Email: bernhard.reuterspam prevention@uni-tuebingen.de
https://www.researchgate.net/profile/Bernhard_Reuter
 

Academic Degrees

• Doctorate (Dr. rer. nat.) in Theoretical Physics, University of Kassel, Germany, 2018.
  Thesis: Generalisierte Markov-Modellierung von Nichtgleichgewichtssystemen: Simulation und Modellierung der Amyloid-β-(1-40)-Konformationsdynamik unter Mikrowelleneinfluss
• Diploma in Physics (Dipl.-Phys.), University of Kassel, Germany, 2013.
  Thesis: Faltung kleiner Lennard-Jones-Proteine unter dem Einfluss von Temperaturgradienten in einem reduzierten Modell
   

Research Interests

• Statistical Machine Learning
• Bioinformatics
• Medical Informatics
• Computational Biology
• Molecular Dynamics
• Markov Modeling
• in particular:
  • Development of methods and tools to predict drug-resistance profiles from whole genome sequencing (WGS) data to enable clinicians to conduct individualized therapy regimens for multidrug resistant tuberculosis (MDR-TB) patients.
  • Simulation and modeling of bio-molecular non-equilibrium systems.
  • Development of data-based methods (i.e., the Generalized Perron Cluster Cluster Analysis (G-PCCA)) to model bio-molecular non-equilibrium processes.
     

Teaching

• Summer 2022
  • Lecture: Bioinformatics for Life Scientists (with Nico Pfeifer)
• Winter 2021/22
  • Practical Course: Software Development with Scrum (with Nico Pfeifer)
• Summer 2020
  • Lecture: Bioinformatics for Life Scientists (with Nico Pfeifer)
     

Publications

• Marius Lange, Volker Bergen, Michal Klein, Manu Setty, Bernhard Reuter, Mostafa Bakhti, Heiko Lickert, Meshal Ansari, Janine Schniering, Herbert B. Schiller, Dana Pe’er, Fabian J. Theis: CellRank for directed single-cell fate mapping. Nature Methods, 2022
• Jonas C. Ditz, Bernhard Reuter, Nico Pfeifer: Convolutional Motif Kernel Networks. [arXiv preprint]
• Bernhard Reuter: Generalisierte Markov-Modellierung - Modellierung irreversibler β-Amyloid-Peptid-Dynamik unter Mikrowelleneinfluss. Springer Spektrum, Wiesbaden, 2020; https://doi.org/10.1007/978-3-658-29712-1
• Bernhard Reuter, Marcus Weber, Konstantin Fackeldey: Generalized Markov modeling of nonreversible molecular kinetics. Invited paper on the special topic of Markov Models of Molecular Kinetics in The Journal of Chemical Physics, 150, 174103 (2019); https://doi.org/10.1063/1.5064530
• Bernhard Reuter, Marcus Weber, Konstantin Fackeldey, Susanna Röblitz, Martin E. Garcia: Generalized Markov State Modeling Method for Nonequilibrium Biomolecular Dynamics: Exemplified on Amyloid β Conformational Dynamics Driven by an Oscillating Electric Field. Journal of Chemical Theory and Computation, 14(7), 3579–3594 (2018); https://doi.org/10.1021/acs.jctc.8b00079

Selected Conference Contributions

• Bernhard Reuter, Matthias Merker, Stefan Niemann, Cristoph Lange, Rolf Kaiser, Nico Pfeifer, Jan Heyckendorf: Machine learning for prediction of drug resistance in Mycobacterium tuberculosis strains. Poster, ECCMID, 2021
• Bernhard Reuter, Konstantin Fackeldey, Susanna Röblitz, Marcus Weber, Martin E. Garcia: G-PCCA - a generalized Markov state modeling approach for both equilibrium and non-equilibrium systems. Talk, DPG spring meeting, Berlin, 2018
• Bernhard Reuter, Marcus Weber, Martin E. Garcia: Unraveling the effects of an oscillating electric field on Amyloid-β (1-40) conformational dynamics using G-PCCA, a generalized Markov state modeling method. Poster, DPG spring meeting, Berlin, 2018; http://dx.doi.org/10.13140/RG.2.2.24730.36803
• Bernhard Reuter, Martin E. Garcia: Influence of microwaves on protein conformational dynamics. Poster, Gordon Research Conference: Protein Folding Dynamics, Galveston (USA), 2016; http://dx.doi.org/10.13140/RG.2.2.35635.55842
• Bernhard Reuter, Pedro A. Ojeda May, Martin E. Garcia: Protein folding: Driving forces and external influences. Poster, Gordon Research Conference: Protein Folding Dynamics, Galveston (USA), 2014; http://dx.doi.org/10.13140/RG.2.2.22213.78560

Selected Code

• pyGPCCA - python GPCCA: Generalized Perron Cluster Cluster Analysis package to coarse-grain reversible and non-reversible Markov State Models: https://github.com/msmdev/pyGPCCA
• gpcca - A Generalized Perron Cluster Cluster Analysis (G-PCCA) MATLAB program to coarse-grain reversible AND non-reversible Markov state models: https://github.com/msmdev/gpcca