Distinguishing the rates of gene activation from phenotypic variations.
Distinguishing the rates of gene activation from phenotypic variations.
复制标题
区分基因激活率和表型变异
DOI：
10.1186/s12918-015-0172-0
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发表时间：
2015-06-18
影响因子：
--
通讯作者：
Li T
中科院分区：
文献类型：
--
作者：
Chen Y;Lv C;Li F;Li T
背景由内在噪声驱动的随机遗传转换是基因表达的一个重要过程。当基因激活/失活的速率与mRNA和蛋白质的合成/降解速率相比相对较慢、较快或中等时，蛋白质和mRNA水平的变异性可能表现出非常不同的动力学模式。这是可取的，以提供一个系统的方法来确定其关键的动力学特征在不同的制度，旨在区分该制度所考虑的基因调控网络是在从他们的表型variations.ResultsWe研究了一个基因表达模型的正反馈时，遗传开关率在很大范围内变化。为了提供一种区分开关速率的方法，我们首先着重于理解基因表达系统在不同情况下的基本动力学。在慢开关速率下，系统的有效动力学过程可以归结为两个独立的层上的演化过程，对应于基因的激活和失活状态，两层之间的跃迁是罕见的事件，之后系统主要沿着特定层上的沿着确定性常微分方程轨道到达新的稳态.在这种情况下的能量景观可以很好地近似使用高斯混合模型。在中间切换率的制度下，我们分析了平均切换时间，以研究系统在不同参数范围内的稳定性。我们还从过渡态理论的角度讨论了快开关率的情况。基于所获得的结果，我们提出了一个建议，以区分这三个政权的模拟实验。我们确定了中间政权的事实，即细胞记忆的强度低于其他两种情况下，和快速和缓慢的制度可以区分他们不同的扰动响应行为相对于开关率perturbationsConclusionsWe提出了一个模拟实验来区分慢，中间和快速的制度，这是我们的论文的要点。为了实现这一目标，我们系统地研究了基因表达系统在不同切换速率下的基本动力学行为。我们的理论认识为基因表达实验提供了新的见解。
BackgroundStochastic genetic switching driven by intrinsic noise is an important process in gene expression. When the rates of gene activation/inactivation are relatively slow, fast, or medium compared with the synthesis/degradation rates of mRNAs and proteins, the variability of protein and mRNA levels may exhibit very different dynamical patterns. It is desirable to provide a systematic approach to identify their key dynamical features in different regimes, aiming at distinguishing which regime a considered gene regulatory network is in from their phenotypic variations.ResultsWe studied a gene expression model with positive feedbacks when genetic switching rates vary over a wide range. With the goal of providing a method to distinguish the regime of the switching rates, we first focus on understanding the essential dynamics of gene expression system in different cases. In the regime of slow switching rates, we found that the effective dynamics can be reduced to independent evolutions on two separate layers corresponding to gene activation and inactivation states, and the transitions between two layers are rare events, after which the system goes mainly along deterministic ODE trajectories on a particular layer to reach new steady states. The energy landscape in this regime can be well approximated by using Gaussian mixture model. In the regime of intermediate switching rates, we analyzed the mean switching time to investigate the stability of the system in different parameter ranges. We also discussed the case of fast switching rates from the viewpoint of transition state theory. Based on the obtained results, we made a proposal to distinguish these three regimes in a simulation experiment. We identified the intermediate regime from the fact that the strength of cellular memory is lower than the other two cases, and the fast and slow regimes can be distinguished by their different perturbation-response behavior with respect to the switching rates perturbations.ConclusionsWe proposed a simulation experiment to distinguish the slow, intermediate and fast regimes, which is the main point of our paper. In order to achieve this goal, we systematically studied the essential dynamics of gene expression system when the switching rates are in different regimes. Our theoretical understanding provides new insights on the gene expression experiments.
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能源景观揭示出芽酵母细胞周期是一个稳健且适应性强的多阶段过程
影响因子：
4.3
作者：
Lv C;Li X;Li F;Li T
通讯作者：
Li T
细胞命运决策中的记忆和模块化。
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
56.9
作者：
Maamar, Hedia;Raj, Arjun;Dubnau, David
通讯作者：
Dubnau, David
影响因子：
3.3
作者：
Feng, Haidong;Han, Bo;Wang, Jin
通讯作者：
Wang, Jin
DOI：
10.1126/science.1161427
发表时间：
2008-10-17
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Choi PJ;Cai L;Frieda K;Xie XS
通讯作者：
Xie XS