National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Adv Exp Med Biol. 2013;774:149-67. doi: 10.1007/978-94-007-5590-1_9.
An increasing number of transcription factors (TFs) and microRNAs (miRNAs) is known to form feedback loops (FBLs) of interactions where a TF positively or negatively regulates the expression of a miRNA, and the miRNA suppresses the translation of the TF messenger RNA. FBLs are potential sources of instability in a gene regulatory network. Positive FBLs can give rise to switching behaviors while negative FBLs can generate periodic oscillations. This chapter presents documented examples of FBLs and their relevance to stem cell renewal and differentiation in gliomas. Feed-forward loops (FFLs) are only discussed briefly because they do not affect network stability unless they are members of cycles. A primer on qualitative network stability analysis is given and then used to demonstrate the network destabilizing role of FBLs. Steps in model formulation and computer simulations are illustrated using the miR-17-92/Myc/E2F network as an example. This example possesses both negative and positive FBLs.
越来越多的转录因子（TFs）和 microRNAs（miRNAs）被认为形成了相互作用的反馈环（FBLs），其中 TF 正向或负向调节 miRNA 的表达，而 miRNA 抑制 TF 信使 RNA 的翻译。FBLs 是基因调控网络不稳定性的潜在来源。正 FBL 可以产生切换行为，而负 FBL 可以产生周期性振荡。本章介绍了 FBL 的已有实例及其与胶质瘤中干细胞更新和分化的相关性。前馈环（FFLs）仅简要讨论，因为除非它们是循环的一部分，否则它们不会影响网络稳定性。本文提供了定性网络稳定性分析的入门知识，然后用于证明 FBL 对网络不稳定性的作用。使用 miR-17-92/Myc/E2F 网络作为示例，说明了模型公式化和计算机模拟的步骤。该示例同时具有负 FBL 和正 FBL。
