Abstract
In this study, we focus on a recent stochastic budding yeast cell cycle model. First, we estimate the model parameters using extensive data sets: phenotypes of 110 genetic strains, single cell statistics of wild type and cln3 strains. Optimization of stochastic model parameters is achieved by an automated algorithm we recently used for a deterministic cell cycle model. Next, in order to test the predictive ability of the stochastic model, we focus on a recent experimental study in which forced periodic expression of CLN2 cyclin (driven by MET3 promoter in cln3 background) has been used to synchronize budding yeast cell colonies. We demonstrate that the model correctly predicts the experimentally observed synchronization levels and cell cycle statistics of mother and daughter cells under various experimental conditions (numerical data that is not enforced in parameter optimization), in addition to correctly predicting the qualitative changes in size control due to forced CLN2 expression. Our model also generates a novel prediction: under frequent CLN2 expression pulses, G1 phase duration is bimodal among small-born cells. These cells originate from daughters with extended budded periods due to size control during the budded period. This novel prediction and the experimental trends captured by the model illustrate the interplay between cell cycle dynamics, synchronization of cell colonies, and size control in budding yeast.
Highlights
A major objective in systems biology is the development of predictive mathematical models
Varying the period of CLN2 expression pulses Figure 3B shows that 78 min period pulses result in higher synchrony than 90 and 69 min period pulses (Figures 3A and 3C), whereas without any pulse cell populations lack synchrony: the budding index settles around 0.5 after about 300 min
We use a stochastic differential equation model to explore the potential of periodically forced expression of CLN2 cyclin to synchronize the cell division cycle of budding yeast cells
Summary
A major objective in systems biology is the development of predictive mathematical models. This allows researchers to test hypotheses and guides future experimental studies. The combined use of mathematical models and experiments can impact real life applications, such as drug discovery, when the models can accurately predict the changes in the behavior of an organism under specific perturbations. The particular model structure used in a study is largely determined by the existing experimental data that needs to be incorporated into the model and the kinds of predictions one intends to make. Deterministic models are ideal for reproducing population averaged experimental observations, such as Western blot data. One resorts to stochastic models to describe noisy gene expression patterns and behaviors of heterogeneous cell populations
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