不同设计机制的差异转录调控:数学建模方法。

Q2 Medicine
Necmettin Yildirim, Mehmet Emin Aktas, Seyma Nur Ozcan, Esra Akbas, Ahmet Ay
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引用次数: 1

摘要

细胞通过不同的调节机制来应对外界刺激,维持细胞内稳态。我们研究了两组依赖信号的转录调控机制。在第一组中,我们假设抑制蛋白和激活蛋白竞争结合到DNA上相同的调节位点(竞争机制)。在第二组中,它们可以以非竞争的方式(非竞争机制)绑定到不同的监管区域。对于竞争和非竞争机制,我们通过增加抑制因子或减少激活因子丰度(抑制机制)或减少抑制因子或增加激活因子丰度(激活机制)来研究基因表达动态。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。抑制因子丰度增加导致非竞争性抑制机制反应最快,抑制因子水平增加导致竞争性抑制机制反应最慢。通过降低激活物丰度的竞争性和非竞争性抑制机制的反应时间相当且适中,而通过增加激活物蛋白水平的竞争性和非竞争性激活机制的反应时间更有效且更快。我们的研究举例说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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differential transcriptional regulation by alternatively designed mechanisms: A mathematical modeling approach.

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.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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