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中文摘要：
由高通量微阵列技术产生的数据集可以用于解释生物系统基因调控的未知机制.生物过程是动态的,所以很有必要关注某些条件下特异的基因调控子网络.细胞周期是一个基本的细胞过程,识别酵母的细胞周期特异调控子网是理解细胞周期过程的基础,并且有助于揭示其他细胞条件的基因调控机理.使用一个基因表达微分方程模型（GEDEM）,从静态网络中识别了动态的细胞周期相关调控关系.与已经报道的细胞周期相关调控相互作用相比,该方法识别了更多的真实存在的条件特异调控关系,取得了比当前的方法更好的性能.在大数据集上,GEDEM 识别了具有高敏感性和特异性的调控子网.组合调控的深入分析显示,条件特异调控子网的转录因子之间的相关性呈现出比静态网络中转录因子相关性更强,这说明条件特异网络比静态网络更加接近真实情况.另外,GEDEM 方法还识别更多潜在的共调控转录因子.
英文摘要：
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.
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