Computational Modeling of Gene Regulation

基因调控的计算模型

• 批准号: RGPIN-2017-06743
• 负责人: ZHANG, ZHAOLEI
• 金额: $ 1.89万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711934
• 关键词: Computational; Modeling; Gene; Regulation

The human genome contains over 22,000 genes, which remarkably is not much more than the single cell budding yeast or a fruit fly. So what makes humans so much more complex? The answer lies in the immensely complex and delicate ways by which the human genes are regulated or turned on and off. Glitches in these regulations often result in protein products being made at a wrong amount or at wrong time or wrong place, which in turn can cause diseases such as cancer. In order to better understand how human cells work and function, it is crucial to understand how each of the 22,000 genes is regulated and how groups of genes are turned on and off to fulfill the required cellular task. Supported by a previous Discovery Grant, my students and I have made significant progress in developing computing methods in studying gene regulation and genetic variations. For example, we developed a suite of software tools that can accurately predict microRNA target genes and identify miRNA regulations that are important in human diseases. We also developed algorithms that can integrate transcription factors (TFs), microRNAs and other regulation elements into a comprehensive regulatory network, and computational tools for analyzing such network. In the next funded period, we will further improve and extend these algorithms so that they can run efficiently and quickly on a genomic scale. Our software tool will be able to quickly scan genome sequences and identify mutations that disrupt regulatory elements. These tools will be able to integrate different type of regulatory elements, such as microRNAs, transcription factors (TF), RNA binding proteins (RBP), RNA structure, and DNA or RNA methylation, into one comprehensive network model. Interestingly many of these regulatory mechanisms can cross-talk to each other. For example, microRNAs and RBPs can compete for the same target position in the 3'UTR, and methylation in these target sites can mask the binding by microRNAs or RBPs. Our integrated regulatory network that will help researchers to quickly and accurately identify and prioritize mutations or mutated regulations. With the rapid advancement of next generation sequencing technology and reduction of sequencing cost, we envision that tens of thousands or even millions of human genome sequence will become available in the next years. Fast, efficient, and accurate computational tools will be needed to analyze these genomes and identify actionable mutations. The methodology that we are developing will help to fill these needs.

人类基因组包含超过22，000个基因，这显然不比单细胞芽殖酵母或果蝇多多少。是什么让人类变得如此复杂？答案在于人类基因的调节或开启和关闭的极其复杂和微妙的方式。 这些法规中的漏洞通常会导致蛋白质产品在错误的时间或地点以错误的数量生产，这反过来会导致癌症等疾病。为了更好地了解人类细胞的工作和功能，了解22，000个基因中的每一个是如何调节的，以及基因组如何打开和关闭以完成所需的细胞任务至关重要。 在以前的发现基金的支持下，我和我的学生在开发研究基因调控和遗传变异的计算方法方面取得了重大进展。例如，我们开发了一套软件工具，可以准确预测microRNA靶基因，并识别在人类疾病中重要的miRNA调控。我们还开发了可以将转录因子（TF），microRNA和其他调控元件整合到一个全面的调控网络中的算法，以及用于分析这种网络的计算工具。 在下一个资助期内，我们将进一步改进和扩展这些算法，使它们能够在基因组规模上高效快速地运行。我们的软件工具将能够快速扫描基因组序列，并识别破坏调控元件的突变。这些工具将能够整合不同类型的调控元件，如microRNA，转录因子（TF），RNA结合蛋白（RBP），RNA结构和DNA或RNA甲基化，到一个全面的网络模型。 有趣的是，这些调节机制中的许多可以相互交叉。 例如，微小RNA和RBP可以竞争3'UTR中的相同靶位置，并且这些靶位点中的甲基化可以掩盖微小RNA或RBP的结合。我们的综合监管网络将帮助研究人员快速准确地识别和优先考虑突变或突变的法规。 随着下一代测序技术的快速发展和测序成本的降低，我们预计在未来几年内将有数万甚至数百万的人类基因组序列可用。需要快速、高效和准确的计算工具来分析这些基因组并识别可操作的突变。我们正在开发的方法将有助于满足这些需求。