Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
School of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom.
PLoS Comput Biol. 2018 Feb 16;14(2):e1006000. doi: 10.1371/journal.pcbi.1006000. eCollection 2018 Feb.
Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression. Herein we develop a framework for systematically extending Boolean-level network topologies into higher resolution models of networks which explicitly account for the promoter architectures and gene state switching dynamics. We show the framework to be useful for disentangling the various contributions that gene switching, external signaling, and network topology make to the global heterogeneity and dynamics of transcription factor populations. We find the pluripotent state of the network to be a steady state which is robust to global variations of gene switching rates which we argue are a good proxy for epigenetic states of individual promoters. The temporal dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals.
多能胚胎干细胞对于生物医学科学至关重要，因为它们具有自我更新和分化为所有主要细胞系的固有能力。退出或保持多能状态的决定性决定是由复杂的遗传调控网络调节的。单细胞测序数据的快速增长极大地刺激了统计和机器学习方法在推断调控多能性的遗传网络拓扑结构中的应用。然而，推断出的网络拓扑结构通常只编码布尔信息，而对基因表达中固有的动力学和分子随机性的作用保持沉默。在此，我们开发了一个框架，系统地将布尔级网络拓扑结构扩展为更显式地考虑启动子结构和基因状态转换动力学的网络的更高分辨率模型。我们表明，该框架可用于解耦基因转换、外部信号和网络拓扑结构对转录因子群体的全局异质性和动力学的各种贡献。我们发现网络的多能状态是一个稳定状态，对基因转换速率的全局变化具有鲁棒性，我们认为这是单个启动子的表观遗传状态的良好代理。另一方面，退出多能状态的时间动态受到遗传转换速率的显著影响，这使得细胞对细胞外信号的变化更敏感。
