A Predictive Model for Yeast Cell Polarization in Pheromone Gradients
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CitationMuller, Nicolas, Matthieu Piel, Vincent Calvez, Raphaël Voituriez, Joana Gonçalves-Sá, Chin-Lin Guo, Xingyu Jiang, Andrew Murray, and Nicolas Meunier. 2016. “A Predictive Model for Yeast Cell Polarization in Pheromone Gradients.” PLoS Computational Biology 12 (4): e1004795. doi:10.1371/journal.pcbi.1004795. http://dx.doi.org/10.1371/journal.pcbi.1004795.
AbstractBudding 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.
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