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Theoretical modeling on neurofilament transport near the Node of Ranvier

11/24 2022 Seminar
  • Title Theoretical modeling on neurofilament transport near the Node of Ranvier
  • Speaker Li Yinyun（ Beijing Normal University）
  • Date Nov.24,2022 14:30-16:00
  • Venue 322,North Building https://meeting.tencent.com/dm/7pQdUVZZ3wvv# Tencent：603-434-755 Password：7871
    • Abstract
    Neurofilaments (NFs) are one of the most abundant cytoskeletal filaments in axon and its population shapes the caliber of axon. However, NFs are not static; they are undergoing “slow axonal transport”, which is characterized by rapid, intermittent, bidirectional motion. The average transport velocity is extremely low; however, the time-lapse imaging showed that the instant speed is much faster. A. Brown and P. Jung hypothesized that NFs are undergoing “stop-and-go” kinetics and provided a ‘6-state’ model. This model can fully explain the short time behavior of single NFs and “bell-shaped” wave for a group of NFs. Here in this presentation, I will show that how NFs transport is correlated with the axonal morphology near the Node of Ranvier. Our theory can decipher differential NF kinetics and predict the morphology difference near the Node of Ranvier.

     

    Yinyun Li(李印赟), Ph. D, Assistant Professor in School of Systems Science, Beijing Normal University. She obtained Ph. D in Department of Physics and Astronomy in Ohio University, USA, and had her postdoc training in the III physics institute-biophysics in Goerg-August University Goettingen, Germany. Trained as a physicist, her interest lies in the interdisciplinary research of biophysics, neuroscience by using physical laws and principles. She has published a series papers in The Journal of Neuroscience, Physical Biology, Journal of Computational Neuroscience. The aim is understand the basic mechanism of how bio-systems are functioning in subcellular, cellular and neural circuity levels, which can help us understand brain function as well as neurodegenerative diseases, and develop artificial intelligence. 

     

    Contact: Zhou Haijun