Interacting models of cooperative gene regulation
Interacting models of cooperative gene regulation
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DOI：
10.1073/pnas.0407365101
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发表时间：
2004-11-16
影响因子：
11.1
通讯作者：
Zhang, MQ
中科院分区：
文献类型：
--
作者：
Das, D;Banerjee, N;Zhang, MQ
转录因子之间的协同性对基因调控至关重要。当前的计算方法没有充分考虑到这一显著方面。为了解决这个问题，我们提出了一种基于多元自适应回归样条的计算方法，将启动子DNA中转录因子结合基序的出现及其相互作用与基因表达水平比值的对数相关联。这使我们能够发现最有可能具有功能的单个基序以及协同的基序对，并列举它们在可获得mRNA表达数据的任意时间点的相对贡献。我们展示了模拟结果，并特别关注酵母细胞周期数据。纳入协同相互作用可使预测准确性比线性回归提高多达1.5到3.5倍。在细胞周期的每个阶段都能恰当地预测出重要的基序以及基序组合。我们相信，当应用于高等真核生物，尤其是哺乳动物时，我们基于多元自适应回归样条的方法将变得更加重要，因为在这些生物中基因调控的协同控制是绝对必要的。
Cooperativity between transcription factors is critical to gene regulation. Current computational methods do not take adequate account of this salient aspect. To address this issue, we present a computational method based on multivariate adaptive regression splines to correlate the occurrences of transcription factor binding motifs in the promoter DNA and their interactions to the logarithm of the ratio of gene expression levels. This allows us to discover both the individual motifs and synergistic pairs of motifs that are most likely to be functional, and enumerate their relative contributions at any arbitrary time point for which mRNA expression data are available. We present results of simulations and focus specifically on the yeast cell-cycle data. Inclusion of synergistic interactions can increase the prediction accuracy over linear regression to as much as 1.5- to 3.5-fold. Significant motifs and combinations of motifs are appropriately predicted at each stage of the cell cycle. We believe our multivariate adaptive regression splines-based approach will become more significant when applied to higher eukaryotes, especially mammals, where cooperative control of gene regulation is absolutely essential.