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Yeast Membrane Transport

Overview of attention for book
Attention for Chapter 12: Mathematical Modelling of Cation Transport and Regulation in Yeast.
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Chapter title
Mathematical Modelling of Cation Transport and Regulation in Yeast.
Chapter number 12
Book title
Yeast Membrane Transport
Published in
Advances in experimental medicine and biology, January 2016
DOI 10.1007/978-3-319-25304-6_12
Pubmed ID
Book ISBNs
978-3-31-925302-2, 978-3-31-925304-6
Authors

Kahm, Matthiasé, Kschischo, Maik, Matthiasé Kahm, Maik Kschischo

Abstract

Mathematical modelling of ion transport is a strategy to understand the complex interplay between various ionic species and their transporters. Such models should provide new insights and suggest new interesting experiments. Two essential variables in models for ion transport and control are the membrane potential and the intracellular pH, which generates an additional layer of complexity absent from many other models of biochemical reaction pathways. The aim of this text is to introduce the reader to the basic principles and assumptions of modelling in this field. A simplified model of potassium transport will be used as an example and will be derived in a step by step manner. This forms the basis for understanding the advantages and limitations of more complex models. These are briefly reviewed at the end of this chapter.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 42%
Student > Doctoral Student 2 17%
Professor 2 17%
Student > Bachelor 1 8%
Researcher 1 8%
Other 1 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 42%
Biochemistry, Genetics and Molecular Biology 4 33%
Chemical Engineering 1 8%
Chemistry 1 8%
Unknown 1 8%
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 02 January 2016.
All research outputs
#20,299,108
of 22,836,570 outputs
Outputs from Advances in experimental medicine and biology
#3,969
of 4,951 outputs
Outputs of similar age
#330,608
of 393,564 outputs
Outputs of similar age from Advances in experimental medicine and biology
#334
of 443 outputs
Altmetric has tracked 22,836,570 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 4,951 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 393,564 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 443 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.