Linear mapping approximation of gene regulatory networks with stochastic dynamics.
Linear mapping approximation of gene regulatory networks with stochastic dynamics.
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DOI：
10.1038/s41467-018-05822-0
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发表时间：
2018-08-17
影响因子：
16.6
通讯作者：
Grima R
中科院分区：
文献类型：
--
作者：
Cao Z;Grima R
蛋白质-DNA结合反应的存在常常导致随机基因表达的分析上难以处理的模型。在这里，我们提出了线性映射近似映射系统与蛋白质启动子相互作用到近似等价的系统，没有结合反应。这是通过结婚的条件平均场近似和马格努斯展开，导致近似的时间依赖性和稳态蛋白质数量分布的解析或半解析表达式。随机模拟验证了该方法的准确性，在捕捉蛋白质数量分布随时间的变化，显示自动和相互调节的基因表达的各种网络和独立的时间尺度的比例的动态。该方法也被用来研究启动子开关的首次通过时间分布，蛋白质数量波动的大小对参数扰动的敏感性和随机分岔图表征的发病多峰蛋白质数量分布。基因调控网络（GRNs）的大多数随机模型的棘手性限制了它们的实用性。在这里，作者提出了一个线性映射近似映射模型到更简单的，给出了近似但准确的分析或半解析解的广泛的模型GRN。
The presence of protein–DNA binding reactions often leads to analytically intractable models of stochastic gene expression. Here we present the linear-mapping approximation that maps systems with protein–promoter interactions onto approximately equivalent systems with no binding reactions. This is achieved by the marriage of conditional mean-field approximation and the Magnus expansion, leading to analytic or semi-analytic expressions for the approximate time-dependent and steady-state protein number distributions. Stochastic simulations verify the method’s accuracy in capturing the changes in the protein number distributions with time for a wide variety of networks displaying auto- and mutual-regulation of gene expression and independently of the ratios of the timescales governing the dynamics. The method is also used to study the first-passage time distribution of promoter switching, the sensitivity of the size of protein number fluctuations to parameter perturbation and the stochastic bifurcation diagram characterizing the onset of multimodality in protein number distributions. The intractability of most stochastic models of gene regulatory networks (GRNs) limits their utility. Here, the authors present a linear-mapping approximation mapping models onto simpler ones, giving approximate but accurate analytic or semi- analytic solutions for a wide range of model GRNs.
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影响因子：
4.4
作者：
Grima, Ramon
通讯作者：
Grima, Ramon
DOI：
10.1073/pnas.1008965107
发表时间：
2010-12-21
影响因子：
11.1
作者：
Ochab-Marcinek, Anna;Tabaka, Marcin
通讯作者：
Tabaka, Marcin
影响因子：
7
作者：
Elf, J;Ehrenberg, M
通讯作者：
Ehrenberg, M
影响因子：
3.3
作者：
Qian, Hong;Shi, Pei-Zhe;Xing, Jianhua
通讯作者：
Xing, Jianhua
DOI：
10.1007/978-3-319-45833-5_2
发表时间：
2017-01-01
期刊：
MODELING CELLULAR SYSTEMS
影响因子：
--
作者：
Andreychenko, Alexander;Bortolussi, Luca;Wolf, Verena
通讯作者：
Wolf, Verena