Dresden International Graduate School for Biomedicine and Bioengineering

Our group develops and applies mathematical models and simulation strategies of regulatory processes on the molecular, the cellular, and the tissue level with the aim to understand and potentially predict the behavior of biological systems. Major foci are medically relevant topics, such as the investigation of principles of stem cell organisation ("theoretical stem cell biology") and of mechanisms of cancer development and treatment. Further research topics are statistical/computational methods to study complex experimental data (e.g. cellular genealogies) and image analysis procedures (e.g. model-based segmentation algorithms in the context of automatic single cell tracking).

Specific projects in the field of Medical Systems Biology / Mathematical Modelling include the investigation of stem cell - niche (i.e. local growth environment) interactions, the analysis and the prediction of clonal competition processes, as well as the modelling of aging-related phenomena in the haematopoietic system. Using agent-based modelling and simulation approaches, we contribute to a better understanding of disease development and/or treatment effects (e.g. the context of leukaemia treatment or gene therapy approaches). Another research field is the understanding of gene regulatory principles on the level of small-scale transcription factor networks. In particular, we are interested in the role of transcription factor interactions for the regulation of pluripotentcy in embryonic (ES) and induced pluripotent stem (iPS) cells.

With respect to the newly emerging field of Bioimage Informatics, our group is specifically interested in the development of automatic single cell tracking algorithms and their application. For the tracking, we apply different types of model-based segmentation methods. The analysis of the resulting single cell data (e.g. cellular genealogy structures complemented with information on cell-cell contacts or cell migration) is done by statistical models, such as generalised linear mixed-effect models or by dynamical networks based on (cell) community structures.