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Investigate Data Dependency for Dynamic Gene Regulatory Network Identification through High-dimensional Differential Equation Approach

机译:通过高维微分方程方法研究数据依赖性以进行动态基因调控网络识别

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Gene regulation plays a fundamental role in biological activities. The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. We proposed a comprehensive statistical procedure for ODE model to identify the dynamic GRN. In this article, we applied this model to different segments of time course gene expression data from a simulation experiment and a yeast cell cycle study. We found that the two cell cycle and one cell cycle data provided consistent results, but half cell cycle data produced biased estimation. Therefore, we may conclude that the proposed model can quantify both two cell cycle and one cell cycle gene expression dynamics, but not for half cycle dynamics. The findings suggest that the model can identify the dynamic GRN correctly if the time course gene expression data are sufficient enough to capture the overall dynamics of underlying biological mechanism.
机译:基因调控在生物活动中起着基本作用。基因调控网络(GRN)是一个高维复杂系统,可以用各种数学或统计模型表示。常微分方程(ODE)模型是流行的动态GRN模型之一。我们为ODE模型提出了一种全面的统计程序,以识别动态GRN。在本文中,我们将此模型应用于来自模拟实验和酵母细胞周期研究的时程基因表达数据的不同部分。我们发现,两个细胞周期数据和一个细胞周期数据提供了一致的结果,但是半个细胞周期数据产生了偏差估计。因此,我们可以得出结论,提出的模型可以量化两个细胞周期和一个细胞周期基因的表达动态,但不能量化半周期动态。这些发现表明,如果时程基因表达数据足以捕获潜在生物学机制的整体动力学,则该模型可以正确识别动态GRN。

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