基于约束的基因组规模代谢网络模型构建方法研究进展

Advances in the development of constraint-based genome-scale metabolic network models

摘要:

基因组规模代谢网络模型 (Genome-scale metabolic network model，GSMM) 正成为细胞代谢特性研究的重要工具，经过多年发展相关理论方法取得了诸多进展。近年来，在基础GSMM模型基础上，通过整合基因组、转录组、蛋白组和热力学数据，实现基于各种约束的GSMM构建，在基因靶点识别、系统代谢工程、药物发现、人类疾病机理研究等多个方面取得了进一步的发展和理论突破。文中重点综述包括转录组约束、蛋白组约束、以及热力学约束条件在GSMM中的实施方法、相应方法的不足及应用限制等。最后介绍了如何综合运用转录、蛋白及热力学约束，实现GSMM的全整合模型及其细化，并对基于约束的GSMM构建及应用前景进行了展望。

Abstract:

Genome-scale metabolic network model (GSMM) is becoming an important tool for studying cellular metabolic characteristics, and remarkable advances in relevant theories and methods have been made. Recently, various constraint-based GSMMs that integrated genomic, transcriptomic, proteomic, and thermodynamic data have been developed. These developments, together with the theoretical breakthroughs, have greatly contributed to identification of target genes, systems metabolic engineering, drug discovery, understanding disease mechanism, and many others. This review summarizes how to incorporate transcriptomic, proteomic, and thermodynamic-constraints into GSMM, and illustrates the shortcomings and challenges of applying each of these methods. Finally, we illustrate how to develop and refine a fully integrated GSMM by incorporating transcriptomic, proteomic, and thermodynamic constraints, and discuss future perspectives of constraint-based GSMM.