Biography

Rainer Breitling studied biochemistry at the University of Hannover, Germany, and received his Ph.D. from the Technical University of Munich, Germany, in 2001. He held postdoctoral positions as a bioinformatician at San Diego State University (California, USA) and the University of Glasgow (UK), followed by professorial appointments at the University of Groningen (The Netherlands) and the University of Glasgow. Since September 2012 he is Professor of Systems Biology at the Manchester Institute of Biotechnology, University of Manchester.

Research interests

In our work we are covering a broad range of Systems Biology topics. Some areas of special interest are the following:

Dynamic Systems Modelling: Using a wide range of modelling techniques, from constraint-based approaches to differential equation models, to understand the complexity of cell signalling and cellular metabolism. A particular focus is the explicit incorporation of uncertain, incomplete or semi-quantitative data in the modelling process.

Metabolomics: Developing new tools for the analysis of quantitative metabolomics data generated by high-resolution Fourier Transform and Orbitrap mass spectrometry, to understand cellular physiology, especially of genetically engineered organisms.

Transcriptomics: Creating statistical techniques for gene expression analysis (RankProducts, iterative GroupAnalysis, VectorAnalysis), which are used in laboratories worldwide.

Synthetic Biology: Exploring the application of bioinformatics and systems biology techniques to the engineering of “designer microbes”, and using metabolomics and transcriptomics in the diagnosis and debugging of the organisms created by synthetic biology approaches.

In our work we study model organisms of various levels of complexity, ranging from bacteria, like the antibiotic producer Streptomyces coelicolor, to single-cell eukaryotic models such as the protozoan parasite Trypanosoma brucei, and finally to mammals, including mouse and human.

Overview

The Breitling group is interested in using computational approaches to understand complex biological systems, ranging from microbes to man. Two main strategies dominate our work: on the one hand, we develop methods to interpret “molecular profiles”, e.g. the global patterns of gene expression or the abundance of small molecules (metabolites) in biological samples. On the other hand, we develop quantitative models to describe and predict the behaviour of cells, e.g. after drug treatment or genetic manipulation.