A Predictive Model for Yeast Cell Polarization in Pheromone Gradients
Budding yeast cells exist in two mating types, a and α, which use peptide pheromones to communicate with each other during mating. Mating depends on the ability of cells to polarize up pheromone gradients, but cells also respond to spatially uniform fields of pheromone by polarizing along a single a...
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creator | Muller, Nicolas Piel, Matthieu Calvez, Vincent Voituriez, Raphaël Gonçalves-Sá, Joana Guo, Chin-Lin Jiang, Xingyu Murray, Andrew Meunier, Nicolas |
description | Budding yeast cells exist in two mating types, a and α, which use peptide pheromones to communicate with each other during mating. Mating depends on the ability of cells to polarize up pheromone gradients, but cells also respond to spatially uniform fields of pheromone by polarizing along a single axis. We used quantitative measurements of the response of a cells to α-factor to produce a predictive model of yeast polarization towards a pheromone gradient. We found that cells make a sharp transition between budding cycles and mating induced polarization and that they detect pheromone gradients accurately only over a narrow range of pheromone concentrations corresponding to this transition. We fit all the parameters of the mathematical model by using quantitative data on spontaneous polarization in uniform pheromone concentration. Once these parameters have been computed, and without any further fit, our model quantitatively predicts the yeast cell response to pheromone gradient providing an important step toward understanding how cells communicate with each other. |
doi_str_mv | 10.1371/journal.pcbi.1004795 |
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Mating depends on the ability of cells to polarize up pheromone gradients, but cells also respond to spatially uniform fields of pheromone by polarizing along a single axis. We used quantitative measurements of the response of a cells to α-factor to produce a predictive model of yeast polarization towards a pheromone gradient. We found that cells make a sharp transition between budding cycles and mating induced polarization and that they detect pheromone gradients accurately only over a narrow range of pheromone concentrations corresponding to this transition. We fit all the parameters of the mathematical model by using quantitative data on spontaneous polarization in uniform pheromone concentration. Once these parameters have been computed, and without any further fit, our model quantitatively predicts the yeast cell response to pheromone gradient providing an important step toward understanding how cells communicate with each other.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1004795</identifier><identifier>PMID: 27077831</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Behavior ; Bioinformatics ; Biology and Life Sciences ; Cell division ; Cell interactions ; Cell Polarity - physiology ; Computational Biology ; Computer Science ; Experiments ; Gene expression ; Kinases ; Mathematical models ; Models, Biological ; Observations ; Peptides ; Pheromones ; Pheromones - physiology ; Physiological aspects ; Proteins ; Quorum Sensing - physiology ; Research and Analysis Methods ; Saccharomyces cerevisiae ; Saccharomyces cerevisiae - physiology ; Saccharomyces cerevisiae Proteins - physiology ; Signal Transduction ; Yeast ; Yeasts (Fungi)</subject><ispartof>PLoS computational biology, 2016-04, Vol.12 (4), p.e1004795-e1004795</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Muller N, Piel M, Calvez V, Voituriez R, Gonçalves-Sá J, Guo C-L, et al. (2016) A Predictive Model for Yeast Cell Polarization in Pheromone Gradients. PLoS Comput Biol 12(4): e1004795. doi:10.1371/journal.pcbi.1004795</rights><rights>Attribution</rights><rights>2016 Muller et al 2016 Muller et al</rights><rights>2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Muller N, Piel M, Calvez V, Voituriez R, Gonçalves-Sá J, Guo C-L, et al. (2016) A Predictive Model for Yeast Cell Polarization in Pheromone Gradients. 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Mating depends on the ability of cells to polarize up pheromone gradients, but cells also respond to spatially uniform fields of pheromone by polarizing along a single axis. We used quantitative measurements of the response of a cells to α-factor to produce a predictive model of yeast polarization towards a pheromone gradient. We found that cells make a sharp transition between budding cycles and mating induced polarization and that they detect pheromone gradients accurately only over a narrow range of pheromone concentrations corresponding to this transition. We fit all the parameters of the mathematical model by using quantitative data on spontaneous polarization in uniform pheromone concentration. 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Mating depends on the ability of cells to polarize up pheromone gradients, but cells also respond to spatially uniform fields of pheromone by polarizing along a single axis. We used quantitative measurements of the response of a cells to α-factor to produce a predictive model of yeast polarization towards a pheromone gradient. We found that cells make a sharp transition between budding cycles and mating induced polarization and that they detect pheromone gradients accurately only over a narrow range of pheromone concentrations corresponding to this transition. We fit all the parameters of the mathematical model by using quantitative data on spontaneous polarization in uniform pheromone concentration. 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subjects | Behavior Bioinformatics Biology and Life Sciences Cell division Cell interactions Cell Polarity - physiology Computational Biology Computer Science Experiments Gene expression Kinases Mathematical models Models, Biological Observations Peptides Pheromones Pheromones - physiology Physiological aspects Proteins Quorum Sensing - physiology Research and Analysis Methods Saccharomyces cerevisiae Saccharomyces cerevisiae - physiology Saccharomyces cerevisiae Proteins - physiology Signal Transduction Yeast Yeasts (Fungi) |
title | A Predictive Model for Yeast Cell Polarization in Pheromone Gradients |
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