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Evaluation of predictions of the stochastic model of organelle production based on exact distributions

We present a reanalysis of the stochastic model of organelle production and show that the equilibrium distributions for the organelle numbers predicted by this model can be readily calculated in three different scenarios. These three distributions can be identified as standard distributions, and the...

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Autor principal: Craven, C Jeremy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4733043/
https://www.ncbi.nlm.nih.gov/pubmed/26783763
http://dx.doi.org/10.7554/eLife.10167
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author Craven, C Jeremy
author_facet Craven, C Jeremy
author_sort Craven, C Jeremy
collection PubMed
description We present a reanalysis of the stochastic model of organelle production and show that the equilibrium distributions for the organelle numbers predicted by this model can be readily calculated in three different scenarios. These three distributions can be identified as standard distributions, and the corresponding exact formulae for their mean and variance can therefore be used in further analysis. This removes the need to rely on stochastic simulations or approximate formulae (derived using the fluctuation dissipation theorem). These calculations allow for further analysis of the predictions of the model. On the basis of this we question the extent to which the model can be used to conclude that peroxisome biogenesis is dominated by de novo production when Saccharomyces cerevisiae cells are grown on glucose medium. DOI: http://dx.doi.org/10.7554/eLife.10167.001
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spelling pubmed-47330432016-03-17 Evaluation of predictions of the stochastic model of organelle production based on exact distributions Craven, C Jeremy eLife Cell Biology We present a reanalysis of the stochastic model of organelle production and show that the equilibrium distributions for the organelle numbers predicted by this model can be readily calculated in three different scenarios. These three distributions can be identified as standard distributions, and the corresponding exact formulae for their mean and variance can therefore be used in further analysis. This removes the need to rely on stochastic simulations or approximate formulae (derived using the fluctuation dissipation theorem). These calculations allow for further analysis of the predictions of the model. On the basis of this we question the extent to which the model can be used to conclude that peroxisome biogenesis is dominated by de novo production when Saccharomyces cerevisiae cells are grown on glucose medium. DOI: http://dx.doi.org/10.7554/eLife.10167.001 eLife Sciences Publications, Ltd 2016-01-14 /pmc/articles/PMC4733043/ /pubmed/26783763 http://dx.doi.org/10.7554/eLife.10167 Text en © 2016, Craven et al http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Cell Biology
Craven, C Jeremy
Evaluation of predictions of the stochastic model of organelle production based on exact distributions
title Evaluation of predictions of the stochastic model of organelle production based on exact distributions
title_full Evaluation of predictions of the stochastic model of organelle production based on exact distributions
title_fullStr Evaluation of predictions of the stochastic model of organelle production based on exact distributions
title_full_unstemmed Evaluation of predictions of the stochastic model of organelle production based on exact distributions
title_short Evaluation of predictions of the stochastic model of organelle production based on exact distributions
title_sort evaluation of predictions of the stochastic model of organelle production based on exact distributions
topic Cell Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4733043/
https://www.ncbi.nlm.nih.gov/pubmed/26783763
http://dx.doi.org/10.7554/eLife.10167
work_keys_str_mv AT cravencjeremy evaluationofpredictionsofthestochasticmodeloforganelleproductionbasedonexactdistributions