Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.
Department of Chemistry, Rice University, Houston, Texas 77005, United States.
J Phys Chem Lett. 2024 Aug 29;15(34):8781-8789. doi: 10.1021/acs.jpclett.4c02050. Epub 2024 Aug 20.
Transcription is a fundamental biological process of transferring genetic information which often occurs in stochastic bursts when periods of intense activity alternate with quiescent phases. Recent experiments identified strong correlations between the association of transcription factors (TFs) to gene promoters on DNA and transcriptional activity. However, the underlying molecular mechanisms of this phenomenon remain not well understood. Here, we present a theoretical framework that allowed us to investigate how binding dynamics of TF influences transcriptional bursting. Our minimal theoretical model incorporates the most relevant physical-chemical features, including TF exchange among multiple binding sites at gene promoters and TF association/dissociation dynamics. Using analytical calculations supported by Monte Carlo computer simulations, it is demonstrated that transcriptional bursting dynamics depends on the strength of TF binding and the number of binding sites. Stronger TF binding affinity prolongs burst duration but reduces variability, while an optimal number of binding sites maximizes transcriptional noise, facilitating cellular adaptation. Our theoretical method explains available experimental observations quantitatively, confirming the model's predictive accuracy. This study provides important insights into molecular mechanisms of gene expression and regulation, offering a new theoretical tool for understanding complex biological processes.
转录是将遗传信息从 DNA 转移到 RNA 的基本生物学过程,通常在活跃期和静止期交替出现的随机爆发中发生。最近的实验表明,转录因子 (TF) 与 DNA 上基因启动子的结合与转录活性之间存在很强的相关性。然而,这一现象的潜在分子机制仍不清楚。在这里,我们提出了一个理论框架,使我们能够研究 TF 结合动力学如何影响转录爆发。我们的最小理论模型包含了最相关的物理化学特征,包括 TF 在基因启动子上的多个结合位点之间的交换以及 TF 结合/解离动力学。通过支持蒙特卡罗计算机模拟的分析计算,证明转录爆发动力学取决于 TF 结合的强度和结合位点的数量。更强的 TF 结合亲和力延长了爆发持续时间,但降低了可变性,而最佳数量的结合位点最大化了转录噪声,促进了细胞适应。我们的理论方法定量解释了现有的实验观察结果,证实了模型的预测准确性。这项研究为基因表达和调控的分子机制提供了重要的见解,为理解复杂的生物学过程提供了一个新的理论工具。