Chair of Systems Biology

Cellular processes are controlled by complex networks of interacting molecules. Diseases arise from disruptions in these networks and can only be undersood to a limited extent by examining individual genes or proteins. Therefore, our scientific interest is in the systemic investigation of cellular regulatory networks, especially in the field of cancer research. In our research, we combine mathematical models with high-dimensional experimental data in order to obtain insights into disease-related changes.

One focus of our research is the quantitative description of cellular heterogeneity. Even genetically identical cells react differently (heterogeneously) to environmental changes. This heterogeneity is an essential feature of cellular decision-making processes and can be responsible for the diversification of tissues as well as for pathological changes. We characterize the variability of intracellular networks using time-resolved imaging methods and genomic analyzes at the single cell level. Based on this data, we develop theoretical models with which we can quantitatively map heterogeneous cell populations in the computer and thus identify the causes of cellular variability (Strasen et al., 2018; Fritzsch et al., 2018) and understand how biological systems function robustly despite fluctuations (Kamenz et al., 2015).

As a second focus, our group studies how the activity of genes in the cell nucleus is controlled by complex regulatory networks. We have developed systems-theoretical approaches with which we derive the interaction of the molecules in these networks from disturbance measurements and genomic ("Multi-OMICS") data sets (Braun et al., 2018; Stelniec et al., 2012; Becker et al. 2018) . The main focus of our experimental and theoretical analyzes on gene regulation is to quantitatively investigate alternative splicing. This process adds to the complexity of human cells as it enables the production of many protein variants from almost any gene. Using systems biology approaches, based on DNA and RNA sequencing data, we were able to characterize thousands of sequence mutations that influence a deregulated splicing decision in cancer cells (Braun et al., 2018). Furthermore, we have developed quantitative kinetic models to mechanistically describe the highly complex molecular machinery of alternative splicing (Sutandy et al., 2018; Enculescu et al., 2020). With such approaches we hope to gain insights into the regulatory principles of this important gene regulatory process.