Aguirre Matthew, Spence Jeffrey P, Sella Guy, Pritchard Jonathan K
Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America.
Department of Genetics, Stanford University, Stanford, California, United States of America.
PLoS Comput Biol. 2025 Sep 2;21(9):e1013387. doi: 10.1371/journal.pcbi.1013387. eCollection 2025 Sep.
Gene regulatory networks (GRNs) govern many core developmental and biological processes underlying human complex traits. Even with broad-scale efforts to characterize the effects of molecular perturbations and interpret gene coexpression, it remains challenging to infer the architecture of gene regulation in a precise and efficient manner. Key properties of GRNs, like hierarchical structure, modular organization, and sparsity, provide both challenges and opportunities for this objective. Here, we seek to better understand properties of GRNs using a new approach to simulate their structure and model their function. We produce realistic network structures with a novel generating algorithm based on insights from small-world network theory, and we model gene expression regulation using stochastic differential equations formulated to accommodate modeling molecular perturbations. With these tools, we systematically describe the effects of gene knockouts within and across GRNs, finding a subset of networks that recapitulate features of a recent genome-scale perturbation study. With deeper analysis of these exemplar networks, we consider future avenues to map the architecture of gene expression regulation using data from cells in perturbed and unperturbed states, finding that while perturbation data are critical to discover specific regulatory interactions, data from unperturbed cells may be sufficient to reveal regulatory programs.
基因调控网络（GRNs）支配着许多构成人类复杂性状基础的核心发育和生物学过程。即便有大规模的努力来表征分子扰动的影响并解释基因共表达，但要以精确且高效的方式推断基因调控的架构仍具有挑战性。GRNs的关键特性，如层次结构、模块化组织和稀疏性，为实现这一目标既带来了挑战，也提供了机遇。在此，我们试图通过一种新方法来更好地理解GRNs的特性，该方法用于模拟其结构并对其功能进行建模。我们基于小世界网络理论的见解，用一种新颖的生成算法生成逼真的网络结构，并用为适应分子扰动建模而制定的随机微分方程对基因表达调控进行建模。借助这些工具，我们系统地描述了基因敲除在GRNs内部和之间的影响，发现了一部分网络，这些网络概括了最近一项基因组规模扰动研究的特征。通过对这些典型网络的深入分析，我们思考了利用处于扰动和未扰动状态的细胞数据来绘制基因表达调控架构的未来途径，发现虽然扰动数据对于发现特定的调控相互作用至关重要，但来自未扰动细胞的数据可能足以揭示调控程序。
