Radboud University, Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, 6525GA Nijmegen, The Netherlands.
Biochem Soc Trans. 2023 Feb 27;51(1):1-12. doi: 10.1042/BST20210145.
Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional dynamics in developmental systems. Computational prediction of GRNs has been successfully applied to genome-wide gene expression measurements with the advent of microarrays and RNA-sequencing. However, these inferred networks are inaccurate and mostly based on correlative rather than causative interactions. In this review, we highlight three approaches that significantly impact GRN inference: (1) moving from one genome-wide functional modality, gene expression, to multi-omics, (2) single cell sequencing, to measure cell type-specific signals and predict context-specific GRNs, and (3) neural networks as flexible models. Together, these experimental and computational developments have the potential to significantly impact the quality of inferred GRNs. Ultimately, accurately modeling the regulatory interactions between transcription factors and their target genes will be essential to understand the role of transcription factors in driving developmental gene expression programs and to derive testable hypotheses for validation.
基因调控网络（GRNs）是理解发育系统转录动态的有用抽象概念。随着微阵列和 RNA 测序的出现，计算预测 GRNs 已成功应用于全基因组基因表达测量。然而，这些推断的网络是不准确的，并且主要基于相关性而不是因果关系。在这篇综述中，我们强调了三种方法对 GRN 推断的重要影响：（1）从全基因组功能模式之一，基因表达，转移到多组学，（2）单细胞测序，以测量细胞类型特异性信号并预测特定于上下文的 GRNs，以及（3）神经网络作为灵活的模型。这些实验和计算方法的发展有可能显著影响推断的 GRNs 的质量。最终，准确建模转录因子与其靶基因之间的调控相互作用对于理解转录因子在驱动发育基因表达程序中的作用以及得出可验证的假设至关重要。
