Deterministic formulations have been extensively used over the past decades to model the cell cycle. This approach, however, is inadequate for intracellular reaction phenomena occurring in control volumes on the order of some pico-liters: the probabilistic fashion in which reactions occur in every single cell creates significant random fluctuations, termed “intrinsic noise”. Therefore, the underlying dynamics of transcription and translation, the protein-protein and protein-ligand interactions, as well as the degradation phenomena, must be described in probabilistic terms, with the use of stochastic models. Taking into account this inherent intracellular stochasticity can reveal patterns in the behavior of a biochemical network that are different from those predicted by a deterministic model. In this work, we have developed a reaction network that mimics the salient processes involved in cell cycle. The network incorporates a positive and a negative feedback loop: the positive feedback stems from the activation of a cyclin-dependent kinase by a cyclin and the subsequent autocatalytic effect in the expression of cyclins. The negative feedback arises from the promotion of S-phase inhibitors' degradation by cyclin-CDK complexes. From the reaction network we derive equivalent deterministic and stochastic models and compare the predictions of the two in order to reveal the effect of stochasticity in the behavior of the system. Comparisons are performed in different regions of the parameter space, using a bifurcation parameter that quantifies the strength of the positive feedback. The comparisons reveal that stochasticity can convert deterministic excitability to stochastic oscillations thereby increasing the range of the bifurcation parameter where the system exhibits oscillatory behavior. This effect may have a physiological importance since oscillations are essential for the function of the cell cycle.

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