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    TIME RESOLVED STUDIES OF TRANSPORT IN RENAL EPITHELIAL CELLS

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    Date
    2011-04-18
    Author
    HUA, ZONGLU S Principal Investigator
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    Abstract
    DESCRIPTION (provided by applicant): My educational background is in physics (B. S. and M.S.) and materials engineering (PhD). My academic research over the last five years has been focused on both ballistic electron transport and microfluidics. I built a microfabricated microfluidic sensor to measure cell volume in real time, and this project caused me to become interested in biomedical sciences. My immediate goal is to train in biology so I may become an independent researcher in kidney biophysics. My long-term goal is to apply my knowledge of physics and materials science to the kidney and hopefully to make a contribution to treating kidney diseases. My current knowledge of biology has come from self-education and is thus rather narrow. I want a more formal training with didactic courses, hands-on laboratory experience and attendance at scientific meetings. I will do research on the regulation of renal cell volume with Dr. Frederick Sachs as mentor. Dr. Sachs is an expert in biophysics of cells, particularly mechanical transduction. Under his guidance I will study the biophysics of how renal cells respond to osmotic and chemical challenges. The research will use the new volume sensor chip. I have five specific goals in this project: 1) optimize the technology of the sensor for minimal fluid exchange time, temperature regulation and stability, 2) measure the dynamics of cell volume regulation in cultured renal epithelial cells; correlate the volume changes with the concentration of intracellular ions using fluorescence microscopy, 3) partition the solute and water flux into separate components using specific pharmacological interventions, 4) to examine the effect of tubular fluid flow and shear stress on epithelial cell volume with particular relevance to polycystic kidney disease, and 5) to build mathematical models of volume regulation to extract physically relevant parameters from the data.
    URI
    http://hdl.handle.net/10477/936
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