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Quantitative modeling of transcriptional regulatory networks by integrating multiple source of knowledge

机译：通过整合多种知识来源对转录调控网络进行定量建模

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摘要

A key challenge in the post genome era is to identify genome-wide transcriptional regulatory networks, which specify the interactions between transcription factors and their target genes. In this work, a regulatory model-based binding energy is proposed to quantify the transcriptional regulatory network. Multiple quantities, including binding affinity, regulatory efficiency, and the activity level of transcription factor (TF) are incorporated into a general learning model. The sequence features of the promoter are exploited to derive the binding energy. Comparing with the previous models that only employ microarray data, the proposed model can bridge the gap between the relative background frequency of the observed nucleotide and the gene's transcription rate. Experimental results show that the proposed model can effectively identify the parameters and the activity level of TF. Moreover, the kinetic parameters introduced in the proposed model can reveal more biological sense than some previous models can do.
机译：后基因组时代的一个关键挑战是确定全基因组的转录调控网络，该网络规定了转录因子与其靶基因之间的相互作用。在这项工作中，提出了一种基于调控模型的结合能来量化转录调控网络。包括结合亲和力，调节效率和转录因子（TF）活性水平在内的多个量都被纳入了通用学习模型中。利用启动子的序列特征来获得结合能。与仅使用微阵列数据的先前模型相比，所提出的模型可以弥补观察到的核苷酸的相对本底频率与基因转录率之间的差距。实验结果表明，该模型可以有效地识别TF的参数和活性水平。此外，所提出的模型引入的动力学参数比某些先前模型可以揭示更多的生物学意义。

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