Intersections between data science and developmental biology, from images to cross-view kernel transfer

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Date(s) - 12/04/2019
14 h 00 min - 15 h 00 min

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Embryogenesis is the process by which a single fertilized cell is turned into a multi-cellular organism. It is a process involving coordinated dynamics at multiple scales, from single molecules, to cells, to tissues. Dynamical processes in biology are studied using an ever-increasing number of microscopy techniques, each of which brings out unique features of the system. What can we learn from partial measurements of a developing embryo? How can we detect reproducible behaviors in 1000s of cells? How accurately can we predict the behavior of individual cells? This requires developing acquisition tools, integrating heterogeneous measurements, developing pattern recognition algorithms and inventing predictive theories. The techniques that we are using stem from the fields of multi-dimensional statistics, machine learning, image processing, complex systems and data visualization. We will illustrate our approaches with specific examples using datasets from studies of development in the sea urchin early embryogenesis, drosophila pattern formation and oogenesis.


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