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ICS-2 Talk: Mathematical models for collective cell migration in development and disease

Prof. Dr. Franziska Matthäus, Faculty of Biological Sciences & FIAS
University of Frankfurt

29.11.2018 14:00 Uhr
Gebäude 04.16, Raum 2001


Background: Cell motility plays an important role in immune and developmental processes, wound healing and regeneration, but also in diseases such as cancer. In epithelial systems, cells often migrate collectively, because they adhere to each other to form connected tissues. Collective migration is characterized by a strong coordination of neighboring cells and spatial velocity correlations. To gain understanding on the characteristics and the regulatory processes guiding collective cell motility we combine exhaustive data analysis with mathematical modeling.

Methods and Results: We use particle image velocimetry (PIV) to obtain quantitative data from time-lapse microscopy. From the resulting velocity fields we derive spatio-temporal velocity distributions, divergence, vorticity, streamlines or pathlines. Based on these data we develop agent-based or hybrid mathematical models accounting for mechanical cell-cell interaction (adhesion, repulsion), mechanotransduction, chemotaxis, as well as the interaction with a dynamically changing chemical environment. We present results for two systems – collective migration of lung cancer cell lines, and skin patterning in embryonal development.

Discussion: Our models demonstrate that many observed phenomena can be explained by the mechanical interplay between the cells, or the interplay of cell mechanics and cell response to chemical cues. Altered migration phenotypes in lung cancer cell lines following growth factor treatment can be attributed to changes in cell elasticity, cell adhesion or mechanotransduction. Pattern formation in embryonal skin development involves the interplay of cell mechanics, a chemical Turing system and chemotaxis. We also outline a possible approach for the inference of model parameters, enabling the estimation of single-cell mechanical properties from time-lapse data. 


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