利用代谢网络模型和线性规划法在线预测谷氨酸发酵中产物浓度

在好氧型的谷氨酸发酵实验中发现，溶解氧（DO）对发酵性能有很大的影响，谷氨酸的生成方式也因此有很大不同：较低的DO水平能够延长产酸期、提高谷氨酸的最终浓度，但是代谢副产物——乳酸也有较大程度的积蓄；而DO水平过高，虽然代谢副产物不会生成积蓄，但菌体消亡过快导致产酸期缩短、谷氨酸的最终浓度降低．同时，谷氨酸的生成方式与发酵过程中摄氧率（OUR）和CO2的释放率（CER）有着非常紧密的关联．作者利用代谢网络模型并结合使用线性规划优化法，通过在线测定OUR和CER，比较准确地在线推定出发酵过程中谷氨酸的质量浓度变化。与传统的非构造式动力学模型相比，上述预测方法具有建模简单、模型物理意义明确、通用性能好等优点，为后续过程的在线控制和优化提供一种全新和有效的途径。

On-Line Prediction of Product Concentrations in Glutamate Fermentation Using Metabolic Network Model and Linear Programming

The experimental data showed that, in aerobic glutamate fermentation with strain C.glutamicum S9114, glutamate produced in different ways with different patterns of O_(2) uptake rate (OUR), CO_(2) evolution rate (CER) and by-products accumulation at different dissolved oxygen (DO) levels: glutamate production could be prolonged but with higher lactate accumulation at a lower DO level; while glutamate production stopped at low concentration level with a quick decline of both OUR and CER when controlling DO at a higher level. In this study, a metabolic network model combined with the linear programming optimization was used to on-line predict the glutamate production under different DO levels, with only OUR and CER be on-line measured. The results indicated the power and advantages of the metabolic network model over the traditional unstructured dynamic models, in terms of easy parameter identification, clear biochemical interpreting of data, as well as prediction performance. The proposed method supplies a novel and alternative way for on-line control and optimization of fermentation processes.