Prof. Dr. rer. med. Ingo Röder
TU Dresden, Carl Gustav Carus Faculty of Medicine,
Institute for Medical Informatics and Biometry, Director
Systems biology, i.e. the complementation of experimental and clinical approaches by computational methods to quantitatively describe complex biological systems, is still a nascent discipline. However, even in the context of translational and clinical applications, the use of computational approaches is becoming more and more accepted.
In my presentation I will address the use of mathematical modelling to foster data analysis, data interpretation, but also clinical decision-making. To do so, I will present three examples of model-based strategies. All are motivated by the objective to better understand physiological and pathophysiological organization of hematopoietic stem cells and ultimately to improve leukemia treatment. Although guided by a common aim, the three examples show methodologically distinct contributions of model-based approaches at different scales.
The first example demonstrates how mathematical models can help to improve image analysis in the context of single cell tracking experiments. Here the combination of automatic image segmentation with fluid-like registration methods has been applied to resolve ambiguities in the automatic tracking of cells over time.
Whereas the improvement of image analysis methods is already beneficial on its own, interpretation of cell tracking data is also important to understand the physiological and pathophysiological behavior of cells, e.g. regarding the question of where hematopoietic stem cells reside in the bone marrow and of how they migrate between different local environments. To solve these questions, we apply a mechanistic mathematical model that has been developed to describe stem cell – niche interactions within 2D and 3D environments.
Finally, altered stem cell – niche interactions in the bone marrow are discussed as sources of clonal dysregulations and, therefore, also of leukemia. To show that already simple mathematical models can help to design improved therapeutic strategies and to support clinical decision-making, I will present recent results on model-based prediction of disease and treatment dynamics in chronic myeloid leukemia. The model quantitatively describes stem cell – niche interaction, however, this time at a much broader scale, integrating the effects at the individual cell level and describing leukemia as clonal competition between normal and malignant cell populations.
All three examples outline the diversity of mathematical modelling strategies, but they also clearly point out that a systemic understanding of biological processes, including disease development and therapeutic effects needs the concerted action of data generation, analysis, integration, and interpretation. Here computational methods have the potential to considerably strengthen clinical research in the future.