We integrate information on multiple scales, ranging from single-cell time series to cohort omics data. Heterogeneities in the data are modeled using statistical and dynamical approaches, both on the cellular and on the population level. We develop innovative methods and tools in - data sciences - dynamical modeling - integrative omics and - bioimage informatics and apply them collaboratively with our experimental partners to - modeling stem cell decisions - modeling transcriptional heterogeneities and - quantifying gene environment effects in diabetes and lung disease to jointly advance our understanding and treatment of human diseases.