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Gaudenz Danuser, PhD

Professor, Department of Cell Biology, Harvard Medical School

Contact Info

Gaudenz Danuser
Harvard Medical School
240 Longwood Avenue, LHRRC 301B
Boston, MA, 02115
Phone: 617-432-7941
Gaudenz_Danuser@hms.harvard.edu

Assistant

Not Available.

DF/HCC Program Affiliation

Cancer Cell Biology

Lab Website

Danuser Lab

Research Abstract

Our lab studies how chemical and mechanical signals regulate cytoskeleton organization and dynamics in cell migration and cell division. Characteristic to this integration of diverse signals are complex feedback interactions between pathway components that play over a broad range of temporal and spatial scales. The conventional approach of cell biology, i.e. the molecular perturbation of one component followed by analysis of cell responses, is severely limited in disentangling nonlinear pathways. At best we can learn how the pathway adapts to defects, but it is impossible to gain robust information about the actual functions of any of the components under normal conditions. Thus, our research program is driven by the idea that we should “watch” pathways in as physiological conditions as possible and that interdependency between the pathway components should be reconstructed from the co-variation in their activities over time, space, and repeated experiments.

To this end, our lab develops quantitative live cell microscopy to measure the coordination of activities that contribute to regulating cytoskeleton dynamics. In the past we have made major contributions to imaging technologies that map out with high spatial resolution and in real-time the full dynamics of cellular polymers. Using these methods we study the assembly and turnover of actin, microtubule, and intermediate filament cytoskeleton modules during cell migration and cell division. More recently, we have also begun to “watch” signaling pathways. We collaborate with several labs, which design biosensors for probing signaling activities in living cells. Using such sensors we have shown that the spatiotemporal relationships between signaling components can be identified by computationally combining sequential experiments, each measuring the constitutive variation of one component only. This allows us to reconstruct signaling pathways with many more components than can be imaged simultaneously. The next step in this endeavor will be the combining of measurements of signaling activities with measurements of cytoskeleton dynamics to elucidate their mutual feedback interactions.

Publications

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