Parameter Assessment for a Stochastic Cell Cycle Model

The eukaryotic cell cycle is probabilistic by nature. Disruption in normal cell cycle operation can lead to a multitude of problems, including cancer.  An accurate, stochastic model of the eukaryotic cell cycle would provide new insights into cell cycle regulation and how environmental perturbations can adversely affect its normal progression. These insights could be used to combat illnesses associated with the cell cycle malfunction.

We have used a stochastic kinetic model of the chemical oscillator which drives the eukaryotic cell cycle to carry out Monte Carlo simulations of cell cycle progression, and to model the effects of various drug treatment regimens on cell populations. Drugs are assumed to inhibit specified reactions in the model and different cell types can be simulated using different sets of model parameters. Different drug removal scenarios, such as excretion through kidneys or metabolic breakdown of the drug are easily included in the model.

To facilitate determination of model parameter values, we have created fusion proteins in which green fluorescent protein is tethered to various cell cycle regulated proteins.  The fluorescence intensity was quantified and used to improve initial parameter estimations, making our model more biologically realistic.  This improved model allows us to better screen putative drug targets and treatment protocols and will ultimately enable us to propose clinical drug tests based on the model’s predictions.