Project 9

We are developing models, methods, and algorithms to identify rhythms across different scales and to decipher how they combine to generate the recorded data or how their coupling can be resolved. Our focus is particularly on interaction with spatial processes such as diffusion, localized dynamics, and compartmentalization in the RTG´s biological organisms. Our approach combines advanced signal processing, unsupervised machine learning, numerical methods for ODE/PDE systems, and finite element simulations for the analysis of heterogeneous data. 
Using these methods, we aim to study multiscale metabolic oscillators in yeast (Klassen and Schaffrath) and various forms of rhythmic animal behavior (tardigrades Mayer; cockroaches Stengl) as well as the rhythmic expression and release of neuropeptide gene products at the single-cell level in Drosophila (Neupert).