Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges

被引：17

作者：

Bi, Xinyu ^{[1
,2
]}

Liu, Yanfeng ^{[1
,2
]}

Li, Jianghua ^{[1
,2
]}

Du, Guocheng ^{[1
,2
]}

Lv, Xueqin ^{[1
,2
]}

Liu, Long ^{[1
,2
]}

机构：

[1] Jiangnan Univ, Key Lab Carbohydrate Chem & Biotechnol, Minist Educ, Wuxi 214122, Jiangsu, Peoples R China

[2] Jiangnan Univ, Sci Ctr Future Foods, Minist Educ, Wuxi 214122, Jiangsu, Peoples R China

来源：

BIOMOLECULES | 2022年 / 12卷 / 05期

基金：

中国国家自然科学基金;

关键词：

multiscale genome-scale metabolic models; multiconstraint models; multiomics models; machine learning; whole-cell models; TRANSCRIPTIONAL REGULATORY NETWORKS; ESCHERICHIA-COLI; RECONSTRUCTION; INTEGRATION; BALANCE; CONSTRAINTS; GENERATION; PLATFORMS; PHENOTYPE; ALGORITHM;

D O I：

10.3390/biom12050721

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene-protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed.

引用

页数：21

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