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.

Rearrangement of cytoskeletal network in T-cells. Simulations with cytosim (MSc project by O.J. Gros)
Spontaneous oscillations of hair cells in the inner ear of a frog (by F. Berger, The Rockefeller University)

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.

Confocal image of microtubule network (by E. Katrukha, Utrecht University)
Super-resolution prediction from confocal image by a deep neural network (MSc project, M. Hamakers)

Molecular Motors

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.

We developed a method to determine the force-dependent unbinding rate of a molecular motor in a stationary optical trap. (Nano Lett 2019)