Huang Jun, Mei Le-He, Xia Jiang
Department of Chemical and Biochemical Engineering, Zhejiang University, Hangzhou 310027 PR China.
Biotechnol Bioeng. 2007 Apr 1;96(5):924-31. doi: 10.1002/bit.21162.
The biotransformation of L-sodium glutamate (L-MSG) to gamma-aminobutyric acid (GABA) catalyzed by the cells of Lactobacillus brevis with higher glutamate decarboxylase activity was investigated. The results showed that pH, temperature, and FeSO(4) x 7H(2)O concentration had significantly positive effect on GABA yield. The individual and interactive effects of pH, temperature, and FeSO(4) x 7H(2)O concentration were further optimized in terms of GABA yield. In the present work, an artificial neural network (ANN) and response surface methodology (RSM) models were developed, which incorporated pH, temperature, and FeSO(4) x 7H(2)O concentration as input variables, and GABA yield as output variable. The optimized ANN topology included four neurons in the hidden layer and the best network architecture was 3-4-1. The trained ANN gave total root-mean square error (sigma) equal to 1.84 for GABA yield while the RSM gave sigma equal to 2.63. The results demonstrated a slightly higher prediction accuracy of ANN compared to RSM. The modeled maximum GABA yield was identified by applying particle swarm optimization algorithm to the ANN model developed. The modeled maximum GABA yield reached 91 mM under the following optimal conditions: 25 mL Na(2)HPO(4)-citric acid buffer (100 mM, pH 4.23), 120 mM L-MSG, 0.83 g/L FeSO(4) x 7H(2)O, 10 microM PLP, the resting cells obtained from a 60-h culture broth, 2.68 g dry cell weight (DCW)/L, and without agitation at 40 degrees C for 5 h. The previous high value of GABA yield that was observed was 81.8 mM. The optimized conditions allowed GABA yield to be increased from 81.8 to 90.57 mM after verification experiments test.
研究了具有较高谷氨酸脱羧酶活性的短乳杆菌细胞催化L-谷氨酸钠（L-MSG）转化为γ-氨基丁酸（GABA）的过程。结果表明，pH、温度和FeSO₄·7H₂O浓度对GABA产量有显著的正向影响。从GABA产量方面进一步优化了pH、温度和FeSO₄·7H₂O浓度的单独及交互作用。在本研究中，开发了人工神经网络（ANN）和响应面方法（RSM）模型，将pH、温度和FeSO₄·7H₂O浓度作为输入变量，GABA产量作为输出变量。优化后的ANN拓扑结构在隐藏层中有四个神经元，最佳网络架构为3-4-1。训练后的ANN对GABA产量的总均方根误差（σ）等于1.84，而RSM的σ等于2.63。结果表明，ANN的预测精度略高于RSM。通过将粒子群优化算法应用于所开发的ANN模型，确定了模拟的最大GABA产量。在以下最佳条件下，模拟的最大GABA产量达到91 mM：25 mL Na₂HPO₄-柠檬酸缓冲液（100 mM，pH 4.23）、120 mM L-MSG、0.83 g/L FeSO₄·7H₂O、10 μM 磷酸吡哆醛、从60小时培养液中获得的静止细胞、2.68 g干细胞重量（DCW）/L，在40℃下不搅拌5小时。之前观察到的GABA产量的高值为81.8 mM。经过验证实验测试，优化条件使GABA产量从81.8 mM提高到90.57 mM。
