Stochastic E2F activation and reconciliation of phenomenological cell-cycle models.
Abstract
The transition of the mammalian cell from quiescence to proliferation is a highly
variable process. Over the last four decades, two lines of apparently contradictory,
phenomenological models have been proposed to account for such temporal variability.
These include various forms of the transition probability (TP) model and the growth
control (GC) model, which lack mechanistic details. The GC model was further proposed
as an alternative explanation for the concept of the restriction point, which we recently
demonstrated as being controlled by a bistable Rb-E2F switch. Here, through a combination
of modeling and experiments, we show that these different lines of models in essence
reflect different aspects of stochastic dynamics in cell cycle entry. In particular,
we show that the variable activation of E2F can be described by stochastic activation
of the bistable Rb-E2F switch, which in turn may account for the temporal variability
in cell cycle entry. Moreover, we show that temporal dynamics of E2F activation can
be recast into the frameworks of both the TP model and the GC model via parameter
mapping. This mapping suggests that the two lines of phenomenological models can be
reconciled through the stochastic dynamics of the Rb-E2F switch. It also suggests
a potential utility of the TP or GC models in defining concise, quantitative phenotypes
of cell physiology. This may have implications in classifying cell types or states.
Type
Journal articleSubject
AnimalsBlotting, Western
Cell Cycle
Cell Line
E2F Transcription Factors
Flow Cytometry
Models, Biological
Rats
Stochastic Processes
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https://hdl.handle.net/10161/4447Published Version (Please cite this version)
10.1371/journal.pbio.1000488Publication Info
Lee, Tae J; Yao, Guang; Bennett, Dorothy C; Nevins, Joseph R; & You, Lingchong (2010). Stochastic E2F activation and reconciliation of phenomenological cell-cycle models.
PLoS Biol, 8(9). pp. e1000488. 10.1371/journal.pbio.1000488. Retrieved from https://hdl.handle.net/10161/4447.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Lingchong You
James L. Meriam Distinguished Professor of Biomedical Engineering
The You lab uses a combination of mathematical modeling, machine learning, and quantitative
experiments to elucidate principles underlying the dynamics of microbial communities
in time and space and to control these dynamics for applications in computation, engineering,
and medicine.

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