系统开发代谢网络模型及预测算法促进解脂耶氏酵母油脂合成的改造提高

Modeling and prediction of cellular metabolic network is an important part of systems metabolic engineering, which needs to be developed and applied in depth. With the rational construction of cell metabolic model and the innovation of the simulation method, the metabolic characteristics of the cells can be understood more deeply together with better gene manipulation strategies to guide the improvement of the target metabolic performance. In this project, based on the metabolic network model of Yarrowia lipolytica developed by the applicant’s group, the structure of the simulation algorithms will be developed and optimized while updating the metabolic reaction information, integrating the relevant transcriptional and metabolic flux information. The metabolic model-based predictions will therefore be performed to provide effective metabolic engineering strategies for promoting the lipid synthesis of Y. lipolytica, which will also be experimentally validated. This study provides us not only a better understanding of characteristics of lipid biosynthesis in the strain, but also more importantly, an important metabolic engineering research platform for efficiently engineering Y. lipolytica to synthesize multiple useful chemicals including organic acids, terpenoid compounds, and fatty acid derivatives.

细胞代谢网络模型化及仿真预测是系统代谢工程的重要组成部分，也是一个需要不断开发和实践应用的研究领域。基于合理的细胞代谢模型构建和模拟方法创新，可以更为深入地理解细胞代谢特性并获得更优的基因操作策略用以指导目标代谢性能的提高，因而细胞代谢模型法正在得到广泛的关注。本项目将以申请人课题组领先开发的解脂耶氏酵母细胞代谢网络模型为基础，在更新拓展代谢反应信息、整合相关转录组学和代谢流量组学信息的同时，优化改进模拟算法结构，为促进解脂耶氏酵母的油脂合成效率提供有效的代谢工程操作策略，并从菌株分子构建及培养比较等方面进行实验验证及模型反馈。通过本项目的研究，不但可以深入地认识该产油脂模式酵母菌的油脂合成特性，更有意义的是能够为解脂耶氏酵母这一重要工业生物菌株高效合成有机酸、萜类化合物、脂肪酸衍生物等有用化合物构筑更为完善的代谢工程研究平台。

本项目通过更新并重构解脂耶氏酵母细胞代谢反应网络模型以及开发相应的代谢预测比较方法来仿真细胞内乙酰辅酶A、脂肪酸等重要油脂关联代谢物的合成，系统分析细胞关键代谢途径在不同基因及培养条件改变情况下的代谢流量分布，识别筛选有利于提供目标产物合成的代谢反应或基因靶点，为实际细胞代谢工程改造、脂肪酸及其衍生物、油脂类代谢物、以及萜类化合物的高效合成提供菌株改造思路及方向，并为分析解脂耶氏酵母细胞内脂肪酸关联代谢机制提供有用的理论指导。