The huge datasets produced from high-throughput microarray technology can elucidate unknown mechanisms of gene regulation in biological systems. Because biological processes are dynamic, it is relevant to focus on certain condition-specific gene regulatory sub-networks. The cell cycle is a basic cellular process, thus, identifying cell cycle specific regulatory sub-networks in yeast will provide a basis for understanding the cell cycle and may be important in other cellular conditions. With a gene expression differential equation model (GEDEM), dynamic cell cycle-related regulatory relationships were indentified from a static regulatory network. Compared to cell cycle-related regulatory interactions previously published, this method identified more true regulatory relationships and show higher performance than other methods. On larger datasets, the GEDEM identified regulatory sub-networks with high sensitivity and specificity. Further analysis on combinatorial regulation revealed that condition-specific regulatory sub-networks exhibited more significant correlations between transcription factors than previously implied in static network analyses, which infer that the condition-specific sub-networks are closer to reality than static network. Additionally, the GEDEM identified more potential co-regulatory transcriptional factors in the cell cycle.