Interrogating the topological robustness of gene regulatory circuits by randomization.
Interrogating the topological robustness of gene regulatory circuits by randomization.
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DOI：
10.1371/journal.pcbi.1005456
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发表时间：
2017-03
影响因子：
4.3
通讯作者：
Onuchic JN
中科院分区：
文献类型：
--
作者：
Huang B;Lu M;Jia D;Ben-Jacob E;Levine H;Onuchic JN
细胞最重要的作用之一是为了生存而正确地执行细胞任务。细胞通常通过涉及许多基因的多层调控来实现强大的功能，例如细胞命运决策和信号转导。尽管基因调控具有组合复杂性，但其定量行为的研究通常是基于实验验证的核心基因调控回路（由一小部分重要元件组成）。目前尚不清楚这种核心电路是如何在许多其他调控分子存在的情况下以及在拥挤嘈杂的细胞环境中运作的。在这里，我们报告了一种新的计算方法，称为随机电路摄动（RACIPE），用于询问基因调控电路的鲁棒动力学行为，即使没有精确的电路动力学参数测量。RACIPE生成与固定电路拓扑相对应的随机动力学模型集合，并利用统计工具识别电路的一般属性。通过将RACIPE应用于简单的开关基序，我们观察到即使参数受到强烈干扰，所有模型的稳定状态也收敛于实验观察到的基因状态簇。RACIPE进一步应用于上皮-间充质转化（EMT）的22个基因网络，从中我们确定了四种实验观察到的基因状态，包括与两种不同类型的上皮/间充质杂交表型相关的状态。我们的研究结果表明，基因电路的动力学主要由其拓扑结构决定，而不是由详细的电路参数决定。我们的工作为基于电路的系统生物学建模提供了理论基础。我们预计RACIPE将成为一个强大的工具，以不偏不倚的方式预测和解码电路设计原理，并定量评估基因表达的稳健性和异质性。细胞能够稳健地执行其基本的生物学功能，可能是因为通过复杂而精心设计的基因调控网络，涉及大量基因的多层严格调控。最先进的基因组学技术使这些大型基因网络的定位成为可能，但阐明它们的设计原理和它们的生物学功能（如信号处理和决策）背后的调控机制仍然是一个巨大的挑战。其中一个关键的障碍是缺乏准确的动力学调节相互作用，特别是从体内实验。为此，我们开发了一种新的计算建模方法，随机电路摄动（RACIPE），以探索基因调控电路的动态行为，而不需要详细的动力学参数。RACIPE以网络拓扑为输入，生成具有不同动力学参数的无偏模型集合。对每个模型进行模拟，然后对集合进行统计分析。我们在几个基因回路上测试了RACIPE，发现所有模型预测的基因表达模式都收敛于实验观察到的基因状态簇。我们期望RACIPE能够成为一种识别网络拓扑在确定网络运行原则中的作用的强大方法。
One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example, cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal Transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression. Cells are able to robustly carry out their essential biological functions, possibly because of multiple layers of tight regulation via complex, yet well-designed, gene regulatory networks involving a substantial number of genes. State-of-the-art genomics technology has enabled the mapping of these large gene networks, yet it remains a tremendous challenge to elucidate their design principles and the regulatory mechanisms underlying their biological functions such as signal processing and decision-making. One of the key barriers is the absence of accurate kinetics for the regulatory interactions, especially from in vivo experiments. To this end, we have developed a new computational modeling method, Random Circuit Perturbation (RACIPE), to explore the dynamic behaviors of gene regulatory circuits without the requirement of detailed kinetic parameters. RACIPE takes a network topology as the input, and generates an unbiased ensemble of models with varying kinetic parameters. Each model is subjected to simulation, followed by statistical analysis for the ensemble. We tested RACIPE on several gene circuits, and found that the predicted gene expression patterns from all of the models converge to experimentally observed gene state clusters. We expect RACIPE to be a powerful method to identify the role of network topology in determining network operating principles.