Darnell Cynthia L, Schmid Amy K
Biology Department, Duke University, Durham, NC 27708, USA.
Biology Department, Duke University, Durham, NC 27708, USA; Center for Systems Biology, Duke University, Durham, NC 27708, USA.
Methods. 2015 Sep 15;86:102-14. doi: 10.1016/j.ymeth.2015.04.034. Epub 2015 May 12.
To survive complex and changing environmental conditions, microorganisms use gene regulatory networks (GRNs) composed of interacting regulatory transcription factors (TFs) to control the timing and magnitude of gene expression. Genome-wide datasets; such as transcriptomics and protein-DNA interactions; and experiments such as high throughput growth curves; facilitate the construction of GRNs and provide insight into TF interactions occurring under stress. Systems biology approaches integrate these datasets into models of GRN architecture as well as statistical and/or dynamical models to understand the function of networks occurring in cells. Previously, these types of studies have focused on traditional model organisms (e.g. Escherichia coli, yeast). However, recent advances in archaeal genetics and other tools have enabled a systems approach to understanding GRNs in these relatively less studied archaeal model organisms. In this report, we outline a systems biology workflow for generating and integrating data focusing on the TF regulator. We discuss experimental design, outline the process of data collection, and provide the tools required to produce high confidence regulons for the TFs of interest. We provide a case study as an example of this workflow, describing the construction of a GRN centered on multi-TF coordinate control of gene expression governing the oxidative stress response in the hypersaline-adapted archaeon Halobacterium salinarum.
为了在复杂多变的环境条件下生存,微生物利用由相互作用的调控转录因子(TFs)组成的基因调控网络(GRNs)来控制基因表达的时间和幅度。全基因组数据集,如转录组学和蛋白质-DNA相互作用,以及高通量生长曲线等实验,有助于构建GRNs,并深入了解应激条件下发生的TF相互作用。系统生物学方法将这些数据集整合到GRN架构模型以及统计和/或动力学模型中,以了解细胞中网络的功能。以前,这类研究主要集中在传统模式生物(如大肠杆菌、酵母)上。然而,古菌遗传学和其他工具的最新进展使得能够采用系统方法来理解这些相对较少研究的古菌模式生物中的GRNs。在本报告中,我们概述了一种以TF调节因子为重点的数据生成和整合的系统生物学工作流程。我们讨论了实验设计,概述了数据收集过程,并提供了为感兴趣的TF生成高可信度调控子所需的工具。我们提供了一个案例研究作为该工作流程的示例,描述了以多TF协调控制基因表达为中心构建GRN的过程,该基因表达控制了适应高盐环境的古菌盐沼盐杆菌中的氧化应激反应。