Lotte Søgaard-Andersen (Head of the Laboratory of Ecophysiology)
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Prof. Dr. Lotte Søgaard-Andersen Laboratorium für Mikrobiologie Fachbereich Biologie Philipps-Universität Marburg Karl-von-Frisch Strasse 8 D-35043 Marburg/Germany Contact: MPI für terrestrische Mikrobiologie Phone: +49-6421-178201 Fax: +49-6421-178209 Email:sogaard@mpi-marburg.mpg.de |
Lotte Søgaard-Andersen (born 1959)
M. Sc. thesis (Molecular biology), University of Odense, 1984
M.D., University of Odense, 1988
Visiting scientist, Institut Pasteur, Paris, 1990
PhD (Molecular biology), University of Odense, 1991
Post-doc, University of Odense, 1991
Assistant professor, University of Odense, 1992
Visiting scientist, Stanford University, 1994
Associate professor, University of Southern Denmark, 1996
Professor, University of Southern Denmark, 2002
Director and Head of the Department of Ecophysiology at the MPI in Marburg, since 2004
Professor for Microbiology at the Philipps University Marburg, since 2008
Research Area: Our research focuses on understanding how intracellular signalling networks are wired to allow bacteria to adapt and differentiate in response to changes in the environment or in response to self-generated signals. Bacterial cells process vast amounts of environmental information and information from self-generated signals to generate sophisticated responses such as adaptation, differentiation, growth and movement. Information-processing is carried out by complex networks of signal transduction proteins. One of the most challenging problems in biology is to understand how these protein networks are organized in space and time to allow the ordered execution of these tasks. We are probing this question by studying signal transduction pathways governing growth, cell polarity, development and motility in the bacterium Myxococcus xanthus. Experimentally, we take an interdisciplinary approach that incorporates diverse techniques including:
• Molecular genetics
• In vitro analyses of purified proteins
• Cell biology
• Comparative genomics
• Functional genomics
• Mathematical modeling
