Stem cells can precisely and robustly undergo cellular differentiation and lineage commitment, referred to as stemness. However, how the gene network underlying stemness regulation reliably specifies cell fates is not well understood. To address this question, we applied a recently developed computational method, random circuit perturbation (RACIPE), to a nine-component gene regulatory network (GRN) governing stemness, from which we identified robust gene states. Among them, four out of the five most probable gene states exhibit gene expression patterns observed in single mouse embryonic cells at 32-cell and 64-cell stages. These gene states can be robustly predicted by the stemness GRN but not by randomized versions of the stemness GRN. Strikingly, we found a hierarchical structure of the GRN with the Oct4/Cdx2 motif functioning as the first decision-making module followed by Gata6/Nanog. We propose that stem cell populations, instead of being viewed as all having a specific cellular state, can be regarded as a heterogeneous mixture including cells in various states. Upon perturbations by external signals, stem cells lose the capacity to access certain cellular states, thereby becoming differentiated. The new gene states and key parameters regulating transitions among gene states proposed by RACIPE can be used to guide experimental strategies to better understand differentiation and design reprogramming. The findings demonstrate that the functions of the stemness GRN is mainly determined by its well-evolved network topology rather than by detailed kinetic parameters.

中文翻译：

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通过随机电路扰动解码干细胞分化过程中细胞命运决策的机制

干细胞可以精确而有力地进行细胞分化和谱系定型，称为干性。然而，干性调控背后的基因网络如何可靠地指定细胞命运尚不清楚。为了解决这个问题，我们将最近开发的计算方法随机电路扰动 (RACIPE) 应用于控制干性的九组分基因调控网络 (GRN)，从中我们确定了稳健的基因状态。其中，五个最可能的基因状态中有四个表现出在 32 细胞和 64 细胞阶段的单个小鼠胚胎细胞中观察到的基因表达模式。这些基因状态可以通过干性 GRN 进行稳健预测，但不能通过干性 GRN 的随机版本进行预测。引人注目的是，我们发现了 GRN 的层次结构，其中 Oct4/Cdx2 模体作为第一个决策模块，其次是 Gata6/Nanog。我们建议干细胞群，而不是被视为都具有特定的细胞状态，而是可以被视为包括处于各种状态的细胞的异质混合物。在受到外部信号的干扰时，干细胞失去进入某些细胞状态的能力，从而分化。RACIPE 提出的新基因状态和调控基因状态间转换的关键参数可用于指导实验策略，以更好地理解分化和设计重编程。研究结果表明，干性 GRN 的功能主要取决于其进化良好的网络拓扑，而不是由详细的动力学参数决定。