Ghorbani Mahboobeh, Jonckheere Edmond A, Bogdan Paul
Electrical Engineering Department, University of Southern California, Los Angeles, CA, United States.
Front Physiol. 2018 Oct 22;9:1446. doi: 10.3389/fphys.2018.01446. eCollection 2018.
Gene expression is a vital process through which cells react to the environment and express functional behavior. Understanding the dynamics of gene expression could prove crucial in unraveling the physical complexities involved in this process. Specifically, understanding the coherent complex structure of transcriptional dynamics is the goal of numerous computational studies aiming to study and finally control cellular processes. Here, we report the scaling properties of gene expression time series in and . Unlike previous studies, which report the fractal and long-range dependency of DNA structure, we investigate the individual gene expression dynamics as well as the cross-dependency between them in the context of gene regulatory network. Our results demonstrate that the gene expression time series display fractal and long-range dependence characteristics. In addition, the dynamics between genes and linked transcription factors in gene regulatory networks are also fractal and long-range cross-correlated. The cross-correlation exponents in gene regulatory networks are not unique. The distribution of the cross-correlation exponents of gene regulatory networks for several types of cells can be interpreted as a measure of the complexity of their functional behavior.
基因表达是一个至关重要的过程，通过这个过程细胞对环境做出反应并表现出功能行为。了解基因表达的动态变化对于揭示这一过程中涉及的物理复杂性可能至关重要。具体而言，了解转录动力学的相干复杂结构是众多旨在研究并最终控制细胞过程的计算研究的目标。在此，我们报告了[具体内容缺失]中基因表达时间序列的标度特性。与之前报道DNA结构的分形和长程依赖性的研究不同，我们在基因调控网络的背景下研究单个基因表达动态以及它们之间的交叉依赖性。我们的结果表明，基因表达时间序列表现出分形和长程依赖性特征。此外，基因调控网络中基因与相关转录因子之间的动态也是分形和长程交叉相关的。基因调控网络中的交叉相关指数并非唯一。几种类型细胞的基因调控网络交叉相关指数的分布可以解释为其功能行为复杂性的一种度量。
