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The yeast (Saccharomyces cerevisiae) cell cycle has been studied for years, providing us a good knowledge about this cellular process. However, behind this process, there are still complex interactions between genes and proteins that are not fully understood. In this paper, we present a yeast cell cycle modeled by a context-sensitive probabilistic Boolean network (cPBN). The importance of understanding the cell cycle process under this model is that this knowledge may be useful for inferring other gene regulatory networks (cPBNs) from biological data. Furthermore, this work shows an application of the cPBN model for a real biological system.