整合组学数据的代谢网络模型研究进展  

Progress on genome-scale metabolic models integrated with multi-omics data

作　　者：王雪亮 张芸[1,3] 温廷益[1,3,4] Xueliang Wang;Yun Zhang;Tingyi Wen(Key Laboratory of Pathogenic Microbiology and Immunology,Institute of Microbiology,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;Innovation Academy for Green Manufacture,Chinese Academy of Sciences,Beijing 100190,China;Savaid Medical School,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院微生物研究所,中国科学院病原微生物与免疫学重点实验室,北京100101 [2]中国科学院大学,北京100049 [3]中国科学院绿色过程制造创新研究院,北京100190 [4]中国科学院大学存济医学院,北京100049

出　　处：《科学通报》2021年第19期2393-2404,共12页Chinese Science Bulletin

基　　金：中国科学院A类战略性先导科技专项(XDA17010503);中国科学院绿色过程制造创新研究院A类重大示范工程项目(IAGM-2019-A02);国家自然科学基金(31870070,31870074,32071460)资助。

摘　　要：基因组规模代谢网络模型(genome-scale metabolic model,GEM)是根据化学计量平衡原理,基于基因-蛋白-反应三者关联,建立包含细胞生长必需的生化反应数学模型.细胞代谢网络存在复杂的调控,通过基因和蛋白表达量及代谢物浓度变化调节代谢反应通量,而这些数据无法直接反映代谢反应的通量.GEM研究面临的一个挑战是如何整合不直接反映代谢通量的数据类型,并将这些调控过程在代谢模型中进行准确的描述.本文综述了将高通量组学数据整合到代谢化学计量模型中的方法,包括根据基因和蛋白的表达判定代谢反应状态、筛选核心代谢反应集、代谢反应通量边界约束、转录调控网络整合和多时间尺度基因动态表达约束等;比较分析了不同算法的优缺点和应用场景;介绍了GEM与多组学数据整合在解析生物代谢特征、分析遗传和环境扰动、筛选癌症治疗潜在药物靶标和抗代谢药物等方面的应用;展望了组学数据和代谢网络模型集成的发展趋势.The genome-scale metabolic network model(GEM)is a mathematical framework based on gene-protein-reaction associations combined with stoichiometric balance and is capable of facilitating the computation and prediction of multiscale phenotypes by optimizing the objective function of interest.It has been increasingly used as an important tool for understanding cellular metabolism and characterizing cell phenotypes.In cells,metabolism is tightly controlled by intricate regulatory mechanisms at the different system levels and is strictly regulated to ensure the dynamic adaptation of biochemical reaction fluxes for maintaining cell homeostasis to ultimately achieve optimal metabolic fitness.Advances in high-throughput screening and analysis technologies have generated massive amounts of genome sequences,along with transcriptomic,proteomic and metabolomic data,providing quantitative regulatory information to gain insights into cellular metabolism;however,integrating the available omics data into constraint-based metabolic models and quantitatively profiling genotype-phenotype relationships remains an outstanding challenge for computational biology.Here,we describe the recent developments in introducing macromolecular expression into GEMs and generating metabolic expression(ME)models,which increase the complexity and predictive capability of computational frameworks.Various algorithms employ different approaches to combine additional layers of omics data to limit the cone of allowable flux distributions in the metabolic model.In this review,we categorize all methods by five different grouping criteria and evaluate their practical perspectives.The first category of methods utilizes a threshold to distinguish active and inactive states of the corresponding reactions based on the gene expression measurement data.The second uses omics data to build cell-and tissue-specific models of human metabolism by removing unexpressed reactions from the global human metabolic network.The third category of methods involves modifying react

关 键 词：代谢网络模型 转录组学 蛋白质组学 代谢组学 组学数据整合 

分 类 号：Q811.4[生物学—生物工程]