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Dynamic Complexity of the Temporal Transcriptional Regulation Program in Human Endotoxemia

机译:人体内毒性颞型转印规律计划的动态复杂性

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Human endotoxemia is a well-accepted surrogate model for studying the acute inflammatory responses. In order to discover the complex underlying dynamics, identifying biologically relevant transcriptional regulators as well as their putative regulatory interactions with target genes is an essential step. However, prediction of relevant transcriptional regulators in higher eukaryotes remains a challenge both in silico and in vivo. In this study, we analyzed gene expression data from human blood leukocytes to extract four significant patterns of highly coexpressed genes that capture the essence of inflammatory phases. Upon identification of these patterns, a number of inflammation-specific pathways are selected by evaluating the enrichment of the corresponding subsets. Subsequently, statistically significant cis-regulatory modules (CRMs) are selected and decomposed into a list of relevant transcription factors (34 TFs) which are further validated from prior experiments and computational studies in literature. Additionally, our analysis also allows for the construction of a putative dynamic representation of the transcriptional regulatory program, making it become a critical enabler for unraveling regulatory interactions which is an essential step towards a quantification of dynamic transcriptional regulatory networks.
机译:人内毒血症是一种良好的替代模型,用于研究急性炎症反应。为了发现复杂的潜在动态,鉴定生物相关的转录调节剂以及与靶基因的推定调节相互作用是重要的步骤。然而,在较高真核续中的相关转录调节剂的预测仍然是硅和体内的挑战。在这项研究中,我们分析了来自人血白细胞的基因表达数据,以提取捕集炎症阶段的本质的高度共识基因的四种显着模式。在鉴定这些模式时,通过评估相应的子集的富集来选择许多炎症特异性途径。随后,选择统计学显着的顺式调节模块(CRM)并分解成相关转录因子(34 TF)的清单,这些因子(34个TFS)是从先前的实验和文献中的计算研究进一步验证的。此外,我们的分析还允许建造转录监管计划的推定动态表示,使其成为解除监管相互作用的关键推动因素,这是朝向量化动态转录监管网络的基本步骤。

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