Division of Systems Biology of Signal Transduction

Bridging from the cellular to the organ scale, the division aims at unraveling molecular mechanisms regulating cell communication and cell decisions, as a failure of these control mechanisms contributes to tumor development. We combine quantitative technologies with mechanism-based mathematical modeling to enable early detection and personalized treatment of cancer to prevent tumor progression and improve quality of life.

Cellular responses are regulated by a multitude of extracellular signals received by cell surface receptors. Within cells the information is processed through complex intracellular signaling networks that in turn impinge on gene regulation. The dynamics of signal transduction is affected by the metabolic state of cells as well as the impact of signal transduction on metabolism. These multitude of reactions are integrated and converted to phenotypic responses such as proliferation, survival and differentiation. The majority of these responses are non-linear and operate on very different time scales, ranging from minutes to hours and days. Due to this complexity, it is of advantage to employ mathematical modelling approaches. Data-based mathematical models not only enable rapid testing of hypotheses to uncover deregulation in cancer, but also facilitate the prediction of trajectories such as disease development and support the establishment optimized intervention strategies to change the course of a disease.

In close cooperation with our mathematical modelling partners and the clinical partners, we are advancing cellular model systems to include an authentic extracellular matrix, developing cutting edge mass spectrometry approaches for clinical proteomics and increasingly combine AI-based modelling approaches with dynamic pathway modeling. The key areas of medical interest are processes determining the development of leukemia, lung cancer or liver cancer.

Areas of focus in the division are:

1. Advancing systems medicine approaches for clinical translation through standardization of workflows for high-quality data generation and the development of predictive mathematical models
2. Bridging from the single cell to the cell population and organ level to unravel principal mechanisms controlling erythropoietin (Epo)-induced cellular responses in the hematopoietic system.
3. Resolving interactions in the tumor microenvironment determining lung cancer development and personalized optimization of intervention strategies.
4. Deciphering the interconnection of signal transduction and metabolism to unravel mechanisms controlling the compensation of liver injury due to drugs or viral infection and to establish model-based biomarkers for early detection of liver cancer.

OUTLOOK
Our ambition is to establish mathematical models for the prediction of disease trajectories to support early detection of cancer and to establish effective intervention strategies for converting an aggressively progressing cancer to a controllable disease. Therefore, we continue to develop integrative models linking signal transduction, metabolism and phenotypic responses that are essential building blocks for the establishment of mechanistic multi-scale models to predict the impact of cellular alterations on the entire organ. Our mass spectrometry based clinical proteomics pipeline greatly increases the possibilities to generate reliable quantitative data and through the combination of AI-based pattern recognition with dynamic pathway modeling much extends the development of predictive models. Through our clinical partners we have access to high-quality patient samples that allow to adapt our predictive mathematical models to disease states and to resolve mechanisms leading from chronic diseases to cancer and impacting the heart.