BACKGROUND:Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models. RESULTS:We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time. CONCLUSION:The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.

译文

背景技术:反向工程蜂窝网络目前是系统生物学中最具挑战性的问题之一。动态贝叶斯网络(DBN)似乎特别适用于通过对mRNA或蛋白质浓度的时间序列测量结果进行分析来推断细胞变量之间的关系。由于评估真实数据集上的推理结果存在争议,因此提出了使用模拟数据的建议。然而,尚未对使用连续变量,从而避免与离散化相关的信息丢失的DBN方法进行了广泛的评估,并且大多数提议的方法已经处理了线性高斯模型。
结果:我们提出了动态高斯网络的一般化,以适应变量之间的非线性依赖性。作为测试新方法的基准数据集,我们使用了发芽酵母中细胞周期控制数学模型的数据,真实地再现了细胞系统的复杂性。我们评估了网络描述蜂窝系统动态的能力及其在重构变量之间真正的因果关系上的精度。我们还通过分析噪声对数据的影响以及不同采样时间的影响,测试了结果的鲁棒性。
结论:结果证实具有高斯模型的DBN可以有效地用于复杂细胞系统数据的第一级分析。推断的模型是简约的,并且具有令人满意的拟合优度。此外,这些网络不仅提供了细胞系统动力学的现象学描述,而且还能够提出有关变量之间因果关系的假设。提出的高斯模型非线性泛化得出的模型的拟合优度比线性模型低,但具有恢复变量之间真正基础联系的能力。

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