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What Is Required for Neuronal Calcium Waves? A Numerical Parameter Study

Overview of attention for article published in The Journal of Mathematical Neuroscience, July 2018
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Title
What Is Required for Neuronal Calcium Waves? A Numerical Parameter Study
Published in
The Journal of Mathematical Neuroscience, July 2018
DOI 10.1186/s13408-018-0064-x
Pubmed ID
Authors

Markus Breit, Gillian Queisser

Abstract

Neuronal calcium signals propagating by simple diffusion and reaction with mobile and stationary buffers are limited to cellular microdomains. The distance intracellular calcium signals can travel may be significantly increased by means of calcium-induced calcium release from internal calcium stores, notably the endoplasmic reticulum. The organelle, which can be thought of as a cell-within-a-cell, is able to sequester large amounts of cytosolic calcium ions via SERCA pumps and selectively release them into the cytosol through ryanodine receptor channels leading to the formation of calcium waves. In this study, we set out to investigate the basic properties of such dendritic calcium waves and how they depend on the three parameters dendrite radius, ER radius and ryanodine receptor density in the endoplasmic membrane. We demonstrate that there are stable and abortive regimes for calcium waves, depending on the above morphological and physiological parameters. In stable regimes, calcium waves can travel across long dendritic distances, similar to electrical action potentials. We further observe that abortive regimes exist, which could be relevant for spike-timing dependent plasticity, as travel distances and wave velocities vary with changing intracellular architecture. For some of these regimes, analytic functions could be derived that fit the simulation data. In parameter spaces, that are non-trivially influenced by the three-dimensional calcium concentration profile, we were not able to derive such a functional description, demonstrating the mathematical requirement to model and simulate biochemical signaling in three-dimensional space.

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Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Student > Bachelor 5 17%
Researcher 4 13%
Student > Master 3 10%
Professor 2 7%
Other 3 10%
Unknown 6 20%
Readers by discipline Count As %
Neuroscience 6 20%
Computer Science 4 13%
Biochemistry, Genetics and Molecular Biology 2 7%
Mathematics 2 7%
Medicine and Dentistry 2 7%
Other 5 17%
Unknown 9 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 August 2018.
All research outputs
#17,982,872
of 23,096,849 outputs
Outputs from The Journal of Mathematical Neuroscience
#52
of 80 outputs
Outputs of similar age
#236,171
of 327,048 outputs
Outputs of similar age from The Journal of Mathematical Neuroscience
#2
of 4 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 80 research outputs from this source. They receive a mean Attention Score of 2.5. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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