We study how living systems and their respective viruses encode and decode information about their internal state and their environment. To do so, we combine ideas from cell biology and physics with recent advances in imaging, machine learning, and genomics to make novel measurements.
One of the major computational challenges of analyzing modern imaging experiments is image segmentation, that is determining which parts of a microscope image correspond to which individual cell. Our prior work has demonstrated that deep learning is a natural solution for this problem. We are currently developing the next generation of deep learning-enabled software that can analyze dynamic data from live-cell imaging experiments as well as multi-dimensional data from spatial genomics experiments.
Mammalian cells use dynamics to expand the information encoding capacity of their signaling networks, but how these dynamics are decoded into patterns of gene expression is less clear. A major advance has led to a method to measure signaling dynamics and genome wide gene expression profiles in the same individual cell. We are working to merge live-cell imaging and spatial genomics data to quantify information transmission in signaling networks involved in the anti-viral response.
How do viruses access information about their host cell’s environment and internal state? How does this information flow to key decision points in the viral lifecycle? We are working to answer this question for temperate bacteriophage using functional genomics and high-throughput single-cell imaging.