From Active Processes to Self-Organization in Biology
Active Processes in Biology
Activity is a hallmark of every living system. We are interested in the physical principles that drive intracellular rearrangements of transport systems and the cytoskeleton. For example, when T-cells attack pathogens, the cytoskeleton undergoes a remarkable rearrangement.
On a cellular level, we investigate spontaneous oscillations of hair bundles, which are the mechanoreceptors in our ears.
Connecting Theory to Experiments by Machine Learning
A rigorous connection of theory and experiments is often impaired by the absence of tools that transform experimental data into useful numbers. We explore the possibility of machine-learning techniques including deep learning to facilitate this connection and to enhance the data quality.
Active processes in cells are driven, to a large extent, by molecular motors. How do these molecules transform chemical energy into mechanical work? How can they cooperate in teams? How do they respond to external forces? We are addressing these questions by introducing quantitative descriptions of their biophysical properties and cellular activity.