Data-driven modeling across scales - from cytoskeleton to bacterial swarms to multicellular motility to angiogenesis
Monday, June 14 at 09:30am (PDT)Monday, June 14 at 05:30pm (BST)Tuesday, June 15 01:30am (KST)
Alex Mogilner (NYU, United States), Angelika Manhart (UCL, UK)
Mathematical modeling in cell and developmental biology is thriving. Traditional continuum PDEs' modeling, and discrete particle-based simulations brought quantitative insight and generated testable predictions for experiment. Recently, novel experimental techniques started to yield great volumes of quantitative data, opening new modeling possibilities. To exploit these possibilities, modelers started to combine the traditional mathematical approaches with novel data science tools, including topological data analysis, machine learning, homology analysis and unsupervised feature classification. Four talks will demonstrate how combining the traditional and novel mathematical approaches allows to build predictive models from data. The talks span biological scales, from subcellular (cytoskeletal dynamics), to supercellular (bacterial swarming, collective cell migration, and angiogenesis).