Coleman Matthew C, Buck Kristan K S, Block David E
Department of Chemical Engineering and Material Science, University of California, 1 Shields Avenue, Davis, CA 95616, USA.
Biotechnol Bioeng. 2003 Nov 5;84(3):274-85. doi: 10.1002/bit.10719.
Using a fermentation database for Escherichia coli producing green fluorescent protein (GFP), we have implemented a novel three-step optimization method to identify the process input variables most important in modeling the fermentation, as well as the values of those critical input variables that result in an increase in the desired output. In the first step of this algorithm, we use either decision-tree analysis (DTA) or information theoretic subset selection (ITSS) as a database mining technique to identify which process input variables best classify each of the process outputs (maximum cell concentration, maximum product concentration, and productivity) monitored in the experimental fermentations. The second step of the optimization method is to train an artificial neural network (ANN) model of the process input-output data, using the critical inputs identified in the first step. Finally, a hybrid genetic algorithm (hybrid GA), which includes both gradient and stochastic search methods, is used to identify the maximum output modeled by the ANN and the values of the input conditions that result in that maximum. The results of the database mining techniques are compared, both in terms of the inputs selected and the subsequent ANN performance. For the E. coli process used in this study, we identified 6 inputs from the original 13 that resulted in an ANN that best modeled the GFP fluorescence outputs of an independent test set. Values of the six inputs that resulted in a modeled maximum fluorescence were identified by applying a hybrid GA to the ANN model developed. When these conditions were tested in laboratory fermentors, an actual maximum fluorescence of 2.16E6 AU was obtained. The previous high value of fluorescence that was observed was 1.51E6 AU. Thus, this input condition set that was suggested by implementing the proposed optimization scheme on the available historical database increased the maximum fluorescence by 55%.
利用一个用于生产绿色荧光蛋白（GFP）的大肠杆菌发酵数据库，我们实施了一种新颖的三步优化方法，以确定在发酵建模中最重要的过程输入变量，以及那些能使期望输出增加的关键输入变量的值。在该算法的第一步中，我们使用决策树分析（DTA）或信息论子集选择（ITSS）作为数据库挖掘技术，来确定哪些过程输入变量能最佳地对实验发酵中监测到的每个过程输出（最大细胞浓度、最大产物浓度和生产率）进行分类。优化方法的第二步是使用第一步中确定的关键输入，训练过程输入 - 输出数据的人工神经网络（ANN）模型。最后，使用一种包括梯度和随机搜索方法的混合遗传算法（hybrid GA）来确定ANN建模的最大输出以及导致该最大值的输入条件的值。从所选输入和后续ANN性能两方面对数据库挖掘技术的结果进行了比较。对于本研究中使用的大肠杆菌过程，我们从最初的13个输入中确定了6个输入，这些输入产生了一个能对独立测试集的GFP荧光输出进行最佳建模的ANN。通过将混合GA应用于所开发的ANN模型，确定了导致建模最大荧光的六个输入的值。当在实验室发酵罐中测试这些条件时，则获得了实际最大荧光值2.16E6 AU。之前观察到的荧光高值为为1.51E6 AU。因此，通过在可用历史数据库上实施所提出的优化方案所建议的这个输入条件集，使最大荧光增加了55%。
