Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method
Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method
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DOI：
10.1093/bioinformatics/btr293
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发表时间：
2011-07-15
期刊：
影响因子：
5.8
通讯作者：
Gunawan, Rudiyanto
中科院分区：
文献类型：
--
作者：
Jia, Gengjie;Stephanopoulos, Gregory N.;Gunawan, Rudiyanto
动机：代谢物浓度的时间序列测量变得越来越普遍，为使用常微分方程 (ODE) 构建代谢网络动力学模型提供数据。然而，在实践中，此类时程数据通常不完整且存在噪声，并且根据这些数据估计动力学参数具有挑战性。由于数据和计算方面的实际限制，例如求解刚性 ODE 和寻找估计问题的全局最优解，激发了开发一种可以规避其中一些约束的新估计程序的动机。结果：在这项工作中，提出了一种增量迭代参数估计方法，该方法在两个估计阶段之间进行组合和迭代。其中一个阶段涉及解耦方法，其中使用斜率误差的最小化来估计与测量的代谢物相关的模型参数的子集。接下来是另一个阶段，其中一次求解一个方程的 ODE 模型，并通过最小化浓度误差来获得剩余的模型参数。这种两相方法的性能在乳酸乳球菌的通用分支代谢途径和糖酵解途径上进行了测试。结果表明，即使某些信息丢失，该方法也能有效地获得准确的参数估计。
Motivation: Time-series measurements of metabolite concentration have become increasingly more common, providing data for building kinetic models of metabolic networks using ordinary differential equations (ODEs). In practice, however, such time-course data are usually incomplete and noisy, and the estimation of kinetic parameters from these data is challenging. Practical limitations due to data and computational aspects, such as solving stiff ODEs and finding global optimal solution to the estimation problem, give motivations to develop a new estimation procedure that can circumvent some of these constraints.Results: In this work, an incremental and iterative parameter estimation method is proposed that combines and iterates between two estimation phases. One phase involves a decoupling method, in which a subset of model parameters that are associated with measured metabolites, are estimated using the minimization of slope errors. Another phase follows, in which the ODE model is solved one equation at a time and the remaining model parameters are obtained by minimizing concentration errors. The performance of this two-phase method was tested on a generic branched metabolic pathway and the glycolytic pathway of Lactococcus lactis. The results showed that the method is efficient in getting accurate parameter estimates, even when some information is missing.