Florian Berger: Theoretical Biophysics

CV – Research – Lab members – Publications

Dr. Florian Berger
Cell Biology, Neurobiology and Biophysics
Department of Biology
Faculty of Science, Utrecht University
Kruytgebouw, room N509
Padualaan 8, 3584 CH Utrecht
The Netherlands
Email: f.m.berger@uu.nl

Curriculum Vitae

Florian Berger studied physics at Stuttgart University, Germany, where he investigated stochastic nonequilibrium systems in the research group of Prof. Dr. Seifert. For his Ph.D. work, he joined the Theory and Biosystems Department led by Prof. Dr. Lipowsky at the Max Planck Institute in Potsdam, Germany. Here, he developed a theoretical framework to study the cooperativity of coupled molecular motors, for which he received a Ph.D. in theoretical Physics with highest honors. To investigate the physiological role of molecular motors as tension regulators on mechanosensitive ion channels in the inner ear, Dr. Berger conducted research in Prof. Dr. Hudspeth’s Laboratory of Sensory Neuroscience at the Rockefeller University, New York, USA. In this lab, he combined theoretical descriptions with experimentations to address different problems ranging from nonlinear systems theory in hearing to the cooperative gating of ion channels.

For his postdoctoral research, Dr. Berger received a Feodor Lynen Fellowship from the Alexander von Humboldt Foundation and a Pilot Grant from the Kavli Neural Systems Institute. Since October 2019, he is the principal investigator of the research group for theoretical biophysics at Utrecht University.

Research summary

All forms of life display a remarkable variety of active processes on different length scales. We develop biophysical descriptions to understand how these active processes mediate the flow of energy and matter to self-organize cellular order and function. These descriptions span a wide range of length scales, from structure-function relations of molecular-motor heads, transport and positioning of organelles, up to the cellular activity of hearing organs. We believe that a mutual stimulation of theoretical considerations and experimental findings is a beautiful way to decipher nature’s fundamental principles.

We apply concepts from stochastic physics and statistical mechanics to understand diverse biological systems. In a new effort, we explore the use of machine-learning methods, such as deep neural networks to transform imaging data into suitable data for biophysical modeling. You can find more information about our current activities and possible research projects on our webpage:

www.biophyslab.com