论文标题
布尔网络模型的荟萃分析揭示了基因调节网络的设计原理
A meta-analysis of Boolean network models reveals design principles of gene regulatory networks
论文作者
论文摘要
基因调节网络（GRN）在细胞决策中起着核心作用。了解它们的结构及其影响如何构成他们的动态，因此是一个基本的生物学问题。 GRN经常被建模为布尔网络，它们直观，易于描述，即使数据稀疏也可以产生定性结果。我们组装了最大的专家布尔GRN模型存储库。对这套不同模型的荟萃分析揭示了几种设计原理。 GRNS表现出比预期的更大的携带，冗余和稳定的动力学。此外，对于某些重复的网络图案，它们具有丰富性。这就提出了一个重要的问题，为什么进化有利于这些设计机制。
Gene regulatory networks (GRNs) play a central role in cellular decision-making. Understanding their structure and how it impacts their dynamics constitutes thus a fundamental biological question. GRNs are frequently modeled as Boolean networks, which are intuitive, simple to describe, and can yield qualitative results even when data is sparse. We assembled the largest repository of expert-curated Boolean GRN models. A meta-analysis of this diverse set of models reveals several design principles. GRNs exhibit more canalization, redundancy and stable dynamics than expected. Moreover, they are enriched for certain recurring network motifs. This raises the important question why evolution favors these design mechanisms.
