Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks.
Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks.
复制标题
贝叶斯逆向工程蜂窝系统的方法：非线性高斯网络的仿真研究。
DOI：
10.1186/1471-2105-8-s5-s2
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发表时间：
2007-05-24
影响因子：
3
通讯作者：
Bellazzi, Riccardo
中科院分区：
文献类型：
--
作者：
Ferrazzi, Fulvia;Sebastiani, Paola;Ramoni, Marco F;Bellazzi, Riccardo
细胞网络逆向工程是当前系统生物学中最具挑战性的问题之一。动态贝叶斯网络(DBN)似乎特别适合于从mRNA或蛋白质浓度的时间序列测量分析中推断细胞变量之间的关系。由于在真实数据集上评估推理结果是有争议的，因此建议使用模拟数据。然而，使用连续变量从而避免了与离散化相关的信息损失的DBN方法还没有得到广泛的评估，并且大多数建议的方法都处理了线性高斯模型。我们提出了动态高斯网络的一种推广，以适应变量之间的非线性依赖关系。作为测试新方法的基准数据集，我们使用了来自萌芽酵母细胞周期控制的数学模型的数据，该模型真实地再现了细胞系统的复杂性。我们评估了网络描述细胞系统动力学的能力，以及它们在重建变量之间真实的潜在因果关系方面的精确度。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来测试结果的稳健性。结果证实，采用高斯模型的DBN可以有效地用于复杂细胞系统数据的一级分析。所推导的模型是简约的，并且具有令人满意的拟合优度。此外，这些网络不仅提供了对细胞系统动力学的现象学描述，而且还能够提出关于变量之间因果相互作用的假设。所提出的高斯模型的非线性推广产生的模型的特征是拟合优度略低于线性模型，但能够更好地恢复变量之间的真实潜在联系。
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. 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. 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.