Optimality conditions for cell-fate heterogeneity that maximize the effects of growth factors in PC12 cells.

Recently, the heterogeneity that arises from stochastic fate decisions has been reported for several types of cancer-derived cell lines and several types of clonal cells grown under constant environmental conditions. However, the relation between this stochasticity and the responsiveness to extracel...

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Main Authors: Kazunari Mouri, Yasushi Sako
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-01-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC3828137?pdf=render
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spelling doaj-72ccfda23b7843e2a1983e5bd52997dd2020-11-25T02:31:46ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582013-01-01911e100332010.1371/journal.pcbi.1003320Optimality conditions for cell-fate heterogeneity that maximize the effects of growth factors in PC12 cells.Kazunari MouriYasushi SakoRecently, the heterogeneity that arises from stochastic fate decisions has been reported for several types of cancer-derived cell lines and several types of clonal cells grown under constant environmental conditions. However, the relation between this stochasticity and the responsiveness to extracellular stimuli remains largely unknown. Here we focused on the fate decisions of the PC12 cell line, which was derived from rat pheochromocytoma, and is a model system to study differentiation into sympathetic neurons. Whereas epidermal growth factor (EGF) stimulates the proliferation of populations of PC12 cells, nerve growth factor (NGF) promotes the differentiation of neurites to neuron-like cells. We found that phenotypic heterogeneity increased with time at several surrounding serum concentrations, suggesting stochastic cell-fate decisions in single cells. We made a simple mathematical model assuming Markovian transitions of the cell fates, and estimated the transition rates based on Bayes' theorem. The model suggests that depending on the serum concentration, EGF (NGF) even directs differentiation (proliferation) at the single-cell level. The maximum effects of the growth factors were ensured when the transition rates were appropriately controlled by the serum concentration to produce a nonextremal, moderate amount of cell-fate heterogeneity. Our model was validated by the experimental finding that the means and variances of the local cell densities obey a power-law relationship. These results suggest that even when efficient responses to growth factors are observed at the population level, the growth factors stochastically direct the cell-fate decisions in different directions at the single-cell level.http://europepmc.org/articles/PMC3828137?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Kazunari Mouri
Yasushi Sako
spellingShingle Kazunari Mouri
Yasushi Sako
Optimality conditions for cell-fate heterogeneity that maximize the effects of growth factors in PC12 cells.
PLoS Computational Biology
author_facet Kazunari Mouri
Yasushi Sako
author_sort Kazunari Mouri
title Optimality conditions for cell-fate heterogeneity that maximize the effects of growth factors in PC12 cells.
title_short Optimality conditions for cell-fate heterogeneity that maximize the effects of growth factors in PC12 cells.
title_full Optimality conditions for cell-fate heterogeneity that maximize the effects of growth factors in PC12 cells.
title_fullStr Optimality conditions for cell-fate heterogeneity that maximize the effects of growth factors in PC12 cells.
title_full_unstemmed Optimality conditions for cell-fate heterogeneity that maximize the effects of growth factors in PC12 cells.
title_sort optimality conditions for cell-fate heterogeneity that maximize the effects of growth factors in pc12 cells.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2013-01-01
description Recently, the heterogeneity that arises from stochastic fate decisions has been reported for several types of cancer-derived cell lines and several types of clonal cells grown under constant environmental conditions. However, the relation between this stochasticity and the responsiveness to extracellular stimuli remains largely unknown. Here we focused on the fate decisions of the PC12 cell line, which was derived from rat pheochromocytoma, and is a model system to study differentiation into sympathetic neurons. Whereas epidermal growth factor (EGF) stimulates the proliferation of populations of PC12 cells, nerve growth factor (NGF) promotes the differentiation of neurites to neuron-like cells. We found that phenotypic heterogeneity increased with time at several surrounding serum concentrations, suggesting stochastic cell-fate decisions in single cells. We made a simple mathematical model assuming Markovian transitions of the cell fates, and estimated the transition rates based on Bayes' theorem. The model suggests that depending on the serum concentration, EGF (NGF) even directs differentiation (proliferation) at the single-cell level. The maximum effects of the growth factors were ensured when the transition rates were appropriately controlled by the serum concentration to produce a nonextremal, moderate amount of cell-fate heterogeneity. Our model was validated by the experimental finding that the means and variances of the local cell densities obey a power-law relationship. These results suggest that even when efficient responses to growth factors are observed at the population level, the growth factors stochastically direct the cell-fate decisions in different directions at the single-cell level.
url http://europepmc.org/articles/PMC3828137?pdf=render
work_keys_str_mv AT kazunarimouri optimalityconditionsforcellfateheterogeneitythatmaximizetheeffectsofgrowthfactorsinpc12cells
AT yasushisako optimalityconditionsforcellfateheterogeneitythatmaximizetheeffectsofgrowthfactorsinpc12cells
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