A Tridomain Model for Potassium Clearance in Optic Nerve of Necturus.
Abstract
Complex fluids flow in complex ways in complex structures. Transport of water and
various organic and inorganic molecules in the central nervous system are important
in a wide range of biological and medical processes [C. Nicholson, and S. Hrabetova,
Biophysical Journal, 113(10), 2133(2017)]. However, the exact driving mechanisms are
often not known. In this paper, we investigate flows induced by action potentials
in an optic nerve as a prototype of the central nervous system (CNS). Different from
traditional fluid dynamics problems, flows in biological tissues such as the CNS are
coupled with ion transport. It is driven by osmosis created by concentration gradient
of ionic solutions, which in term influence the transport of ions. Our mathematical
model is based on the known structural and biophysical properties of the experimental
system used by the Harvard group Orkand et al [R.K. Orkand, J.G. Nicholls, S.W. Kuffler,
Journal of Neurophysiology, 29(4), 788(1966)]. Asymptotic analysis and numerical computation
show the significant role of water in convective ion transport. The full model (including
water) and the electrodiffusion model (excluding water) are compared in detail to
reveal an interesting interplay between water and ion transport. In the full model,
convection due to water flow dominates inside the glial domain. This water flow in
the glia contributes significantly to the spatial buffering of potassium in the extracellular
space. Convection in the extracellular domain does not contribute significantly to
spatial buffering. Electrodiffusion is the dominant mechanism for flows confined to
the extracellular domain.
Type
Journal articlePermalink
https://hdl.handle.net/10161/23458Published Version (Please cite this version)
10.1016/j.bpj.2021.06.020Publication Info
Zhu, Yi; Xu, Shixin; Eisenberg, Robert S; & Huang, Huaxiong (2021). A Tridomain Model for Potassium Clearance in Optic Nerve of Necturus. Biophysical journal. 10.1016/j.bpj.2021.06.020. Retrieved from https://hdl.handle.net/10161/23458.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Shixin Xu
Assistant Professor of Mathematics at Duke Kunshan University
Shixin Xu is an Assistant Professor of Mathematics. His research interests are machine
learning and data-driven model for diseases, multiscale modeling of complex fluids,
Neurovascular coupling, homogenization theory, and numerical analysis. The current
projects he is working on are
image data-based for the prediction of hemorrhagic transformation in acute ischemic
stroke,
electrodynamics modeling of saltatory conduction along myelina

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