Dr. Michael Habeck
Foto Dr. Michael Habeck


Institut für Mathematische Stochastik


Goldschmidtstrasse 7
37077 Göttingen

Room:Raum 2.191: Gebaeude GZG: Goldschmidtstr. 3-5 (GZG)
Phone:+49 551 39 172136
Mail:Michael.habeck@mathematik.uni-goettingen.de
Research statement

I develop Bayesian statistical methods, with application to structural biology and biological physics. I also maintain interests in various related areas of statistical physics and biology, including statistical analysis of biomolecular structures and sequences, probability theory, machine learning, image processing and Bayesian computation.

CV

Professional positions
2014-presentProject leader at Max Planck Institute for Biophysical Chemistry and Felix Bernstein Institute for Mathematical Statistics in the Biosciences
2013-2014Leader of an independent research group (Emmy Noether Programme) at Institute for Mathematical Stochastics
2009-2012Leader of an independent research group (Emmy Noether Programme) at Department of Protein Evolution (MPI for Developmental Biology, Tübingen)
2005-2009Research scientist at Department of Empirical Inference (MPI for Biological Cybernetics) and Department of Protein Evolution (MPI for Developmental Biology, Tübingen)
2004-2005Postdoctoral work at Structural Bioinformatics Unit, Institut Pasteur, Paris, France

Education

2004PhD in Biophysics at Regensburg University, Germany
2001-2004PhD work at Institut Pasteur, Paris
1999Diplom in Physics, Heidelberg University, Germany

Funding

2010-2014Baden-Wuerttemberg Stiftung
2009-2014Emmy Noether-Programme (DFG)

Publications

Full publication list at google scholar

Selected publications:

2017

Data-driven coarse graining of large biomolecular structures.
Chen YL, Habeck M
PloS One 2017; 12(8): e0183057
doi:10.1371/journal.pone.0183057
 
Bayesian modeling of biomolecular assemblies with Cryo-EM maps.
Habeck M
Frontiers in Molecular Biosciences 2017; 4: 15
doi:10.3389/fmolb.2017.00015
 
2016

Inferential structure determination of chromosomes from single-cell Hi-C data.
Carstens S, Nilges M, Habeck M
PLoS Computational Biology 2016; 12(12): e1005292
doi:10.1371/journal.pcbi.1005292

A probabilistic model for detecting rigid domains in protein structures.
Nguyen, T, Habeck M
Bioinformatics 2016; 32(17): i710-i717
doi:10.1093/bioinformatics/btw442

2015

Bayesian inference of initial models in cryo-electron microscopy using pseudo-atoms.
Joubert P, Habeck M
Biophysical Journal 2015; 108(5): 1165-1175
doi:10.1016/j.bpj.2014.12.054

Hybrid structure of the type 1 pilus of uropathogenic Escherichia coli.
Habenstein B, Loquet A, ..., Habeck M*, Lange A*
Angewandte Chemie 2015; 127(40): 11857-11861
doi:10.1002/ange.201505065

2014

Bayesian approach to inverse statistical mechanics.
Habeck M
Physical Review E 2014; 89(5): 052113
doi:10.1103/PhysRevE.89.052113

2013

Estimation of interaction potentials through the configurational temperature formalism.
Mechelke M, Habeck M
Journal of Chemical Theory and Computation 2013; 9(12): 5685-5692.
doi:10.1021/ct400580p

2005

Inferential structure determination.
Rieping W*, Habeck M*, Nilges M
Science 2005; 309: 303-6
doi:10.1126/science.1110428
 
Courses

Sommersemester 2013

Bayesian probability theory - foundations and applications