Advances and applications of machine learning and intelligent optimization algorithms in genome‑scale metabolic network models

作     者：Lidan Bai Qi You Chenyang Zhang Jun Sun Long Liu Hengyang Lu Qidong Chen 

作者机构：Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational IntelligenceWuxiJiangsuChina Key Laboratory of Carbohydrate Chemistry and BiotechnologyMinistry of EducationJiangnan UniversityWuxi 214122China Science Center for Future FoodsJiangnan UniversityWuxi 214122China 

出 版 物：《Systems Microbiology and Biomanufacturing》 (系统微生物学与生物制造（英文）)

年 卷 期：2023年第3卷第2期

页      面：193-206页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 0836[工学-生物工程] 

基　　金：supported by the National key research and development program of China(Grant no.2020YFA0908303). 

主　　题：Genome-scale metabolic models Machine learning Intelligent optimization Metabolic engineering 

摘      要：Due to the increasing demand for microbially manufactured products in various industries,it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired products.Recently,with the gradual cross-fertilization between computer science and bioinformatics fields,machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models(GSMMs)based on constrained optimization methods,and many high-quality related works have been published.Therefore,this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering,with special emphasis on GSMMs.Specifically,the development history of GSMMs is first reviewed.Then,the analysis methods of GSMMs based on constraint optimization are presented.Next,this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic models.In addition,the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.