How the architecture of gene regulatory networks ultimately shapes gene expression patterns is an open question, which has been approached from a multitude of angles. The dominant strategy has been to identify non-random features in these networks and then argue for the function of these features using mechanistic modelling. Here we establish the foundation of an alternative approach by studying the correlation of eigenvectors with synthetic gene expression data simulated with a basic and popular model of gene expression dynamics -- attractors of Boolean threshold dynamics in signed directed graphs. Eigenvectors of the graph Laplacian are known to explain collective dynamical states (stationary patterns) in Turing dynamics on graphs. In this study, we show that eigenvectors can also predict collective states (attractors) for a markedly different type of dynamics, Boolean threshold dynamics, and category of graphs, signed directed graphs. However, the overall predictive power depends on details of the network architecture, in a predictable fashion. Our results are a set of statistical observations, providing the first systematic step towards a further theoretical understanding of the role of eigenvectors in dynamics on graphs.
翻译:基因监管网络的架构如何最终塑造基因表达模式是一个尚未解决的问题,这个问题是从多个角度探讨的。主要战略是确定这些网络的非随机特征,然后使用机械模型来论证这些特征的功能。在这里,我们通过研究叶质生物与基因表达动态基本和流行模型模拟的基因表达动态合成基因表达数据之间的关系来建立替代方法的基础。该模型吸引了经签署的定向图解中的布林临界线动态。图 Laplacian 的精子可以解释图示动态中的集体动态状态(静态模式) 。在本研究中,我们显示,叶质生物还可以预测明显不同的动态类型的集体状态(吸引器 ), Boolean 阈值动态和图表类别, 签名为定向图案。然而,总体预测力取决于网络结构的细节,以可预测的方式。我们的结果是一组统计观测结果,为进一步从理论上理解图表中叶质生物动力的作用提供了第一个系统步骤。