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Potassium Starvation in Yeast: Mechanisms of Homeostasis Revealed by Mathematical Modeling

Overview of attention for article published in PLoS Computational Biology, June 2012
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Title
Potassium Starvation in Yeast: Mechanisms of Homeostasis Revealed by Mathematical Modeling
Published in
PLoS Computational Biology, June 2012
DOI 10.1371/journal.pcbi.1002548
Pubmed ID
Authors

Matthias Kahm, Clara Navarrete, Vicent Llopis-Torregrosa, Rito Herrera, Lina Barreto, Lynne Yenush, Joaquin Ariño, Jose Ramos, Maik Kschischo

Abstract

The intrinsic ability of cells to adapt to a wide range of environmental conditions is a fundamental process required for survival. Potassium is the most abundant cation in living cells and is required for essential cellular processes, including the regulation of cell volume, pH and protein synthesis. Yeast cells can grow from low micromolar to molar potassium concentrations and utilize sophisticated control mechanisms to keep the internal potassium concentration in a viable range. We developed a mathematical model for Saccharomyces cerevisiae to explore the complex interplay between biophysical forces and molecular regulation facilitating potassium homeostasis. By using a novel inference method ("the reverse tracking algorithm") we predicted and then verified experimentally that the main regulators under conditions of potassium starvation are proton fluxes responding to changes of potassium concentrations. In contrast to the prevailing view, we show that regulation of the main potassium transport systems (Trk1,2 and Nha1) in the plasma membrane is not sufficient to achieve homeostasis.

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Geographical breakdown

Country Count As %
Germany 1 1%
Unknown 71 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 19%
Researcher 14 19%
Student > Bachelor 9 13%
Professor 7 10%
Professor > Associate Professor 5 7%
Other 12 17%
Unknown 11 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 38%
Biochemistry, Genetics and Molecular Biology 14 19%
Chemistry 4 6%
Physics and Astronomy 3 4%
Computer Science 2 3%
Other 9 13%
Unknown 13 18%