Rong Li, Johns Hopkins University, “Cellular Dynamics in Space, Time and Evolutionary Adaptation”

December 1, 2015 @ 11:00 am – 12:00 pm
Levering Hall, Sherwood Room
Johns Hopkins University
Baltimore, MD 21218

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“Cellular Dynamics in Space, Time and Evolutionary Adaptation”

Rong-Li-711x1024Rong Li graduated from Yale with combined BS and MS in Molecular Biophysics and Biochemistry.  After Ph.D. at UCSF and postdoc at UC Berkeley, she became Assistant Professor at Harvard Medical School in 1994 and was later promoted to Associate Professor.  She was recruited to Stowers Institute as Investigator in 2005. In 2015, Rong Li came to Johns Hopkins as Bloomberg Distinguished Professor in Department of Cell Biology, School of Medicine, and Department of Chemical and Biomolecular Engineering, School of Engineering. She is also Director of Center for Cell Dynamics in Institute of Basic Biomedical Sciences.  Rong Li takes an interdisciplinary approach to study the biology of the cell and has made seminal discoveries in mitosis regulation, cell polarity, cytoskeleton dynamics, and cellular evolution.

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Cellular Dynamics in Space, Time and Evolutionary Adaptation

talk-titleEukaryotic cells orchestrate precisely patterned cell divisions through dynamic movements of intracellular structures of diverse functions and scales. Movement and asymmetric positioning of the meiotic spindle driven by cytoskeleton-based forces enables oocyte maturation and successful fertilization, whereas movement of damaged-protein aggregates constrained by organelles facilitates cellular aging asymmetry. Most remarkable of all, mechanisms of cell division and proliferation adapt dynamically to a broad range of stress conditions through mitotic infidelity and the resulting large-scale chromosome dosage imbalance. Our recent work employs genetic models and combines high-resolution imaging, computational analysis and mathematical models to decipher the mechanisms and principles governing cellular dynamics in space, time and adaptation.

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JHU - Institute for Computational Medicine