Machine learning based analysis of cell and tissue dynamics / Weijer & Sknepnek / Dundee UK
We are pleased to announce a fully funded interdisciplinary PhD position in “Machine learning based analysis of cell and tissue dynamics during gastrulation” at the School of Life Sciences, University of Dundee, United Kingdom. The position is funded through the BBSRC EASTBIO programme and is limited to the UK/EU applicants only.
This aim of this highly interdisciplinary PhD project is to combine state-of-the-art machine learning and artificial intelligence (AI) based image analysis with sophisticated biophysical modelling to quantitatively analyse the cell behaviours that drive gastrulation in the chick embryo, a model system for human development.
This project will focus on the detailed analysis of key changes in cells behaviours of the epithelial epiblast cells that drive the formation of the primitive streak, their ingression through the streak and their subsequent movement inside the embryo to form different organs. This will involve analysis of distinct cell behaviours of fluorescently labelled cells during normal development and under conditions where candidate cell-cell signalling systems coordinating these behaviours have been disturbed through molecular genetic and direct mechanical manipulations.
To image the over 200,000 cells in the gastrulation stage chick embryo we have designed and built a unique and highly innovative Fluorescence Light Sheet Microscope that for first time has allowed the detailed live imaging the all the complex cell behaviours, i.e. cell shape change, division, movement and ingression in the developing chick embryos where the membranes have been labelled with green fluorescent proteins. This method is used in combination with high-resolution multi-photon confocal microscopy to study how individual cell behaviours are integrated in the context to produce more complex tissues during gastrulation. These experiments produce enormous quantities of high-quality image data (>2TB/experiment) that require a detailed and sophisticated computational analysis. Therefore this project will make extensive use of advanced computational image processing and AI based data analysis methods.
Analysis and interpretation of the data will be supported by detailed modelling of gastrulation as collective cell motion using concepts and methods from the physics of soft and active matter: The PhD candidate will closely collaborate with both the experimental (Weijer) and theoretical (Sknepnek) groups. This highly interdisciplinary project will provide training in state-of-the-art machine learning methods for image analysis, advanced cell and developmental biology, molecular genetics, live imaging using advanced lightsheet and Multiphoton confocal microscopy as well as provide the opportunity to learn/use advanced biophysical and mathematical modelling techniques.
If you know a highly motivated undergraduate with background in physics or applied mathematics who would be interested in working an interdisciplinary team, could you please kindly ask them to contact either Prof Kees Weijer (c.j.weijer /at/ dundee.ac.uk <email@example.com>) or Dr Rastko Sknepnek (r.sknepnek /at/ dundee.ac.uk <firstname.lastname@example.org>) for further details.
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