The integration of transcriptomics, proteomics and metabolomics data with existing knowledge allows connecting biological processes which were treated as independent so far. In this context the aim of our group is to apply metabolomics and proteomics techniques for absolute quantification and to analyze turnover rates of proteins and metabolites using stable isotopes. In addition, the development of data analysis workflows and integrative strategies are in the focus of our interest.

The central metabolism is the principal source of energy and building blocks for cell growth and survival. It is highly flexible and adjusted to the physiological program of the cell, organ and organism. In a healthy state cellular metabolism is tightly regulated to guarantee physiological function but also efficient usage of available recourses. Metabolic dys-regulations are cause or response to many diseases. An impaired metabolic activity can lead to the loss of the physiological activity, cell damage or inefficient substrate usage. However, the underlying mechanisms leading to metabolic dys-functions are not well understood. The regulation of metabolism is complex, because it acts at all biological layers – transcriptional, translational and post-translational. Thus the metabolic activity of a cell, organ or organism inherits the information of regulatory layers in a multidimensional manner. I guess only the use of integrative mathematical approaches will enable us to decode such complex information.

In this regard, decoding the metabolic composition of biofluids e.g. blood serum may allow to determine a systems status, to identify diseases, predict drug responsiveness and to follow the success of medical treatments. This is a step towards personalized medicine.