Bioprocess Engineering Group, IIM-CSIC, Vigo, Spain.
J Phys Chem A. 2011 Aug 4;115(30):8426-36. doi: 10.1021/jp203158r. Epub 2011 Jul 14.
A new approach for parameter estimation in chemical kinetics has been recently proposed (Ross et al. Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 12777). It makes use of an optimization criterion based on a Generalized Fisher Equation (GFE). Its utility has been demonstrated with two reaction mechanisms, the chlorite-iodide and Oregonator, which are computationally stiff systems. In this Article, the performance of the GFE-based algorithm is compared to that obtained from minimization of the squared distances between the observed and predicted concentrations obtained by solving the corresponding initial value problem (we call this latter approach "traditional" for simplicity). Comparison of the proposed GFE-based optimization method with the "traditional" one has revealed their differences in performance. This difference can be seen as a trade-off between speed (which favors GFE) and accuracy (which favors the traditional method). The chlorite-iodide and Oregonator systems are again chosen as case studies. An identifiability analysis is performed for both of them, followed by an optimal experimental design based on the Fisher Information Matrix (FIM). This allows to identify and overcome most of the previously encountered identifiability issues, improving the estimation accuracy. With the new data, obtained from optimally designed experiments, it is now possible to estimate effectively more parameters than with the previous data. This result, which holds for both GFE-based and traditional methods, stresses the importance of an appropriate experimental design. Finally, a new hybrid method that combines advantages from the GFE and traditional approaches is presented.
一种新的化学动力学参数估计方法最近被提出(Ross 等人,Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 12777)。它利用基于广义 Fisher 方程(GFE)的优化准则。该方法在两个反应机制——亚氯酸盐-碘化物和 Oregonator 中得到了验证,这两个机制是计算上刚性的系统。在本文中,将基于 GFE 的算法的性能与通过求解相应的初值问题(我们为简单起见将这种方法称为“传统”)得到的观察浓度与预测浓度之间的平方距离最小化所获得的性能进行了比较。与“传统”方法相比,所提出的基于 GFE 的优化方法的性能存在差异。这种差异可以看作是速度(有利于 GFE)和准确性(有利于传统方法)之间的权衡。再次选择亚氯酸盐-碘化物和 Oregonator 系统作为案例研究。对它们都进行了可识别性分析,然后根据 Fisher 信息矩阵(FIM)进行了最佳实验设计。这允许识别并克服以前遇到的大多数可识别性问题,从而提高估计精度。利用从最佳设计实验中获得的新数据,现在可以有效地估计比以前数据更多的参数。这一结果对于基于 GFE 和传统方法的方法都成立,强调了适当的实验设计的重要性。最后,提出了一种新的混合方法,该方法结合了 GFE 和传统方法的优点。