Document Detail

Age-dependent stochastic models for understanding population fluctuations in continuously cultured cells.
MedLine Citation:
PMID:  23760298     Owner:  NLM     Status:  MEDLINE    
For symmetrically dividing cells, large variations in the cell cycle time are typical, even among clonal cells. The consequence of this variation is important in stem cell differentiation, tissue and organ size control, and cancer development, where cell division rates ultimately determine the cell population. We explore the connection between cell cycle time variation and population-level fluctuations using simple stochastic models. We find that standard population models with constant division and death rates fail to predict the level of population fluctuation. Instead, variations in the cell division time contribute to population fluctuations. An age-dependent birth and death model allows us to compute the mean squared fluctuation or the population dispersion as a function of time. This dispersion grows exponentially with time, but scales with the population. We also find a relationship between the dispersion and the cell cycle time distribution for synchronized cell populations. The model can easily be generalized to study populations involving cell differentiation and competitive growth situations.
Evgeny B Stukalin; Ivie Aifuwa; Jin Seob Kim; Denis Wirtz; Sean X Sun
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2013-06-12
Journal Detail:
Title:  Journal of the Royal Society, Interface / the Royal Society     Volume:  10     ISSN:  1742-5662     ISO Abbreviation:  J R Soc Interface     Publication Date:  2013 Aug 
Date Detail:
Created Date:  2013-06-13     Completed Date:  2013-12-09     Revised Date:  2014-08-07    
Medline Journal Info:
Nlm Unique ID:  101217269     Medline TA:  J R Soc Interface     Country:  England    
Other Details:
Languages:  eng     Pagination:  20130325     Citation Subset:  IM    
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MeSH Terms
Cell Aging / physiology*
Cell Proliferation*
Models, Biological*
Stem Cells / cytology,  physiology*
Stochastic Processes
Grant Support

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