The Guignard team uses computer science to study developmental biology, with a specific focus on embryogenesis at the single-cell scale in various developing organisms. They develop novel computer vision, graph theory, machine learning, and big data science algorithms to quantitatively understand developmental reproducibility during embryogenesis in a large variety of model organisms.
Their primary objective is to explore the impact of developmental variability on morphogenesis and species adaptation. To achieve this, the team combines 3D fluorescence microscopy movies with spatial transcriptomic data, merging these extensive and diverse modalities to construct comprehensive statistical representations of embryogenesis.
