Automated smoother for the numerical decoupling of dynamics models.
Automated smoother for the numerical decoupling of dynamics models.
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DOI：
10.1186/1471-2105-8-305
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发表时间：
2007-08-21
影响因子：
3
通讯作者：
Almeida JS
中科院分区：
文献类型：
--
作者：
Vilela M;Borges CC;Vinga S;Vasconcelos AT;Santos H;Voit EO;Almeida JS
复杂生物系统动力学模型的结构识别是其逆向工程的基础。生物化学系统理论（BST）提供了一个特别方便的解决方案，因为它的参数是动力学阶数，直接识别底层网络的拓扑结构的过程。我们以前提出了一个数值解耦程序，允许识别复杂的生物过程的多变量动态模型。虽然这里在BST的上下文中进行了描述，但是该过程对于信号提取具有普遍适用性。我们最初的实现依赖于人工神经网络（ANN），这在平滑时间过程中会引起轻微的不希望的偏差。作为一种替代方案，我们在这里提出了一个适应的惠特克的平滑，并证明其作用在一个强大的，全自动的结构识别过程。在这份报告中，我们提出了一个强大的，完全自动化的解决方案，从时间序列中提取信号，这是生物系统模型的有效逆向工程的先决条件。惠特克的平滑的信息理论的背景下重新制定和扩展的发展自适应信号分割考虑到异构噪声结构。由此产生的程序可用于任意时间序列与非平稳噪声过程，它在这里说明从体内NMR实验获得的代谢谱。平滑的解决方案，是免费的参数偏差允许微分，这是至关重要的数值解耦微分方程系统。该方法适用于从具有非平稳噪声结构的时间序列中提取信号，并可用于将微分方程组数值解耦为代数方程组，从而构成了从多元实验时间序列中逆向工程机械模型描述的一个相当通用的工具。
Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittaker's smoother and demonstrate its role within a robust, fully automated structure identification procedure. In this report we propose a robust, fully automated solution for signal extraction from time series, which is the prerequisite for the efficient reverse engineering of biological systems models. The Whittaker's smoother is reformulated within the context of information theory and extended by the development of adaptive signal segmentation to account for heterogeneous noise structures. The resulting procedure can be used on arbitrary time series with a nonstationary noise process; it is illustrated here with metabolic profiles obtained from in-vivo NMR experiments. The smoothed solution that is free of parametric bias permits differentiation, which is crucial for the numerical decoupling of systems of differential equations. The method is applicable in signal extraction from time series with nonstationary noise structure and can be applied in the numerical decoupling of system of differential equations into algebraic equations, and thus constitutes a rather general tool for the reverse engineering of mechanistic model descriptions from multivariate experimental time series.
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影响因子：
--
作者：
KULLBACK, S;LEIBLER, RA
通讯作者：
LEIBLER, RA
影响因子：
6.8
作者：
BENVENISTE, A;GOURSAT, M;RUGET, G
通讯作者：
RUGET, G
影响因子：
7.8
作者：
HORNIK, K;STINCHCOMBE, M;WHITE, H
通讯作者：
WHITE, H
影响因子：
5.8
作者：
Kikuchi, S;Tominaga, D;Tomita, M
通讯作者：
Tomita, M
影响因子：
7.4
作者：
Eilers, PHC
通讯作者：
Eilers, PHC