Stochastic E2F activation and reconciliation of phenomenological cell-cycle models.
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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.
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Published Version (Please cite this version)10.1371/journal.pbio.1000488
Publication InfoLee, 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.
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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.