Habeck, Michael, Dr.
- 1993-1999 Physics studies at the University of Siegen and the University of Heidelberg
- 2001-2004 PhD work at Institut Pasteur, Paris
- 2004-2005 Postdoc Institut Pasteur, Paris
- 2005-2009 Research scientist at the Max-Planck-Institute for Biological Cybernetics and the Max-Planck-Institute for Developmental Biology, Tübingen
- 2009-2012 Leader of an independent research group (Emmy Noether Programme) at the Max-Planck-Institute for Developmental Biology, Tübingen
- Since 2013 Leader of an independent research group (Emmy Noether Programme) at the University of Göttingen
- S. Shahid, B. Bardiaux, T. Franks, L. Krabben, M. Habeck, B. Rossum, D. Linke (2012) Membrane-protein structure determination by solid-state NMR spectroscopy of microcrystals. Nature Methods 9, 1212-7
- M. Habeck (2012) Bayesian estimation of free energies from equilibrium simulations. Physical Review Letters 109, 100601
- V. S. Honndorf, N. Coudevylle, S. Laufer, S. Becker, C. Griesinger and M. Habeck (2012) Inferential NMR/X-ray based structure determination of a dibenzo[a,d]cyclo-heptenone inhibitor/p38 MAP kinase complex in solution. Angewandte Chemie 51, 2359-62
- M. Habeck, W. Rieping, and M. Nilges (2006) Weighting of experimental evidence in macromolecular structure determination. PNAS 103, 1756-61
- W. Rieping, M. Habeck, and M. Nilges (2005) Inferential structure determination. Science 309, 303-6
Major Research Interests
I am broadly interested in Bayesian inference and its application to biological and other real- world data. I view probability theory as the mathematical framework for making consistent inferences and predictions from incomplete and uncertain information. Therefore, Bayesian methods are powerful tools to support scientific inference and to solve engineering problems. My previous and current research focuses on three highly interconnected topics: Computational structural biology, Bayesian computation and Statistical physics.
My group is mainly involved in the development of a Bayesian probabilistic approach to biomolecular structure determination which we call Inferential Structure Determination (ISD). Over the years, ISD has matured into a powerful tool for macromolecular structure calculation from NMR, X-ray, cryo-EM and other structural data by means of Markov chain Monte Carlo simulation. Moreover, we maintain a keen interest in developing new methods for cryo-EM data analysis and image processing. We are currently developing tools to model large and dynamic systems (protein complexes, chromosomes) and work on new algorithms for general Bayesian computation.
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Selected Recent Publications