School of Genome Science and Technology, The University of Tennessee, Knoxville, TN, USA.
Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, USA.
BMC Bioinformatics. 2021 Oct 4;22(1):481. doi: 10.1186/s12859-021-04405-z.
Feedback loops in gene regulatory networks play pivotal roles in governing functional dynamics of cells. Systems approaches demonstrated characteristic dynamical features, including multistability and oscillation, of positive and negative feedback loops. Recent experiments and theories have implicated highly interconnected feedback loops (high-feedback loops) in additional nonintuitive functions, such as controlling cell differentiation rate and multistep cell lineage progression. However, it remains challenging to identify and visualize high-feedback loops in complex gene regulatory networks due to the myriad of ways in which the loops can be combined. Furthermore, it is unclear whether the high-feedback loop structures with these potential functions are widespread in biological systems. Finally, it remains challenging to understand diverse dynamical features, such as high-order multistability and oscillation, generated by individual networks containing high-feedback loops. To address these problems, we developed HiLoop, a toolkit that enables discovery, visualization, and analysis of several types of high-feedback loops in large biological networks.
HiLoop not only extracts high-feedback structures and visualize them in intuitive ways, but also quantifies the enrichment of overrepresented structures. Through random parameterization of mathematical models derived from target networks, HiLoop presents characteristic features of the underlying systems, including complex multistability and oscillations, in a unifying framework. Using HiLoop, we were able to analyze realistic gene regulatory networks containing dozens to hundreds of genes, and to identify many small high-feedback systems. We found more than a 100 human transcription factors involved in high-feedback loops that were not studied previously. In addition, HiLoop enabled the discovery of an enrichment of high feedback in pathways related to epithelial-mesenchymal transition.
HiLoop makes the study of complex networks accessible without significant computational demands. It can serve as a hypothesis generator through identification and modeling of high-feedback subnetworks, or as a quantification method for motif enrichment analysis. As an example of discovery, we found that multistep cell lineage progression may be driven by either specific instances of high-feedback loops with sparse appearances, or generally enriched topologies in gene regulatory networks. We expect HiLoop's usefulness to increase as experimental data of regulatory networks accumulate. Code is freely available for use or extension at https://github.com/BenNordick/HiLoop .
基因调控网络中的反馈回路在控制细胞功能动态方面起着关键作用。系统方法展示了正反馈回路和负反馈回路的特征动态特征，包括多稳定性和振荡。最近的实验和理论表明，高度互联的反馈回路（高反馈回路）在控制细胞分化率和多步细胞谱系进展等额外的非直观功能中发挥作用。然而，由于反馈回路可以以多种方式组合，因此在复杂的基因调控网络中识别和可视化高反馈回路仍然具有挑战性。此外，这些潜在功能的高反馈回路结构在生物系统中是否广泛存在也不清楚。最后，理解包含高反馈回路的单个网络产生的多样化动态特征，如高阶多稳定性和振荡，仍然具有挑战性。为了解决这些问题，我们开发了 HiLoop，这是一个工具包，可用于发现、可视化和分析大型生物网络中的几种类型的高反馈回路。
HiLoop 不仅提取高反馈结构并以直观的方式可视化它们，还量化了过表达结构的丰富度。通过对来自目标网络的数学模型进行随机参数化，HiLoop 在一个统一的框架中呈现了底层系统的特征，包括复杂的多稳定性和振荡。使用 HiLoop，我们能够分析包含数十到数百个基因的真实基因调控网络，并识别出许多小的高反馈系统。我们发现了 100 多个以前未研究过的人类转录因子参与高反馈回路。此外，HiLoop 还发现上皮-间充质转化相关途径中高反馈的富集。
HiLoop 在没有大量计算需求的情况下使复杂网络的研究变得可行。它可以作为一个假设生成器，通过识别和建模高反馈子网，或者作为 motif 富集分析的量化方法。作为发现的一个例子，我们发现多步细胞谱系进展可能由高反馈回路的特定实例驱动，这些实例的出现稀疏，或者一般来说，基因调控网络中的拓扑结构丰富。我们预计，随着调控网络的实验数据不断积累，HiLoop 的实用性将会提高。代码可在 https://github.com/BenNordick/HiLoop 免费使用或扩展。
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