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Differential transcriptional regulation by alternatively designed mechanisms: A mathematical modeling approach

机译:通过替代设计机制进行差异转录调控:一种数学建模方法

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摘要

Cells maintain cellular homeostasis employing different regulatory mechanisms to respond external stimuli. We study two groups of signal-dependent transcriptional regulatory mechanisms. In the first group, we assume that repressor and activator proteins compete for binding to the same regulatory site on DNA (competitive mechanisms). In the second group, they can bind to different regulatory regions in a noncompetitive fashion (noncompetitive mechanisms). For both competitive and noncompetitive mechanisms, we studied the gene expression dynamics by increasing the repressor or decreasing the activator abundance (inhibition mechanisms), or by decreasing the repressor or increasing the activator abundance (activation mechanisms). We employed delay differential equation models. Our simulation results show that the competitive and noncompetitive inhibition mechanisms exhibit comparable repression effectiveness. However, response time is fastest in the noncompetitive inhibition mechanism due to increased repressor abundance, and slowest in the competitive inhibition mechanism by increased repressor level. The competitive and noncompetitive inhibition mechanisms through decreased activator abundance show comparable and moderate response times, while the competitive and noncompetitive activation mechanisms by increased activator protein level display more effective and faster response. Our study exemplifies the importance of mathematical modeling and computer simulation in the analysis of gene expression dynamics.
机译:细胞通过不同的调节机制来维持细胞稳态,以响应外部刺激。我们研究了两组信号依赖性转录调控机制。在第一组中,我们假设阻遏蛋白和激活蛋白竞争与DNA上的同一调控位点结合(竞争机制)。在第二组中,它们可以以非竞争方式(非竞争机制)与不同的监管区域结合。对于竞争性和非竞争性机制,我们通过增加阻遏蛋白或降低激活剂丰度(抑制机制),或通过降低阻遏蛋白或增加激活剂丰度(激活机制)来研究基因表达动力学。我们采用了延迟微分方程模型。我们的模拟结果表明,竞争性和非竞争性抑制机制表现出相当的抑制效果。然而,由于阻遏蛋白丰度增加,非竞争性抑制机制的响应时间最快,而竞争性抑制机制的响应时间因阻遏蛋白水平增加而最慢。通过降低激活剂丰度的竞争性和非竞争性抑制机制显示出相当和适度的响应时间,而通过增加激活剂蛋白水平的竞争性和非竞争性激活机制显示出更有效和更快的反应。我们的研究证明了数学建模和计算机模拟在基因表达动力学分析中的重要性。

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