We seek to theoretically understand biological solutions for motility control and self-organization in cells and tissues. A special emphasis is on principles that make biological function robust in the presence of strong fluctuations.

We combine dynamical systems theory, statistical physics, and image analysis to reverse-engineer physical mechanisms of robust feedback control. We want to understand emergent dynamics from simple rules.

We enjoy close collaborations with biologists with rapid iteration loops between quantitative theory and experiment.

Motility control: At the cellular scale, we study how noisy sensory information controls motion. Our model system are flagellated microswimmers, where we study swimming, steering, and synchronization, e.g. in sperm navigation for the egg.

Pattern control: At the tissue scale, we study elementary rules of self-organized pattern formation during self-repair and adaptation to fluctuating environments. In the past, we have studied self-organized scaling of developmental patterns. Current project address design principles and transport in 3-dimensional tissues such as the liver.

Our work draws inspiration from physics, information theory, and engineering; likewise, we seek to excite bio-inspired applications of biological information processing in these fields.