Brent Michael R
Departments of Computer Science and Genetics and Center for Genome Sciences and Systems Biology, Washington University, , Saint Louis, MO, USA.
Trends Genet. 2016 Nov;32(11):736-750. doi: 10.1016/j.tig.2016.08.009. Epub 2016 Oct 6.
One of the principal mechanisms by which cells differentiate and respond to changes in external signals or conditions is by changing the activity levels of transcription factors (TFs). This changes the transcription rates of target genes via the cell's TF network, which ultimately contributes to reconfiguring cellular state. Since microarrays provided our first window into global cellular state, computational biologists have eagerly attacked the problem of mapping TF networks, a key part of the cell's control circuitry. In retrospect, however, steady-state mRNA abundance levels were a poor substitute for TF activity levels and gene transcription rates. Likewise, mapping TF binding through chromatin immunoprecipitation proved less predictive of functional regulation and less amenable to systematic elucidation of complete networks than originally hoped. This review explains these roadblocks and the current, unprecedented blossoming of new experimental techniques built on second-generation sequencing, which hold out the promise of rapid progress in TF network mapping.
细胞分化并对外部信号或条件变化做出反应的主要机制之一是通过改变转录因子（TFs）的活性水平。这通过细胞的TF网络改变靶基因的转录速率，最终有助于重新配置细胞状态。自从微阵列技术为我们提供了了解全局细胞状态的首个窗口以来，计算生物学家就热切地着手解决绘制TF网络的问题，这是细胞控制电路的关键部分。然而，回顾过去，稳态mRNA丰度水平并不能很好地替代TF活性水平和基因转录速率。同样，通过染色质免疫沉淀法绘制TF结合图谱，其对功能调控的预测性较差，也不如最初期望的那样易于对完整网络进行系统阐释。本综述解释了这些障碍以及基于第二代测序技术的新实验技术目前前所未有的蓬勃发展，这些技术有望在TF网络绘制方面取得快速进展。
