多组学整合下酿酒酵母酶磷酸化修饰与异构调节对代谢流调控机制的基础研究

基于多组学数据整合的系统生物学方法为探索细胞代谢调控机制开辟了崭新的技术路线，成为近年来研究热点。在计量学基础上进行基因组规模代谢网络模型构建为多组学数据整合提供了有效工具，然而动力学信息的缺失严重制约了该模型的适用范围。针对这一问题，本项目拟以酿酒酵母为研究对象，在获得不同稳态条件下蛋白组、磷酸化蛋白组、代谢物组和代谢流组数据的基础上，利用Linlog动力学方程实现多组学数据的定量整合，建立酿酒酵母胞内酶在体动力学模型；其次，通过代谢稳态下刺激响应实验中代谢物浓度动态变化的测定验证构建的在体动力学模型。最后，利用验证的在体动力学模型，系统地研究酶磷酸化修饰和异构调节对代谢流的影响，揭示其内在调控机制。本项目通过高质量动力学模型的构建探索代谢流随异构调节物浓度及酶磷酸化水平的变化规律，在揭示酿酒酵母代谢流调控的内在机制方面具有重要的科学意义。

Multi-omics data integration based methods in system biology are becoming a new hot research point. Stoichiometry based genome-scale metabolic network construction provides a useful tool for integrating multi-omics data. However, regardless of kinetics of individual enzymes, this method is limited to specific situations. Regarding this problem, Saccharomyces cerevisiae is chosen as a model system. Based on data of proteome, phosphoproteome, metabolome, and fluxome under different steady state conditions, the Linlog kinetic equation will be utilized to integrate all the omics data. As a result, an in vivo kinetic model of S. cerevisiae enzymes will be formulated. The model will then be validated through measurement of dynamic intracellular metabolites data under stimulus-response experiment. Finally, the validated in vivo kinetic model will be applied to systematically investigate effects of enzyme phosphorylation level and allosteric regulation on flux, and to discover its inner regulation mechanism. The project will try to interpret the flux regulation mechanisms on allosteric regulator and enzyme phosphorylation level, and it posesses highly scientific relevance on understanding flux regulation mechanism of S. cerevisiae.