Uncovering host-virus interactions with imaging-based reverse genetics
Advances in imaging and genomics have increased our ability to capture information in the form of images. Concurrent advances in machine learning have made it easier to extract quantitative information from biological imaging data. Combined, these advances have positioned images to be a universal data type for biology. In this talk, I discuss how these trends in technology can potentially accelerate our understanding of host-virus interactions. Using the latency decision in bacteriophage lambda as a model system, I show how imaging-based reverse genetics can reveal the host-virus interactions that underlie complex aspects of the viral life cycle. I also describe a new technology for performing similar imaging-based studies of mammalian viruses.
David Van Valen is an Assistant Professor in the Division of Biology and Bioengineering at the California Institute of Technology. His research group’s long-term interest is to develop a quantitative understanding of how living systems process, store, and transfer information, and to unravel how this information processing is perturbed in human disease states. To that end, his group leverages—and pioneers—the latest advances in imaging, genomics, and machine learning to produce quantitative measurements with single-cell resolution as well as predictive models of living systems. Prior to joining Caltech, he studied mathematics (BS 2003) and physics (BS 2003) at the Massachusetts Institute of Technology, applied physics (PhD 2011) at the California Institute of Technology, and medicine at the David Geffen School of Medicine at UCLA (MD 2013).