Many organs of higher animals, such as lung and mammary gland, are composed of heavily branched epithelial structures. The branching process enables a large increase in the epithelial surface area for functional purposes to support fundamental physiological functions. Despite major advances in understanding the mechanisms that determine stereotypic branching morphogenesis of the lung, the mechanisms that regulate stochastic branching of mammary epithelium have remained rather elusive.
In this project, we will apply mathematical modelling, force-inference techniques from civil engineering and computational simulations to experimental data obtained from advanced, physiologically relevant in vitro models of mammary epithelial branching morphogenesis using automated image-analysis to decipher the mechanisms of mammary epithelial branched pattern formation. More specifically, we will investigate the role of FGF2 signalling intensity, myoepithelial cells and mechanical forces in regulation of mammary epithelial branching morphogenesis. To this end, we will develop and use deep-learning-based pipelines for segmentation and tracking of 3D organoids as well as individual cells in data from time-lapse microscopy imaging and a computational model of FGF2-induced mammary organoid branching that will comprehend the complex interplay of cell signalling and mechanical forces. Our approach will be highly iterative: The data obtained from biological experiments will inform modelling choices and feed computational simulations, results of which will stimulate design of new experiments. Implementation of this multidisciplinary iterative approach will allow for both hypothesis testing and discovery to unravel key mechanisms of mammary epithelial branching.
This multidisciplinary study will contribute to understanding general principles of branching morphogenesis as well as identification of key aspects of normal and pathological cell behaviours.

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