Valen, David Van - California Institute of Technology, Caltech - Division of Biology and Biological Engineering

个人简介

B.S., Massachusetts Institute of Technology, 2003; Ph.D., Caltech, 2011; M.D., University of California Los Angeles, 2013; Visiting Associate, Caltech, 2017-18; Research Assistant Professor, 2018; Assistant Professor, 2018-.

研究领域

Biological Engineering; Systems Biology

近期论文

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Our group studies how living systems and their respective viruses encode and decode information about their internal state and their environment by combining ideas from cell biology and physics with recent advances in imaging, machine learning, and genomics to make novel measurements. Active research directions in our group include: Host-virus interactions during viral decision making 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 answering this question for temperate bacteriophage using functional genomics and high-throughput single-cell imaging. Deep learning for single-cell biology 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 algorithms that can analyze dynamic data from live-cell imaging experiments as well as multi-dimensional data from spatial genomics experiments. Integrated measurements of signaling and gene expression 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. Recent technological advances have made it possible 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.