The group for Medical Systems Biology focuses on the development and verification of mathematical models for cellular behavior and cell communication from an initial stimulus to the final phenotype.

In a systems biology approach we combine experimental research on cell-cell communication with the development of appropriate multi-scale dynamic models to investigate the necessary and sufficient control points that lead to cell proliferation, differentiation, migration or death.

We adapt concepts from non-linear dynamics and complex systems to develop appropriate dynamic models unraveling self-organizing properties in cellular behavior. Such behavior in a multicellular environment is most likely the results of time-sequential events, involving protein signaling and gene regulation in feedback-entangled processes lasting several hours.

Systems theory suggests that the slowest evolving variables determine the long term outcome of a system. In a biological context, it is thus the change in gene expression that reflects the macroscopic decision of a cell. Formalizing these ideas in a dynamic modeling approach, we use neural network and rule based modelling approaches to reconstruct the dynamic control logic of cellular decision processes based on gene expression kinetics. Time-resolved experimental data will be recorded in our lab under well defined cell culture and context-dependent conditions. Data is collected on the cell population level using DNA microarrays and RT-PCR as well as on the single cell level by time-lapse microscopy.