“Extracting the invisible from live cell microscopy”

Host: Jason Swedlow

Venue: MSI Small Lecture Theatre, SLS

Abstract 

Cell imaging has entered the “big data” era with high content and multidimensional data sets encapsulating complex and dynamic patterns that are inaccessible by human visual observation. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. In this talk, I will present computation-first data-driven approaches to derive new insight and/or generate new hypotheses from cell image data. I will demonstrate the potential of these methods toward application such as modelling intracellular organization, muscle fiber formation, multicellular organization and information processing, and in vitro fertilization.