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An Integrative Model of Ion Regulation in Yeast

Overview of attention for article published in PLoS Computational Biology, January 2013
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Title
An Integrative Model of Ion Regulation in Yeast
Published in
PLoS Computational Biology, January 2013
DOI 10.1371/journal.pcbi.1002879
Pubmed ID
Authors

Ruian Ke, Piers J. Ingram, Ken Haynes

Abstract

Yeast cells are able to tolerate and adapt to a variety of environmental stresses. An essential aspect of stress adaptation is the regulation of monovalent ion concentrations. Ion regulation determines many fundamental physiological parameters, such as cell volume, membrane potential, and intracellular pH. It is achieved through the concerted activities of multiple cellular components, including ion transporters and signaling molecules, on both short and long time scales. Although each component has been studied in detail previously, it remains unclear how the physiological parameters are maintained and regulated by the concerted action of all components under a diverse range of stress conditions. In this study, we have constructed an integrated mathematical model of ion regulation in Saccharomyces cerevisiae to understand this coordinated adaptation process. Using this model, we first predict that the interaction between phosphorylated Hog1p and Tok1p at the plasma membrane inhibits Tok1p activity and consequently reduces Na(+) influx under NaCl stress. We further characterize the impacts of NaCl, sorbitol, KCl and alkaline pH stresses on the cellular physiology and the differences between the cellular responses to these stresses. We predict that the calcineurin pathway is essential for maintaining a non-toxic level of intracellular Na(+) in the long-term adaptation to NaCl stress, but that its activation is not required for maintaining a low level of Na(+) under other stresses investigated. We provide evidence that, in addition to extrusion of toxic ions, Ena1p plays an important role, in some cases alongside Nha1p, in re-establishing membrane potential after stress perturbation. To conclude, this model serves as a powerful tool for both understanding the complex system-level properties of the highly coordinated adaptation process and generating further hypotheses for experimental investigation.

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Geographical breakdown

Country Count As %
Spain 1 1%
Portugal 1 1%
Norway 1 1%
Unknown 97 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 22%
Researcher 20 20%
Student > Bachelor 12 12%
Professor 7 7%
Professor > Associate Professor 6 6%
Other 18 18%
Unknown 15 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 39%
Biochemistry, Genetics and Molecular Biology 20 20%
Engineering 5 5%
Chemistry 4 4%
Environmental Science 3 3%
Other 10 10%
Unknown 19 19%
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 18 January 2013.
All research outputs
#20,656,820
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#8,208
of 8,960 outputs
Outputs of similar age
#231,335
of 292,501 outputs
Outputs of similar age from PLoS Computational Biology
#107
of 127 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
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We're also able to compare this research output to 127 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.