1-2 October 2020
Europe/Berlin timezone

Simulating morphogen gradients in zebrafish epiboly

2 Oct 2020, 12:00
Main Session (Zoom)

Main Session


Talk Session 5


Justina Stark (Max Planck Institute of Molecular Cell Biology and Genetics)


Studying embryo and tissue morphogenesis is challenging because of the large number of factors influencing each other and the complex and dynamic geometries in which this happens. Computational methods provide a way to disentangle the different processes by gaining control over specific parameters. In this work, we construct a three dimensional (3D) model of a developing zebrafish embryo during epiboly from images. This model is then used to simulate morphogen gradient formation and growth. Both geometry reconstruction and simulation are done using a level set method (LSM) and particle methods (PMs). The LSM facilitates tracking of topological changes, such as cell divisions - an important feature when simulating growth during embryonic development. One advantage of PMs over mesh based numerical schemes is that PMs are more straightforward in handling complex and deforming geometries, as they do not require the generation and maintenance of an (unstructured) mesh. In particular, we apply this method to reconstruct the geometry of the complex-shaped 3D extracellular space of a zebrafish embryo undergoing epiboly. Reconstruction is based on confocal fluorescence microscopy images, which were acquired by Rohit Krishnan Harish from the lab of Prof. Michael Brand (CRTD, TU Dresden). In the subsequent simulation, we aim to numerically reconstitute the coupled processes of cell migration, space deformation, and gradient formation of Fibroblast growth factor 8 (Fgf8) during epiboly, deducing the spatio temporal information from light-sheet microscopy movies and live FCS measurements. We believe that our approach provides a flexible and efficient way for gaining insight into biophysical processes of tissue formation in realistic, complex biological systems by mathematically formulating the information that we can derive from microscopy image data.

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