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Mathematical investigation of IP3-dependent calcium dynamics in astrocytes

Overview of attention for article published in Journal of Computational Neuroscience, March 2017
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Title
Mathematical investigation of IP3-dependent calcium dynamics in astrocytes
Published in
Journal of Computational Neuroscience, March 2017
DOI 10.1007/s10827-017-0640-1
Pubmed ID
Authors

Gregory Handy, Marsa Taheri, John A. White, Alla Borisyuk

Abstract

We study evoked calcium dynamics in astrocytes, a major cell type in the mammalian brain. Experimental evidence has shown that such dynamics are highly variable between different trials, cells, and cell subcompartments. Here we present a qualitative analysis of a recent mathematical model of astrocyte calcium responses. We show how the major response types are generated in the model as a result of the underlying bifurcation structure. By varying key channel parameters, mimicking blockers used by experimentalists, we manipulate this underlying bifurcation structure and predict how the distributions of responses can change. We find that store-operated calcium channels, plasma membrane bound channels with little activity during calcium transients, have a surprisingly strong effect, underscoring the importance of considering these channels in both experiments and mathematical settings. Variation in the maximum flow in different calcium channels is also shown to determine the range of stable oscillations, as well as set the range of frequencies of the oscillations. Further, by conducting a randomized search through the parameter space and recording the resulting calcium responses, we create a database that can be used by experimentalists to help estimate the underlying channel distribution of their cells.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 17%
Student > Bachelor 7 15%
Student > Master 7 15%
Researcher 7 15%
Professor 3 7%
Other 6 13%
Unknown 8 17%
Readers by discipline Count As %
Neuroscience 11 24%
Biochemistry, Genetics and Molecular Biology 6 13%
Mathematics 5 11%
Agricultural and Biological Sciences 5 11%
Engineering 3 7%
Other 7 15%
Unknown 9 20%
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 17 May 2017.
All research outputs
#20,420,242
of 22,971,207 outputs
Outputs from Journal of Computational Neuroscience
#265
of 308 outputs
Outputs of similar age
#268,913
of 308,492 outputs
Outputs of similar age from Journal of Computational Neuroscience
#3
of 4 outputs
Altmetric has tracked 22,971,207 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 308 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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