多约束代谢网络模型的研究进展

Development of metabolic models with multiple constraints: a review

摘要:

基于约束的基因组尺度代谢网络模型（genome-scale metabolic models，GEMs）分析已被广泛应用于代谢表型的预测。而实际细胞中代谢速率除计量学约束外，还受到酶资源可用性和反应热力学可行性等其他因素影响，在GEMs中整合酶资源约束或者热力学约束构建多约束代谢网络模型可以进一步缩小优化解空间，提升细胞表型预测的准确性。文中主要对近年来多约束模型的研究进展进行了综述，介绍了不同多约束模型的构建方法，以及其在研究基因敲除影响、预测可行途径和提供代谢瓶颈信息等方面的应用。将多种约束条件进一步与代谢模型整合，可更准确地预测细胞代谢的瓶颈和关键调控改造靶点，从而为工业菌种代谢工程改造提供精确的设计指导。

Abstract:

Constraint-based genome-scale metabolic network models (genome-scale metabolic models, GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric constraints, other constraints such as enzyme availability and thermodynamic feasibility may also limit the cellular phenotype solution space. Recently, extended GEM models considering either enzymatic or thermodynamic constraints have been developed to improve model prediction accuracy. This review summarizes the recent progresses on metabolic models with multiple constraints (MCGEMs). We presented the construction methods and various applications of MCGEMs including the simulation of gene knockout, prediction of biologically feasible pathways and identification of bottleneck steps. By integrating multiple constraints in a consistent modeling framework, MCGEMs can predict the metabolic bottlenecks and key controlling and modification targets for pathway optimization more precisely, and thus may provide more reliable design results to guide metabolic engineering of industrially important microorganisms.