Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China.
State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
Biotechnol Bioeng. 2023 Aug;120(8):2301-2313. doi: 10.1002/bit.28493. Epub 2023 Jul 13.
Genome-scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number ( ) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme-constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme-constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 c), highly sensitive (1759 ), and nonsensitive (2502 ) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy.
基于酶约束的基因组规模代谢网络模型 (GSMM) 极大地提高了一般代谢模型。酶的周转率 ( ) 被用作扩展 GSMM 时限制反应的参数。因此，周转率在细胞代谢的预测准确性中起着至关重要的作用。在这项工作中，我们提出了一种酶约束 GSMM 参数优化方法。首先，对参数进行了敏感性分析，以选择对预测比生长速率影响最大的参数。然后，采用具有自适应突变策略的差分进化 (DE) 算法对参数进行优化。该算法可以动态选择五种不同的突变策略。最后，分析了优化模型的比生长速率预测、通量变异性和相平面，以进一步评估模型。优化了酿酒酵母的酶约束 GSMM，ecYeast8.3.4。敏感性分析的结果表明，优化变量可以根据敏感性分为三组：最敏感组（149 ）、高敏感组（1759 ）和不敏感组（2502 ）。基于敏感性分析的结果，开发了六种优化策略。结果表明，具有自适应突变策略的 DE 确实可以通过优化高敏感参数来改进模型。保留所有参数并优化高敏感参数是推荐的优化策略。
