Li Xia, Rao Shaoqi, Jiang Wei, Li Chuanxing, Xiao Yun, Guo Zheng, Zhang Qingpu, Wang Lihong, Du Lei, Li Jing, Li Li, Zhang Tianwen, Wang Qing K
Department of Bioinformatics, Harbin Medical University, Harbin 150086, PR China.
BMC Bioinformatics. 2006 Jan 18;7:26. doi: 10.1186/1471-2105-7-26.
It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks.
In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings.
We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs.
全面阐明推动和表征多种生命现象（如细胞周期、发育生物学、衰老以及复杂疾病的进展和复发发病机制等）的分子决定因素之间复杂的相互作用和调控机制，是现代生物学研究的最终目标之一。大量大规模的全基因组时间分辨数据日益可得，这为解决具有挑战性的时间延迟基因调控网络反向工程问题提供了绝佳机会。
特别是，这篇方法学论文旨在通过利用基因之间的延迟相关性，即表达水平随时间相互偏移的成对重叠，从时间基因表达数据重建调控网络。我们因此开发了一种名为TdGRN（时间延迟基因调控网络）的新型无模型计算工具箱，以解决基因在任何时间间隔单位内可能存在的潜在调控问题。这个生物信息学工具箱通过对新设计的时间延迟基因表达矩阵进行决策分析，提供了一种统一的方法来揭示基因调控的时间趋势。我们将所提出的方法应用于酵母细胞周期和人类HeLa细胞周期，发现了大多数潜在的时间延迟调控，这些调控得到了多条实验证据的支持，并且与当前关于细胞周期阶段特征的知识非常一致。
我们建立了一种实用且强大的无模型方法来剖析基因 - 基因相互作用的高阶动态趋势。我们通过将其应用于两个公开可用的细胞周期数据集，仔细验证了所提出的算法。除了揭示细胞周期中基因调控的时间趋势外，这种统一方法还可用于研究与复杂疾病的发育、衰老和进展发病机制相关的复杂基因调控，其中不同实验单位之间可能存在潜在依赖性。
