整合转录组学数据的代谢网络研究进展  

Development of integrating transcriptomic data into matebolic network analysis

作　　者：汪慧[1] 丁德武[1] 孙啸[1] 谢建明[1] 

机构地区：[1]东南大学生物科学与医学工程学院,南京210096

出　　处：《生物信息学》2016年第3期160-166,共7页Chinese Journal of Bioinformatics

基　　金：国家自然科学基金项目(61472078)

摘　　要：高通量测序技术的快速发展催生了涵盖各层次细胞生命活动的组学数据,如转录组学数据、蛋白质组学数据和互作组学数据等。同时,全基因组代谢网络模型在不断完善和增多。整合组学数据,对生物细胞的代谢网络进行更深入的模拟分析成为目前微生物系统生物学研究的热点。目前整合转录组学数据进行全基因组代谢网络分析的方法主要以流量平衡分析(FBA)为基础,通过辨识不同条件下基因表达的变化,进而优化目标函数以得到相应的流量分布或代谢模型。本文对整合转录组学数据的FBA分析方法进行总结和比较,并详细阐述了不同方法的优缺点,为分析特定问题选择合适的方法提供参考。With the advent of high-throughput technologies,the field of systems biology has amassed an abundance of developed metabolic network models and ＂omics ＂ data,such as transcriptomic data,proteomic data and interactomic data. How to integrate omics data into metabolic network for further simulation analysis is becoming a hot spot of the microbial systems biology research. Several published studies have successfully demonstrated that the flux balance analysis（ FBA）,a constraint-based modeling approach,can be used to integrate transcriptomic data into genome-scale metabolic network model reconstructions to generate predictive computational models. In this review,we summarize such FBA-based methods for intergrating expression data into genome-scale metabolic network reconstruction,highlighting the advantages as well as the limitations,and offer the suggestion to select appropriate method to a specific issue.

关 键 词：代谢网络 转录组学 流量平衡分析 算法 

分 类 号：Q493.2[生物学—生理学]