Systems Biology of Signal Transduction

Research Strategy

Cancer progression is a dynamic process fueled by multiple alterations at the executing level of proteins. To disentangle these complex relations, we develop mechanism- based dynamic pathway models and utilize studies on signal transduction
through the erythropoietin (Epo) receptor, in hepatocytes and in lung cancer cells as paradigms for method and concept development. The Epo/EpoR system enabled us to demonstrate that it is possible to identify key regulatory mechanisms with our dynamic pathway modeling approach, e.g., that the abundance of signaling components  determines cell context-specific information processing. We extended this work by employing a mixed effect modelling approach to bridge from the cell population to the single cell level. This allowed us to resolve that the heterogeneity in Epo-induced JAK2/STAT5 signaling in erythroid progenitor cells is primarily caused by processes occurring at cellular or nuclear membranes and is much influenced by the cell size.

Important components of cellular defense mechanisms are interferons (IFN) that also activate the JAK/STAT signaling pathway. By dynamic pathway modelling, we showed that the timing and the abundance of feedback regulators critically shape the dynamics of pathway activation and provide molecular explanations for pathway desensitization and pathway hypersensitization, depending on the pre-stimulation concentration of IFNα. The quantitative analysis of TGFβ-induced SMAD complex formation in combination with dynamic pathway modelling allowed us to identify among the large number of possible combinations the three most relevant trimeric SMAD complexes and link them to target gene expression. In the coming years, we will continue to invest in quantitative proteomics approaches and develop targeted detection for absolute quantification of all major signaling pathways. The aim is to provide high-quality data from cell lines, primary cells and patient samples for dynamic pathway modelling to resolve mechanism of clinical relevance and improve the predictive accuracy of our modelling approaches.