Genome-scale metabolic network models: from first-generation to next-generation.
Appl Microbiol Biotechnol
; 106(13-16): 4907-4920, 2022 Aug.
Article
em En
| MEDLINE
| ID: mdl-35829788
ABSTRACT
Over the last two decades, thousands of genome-scale metabolic network models (GSMMs) have been constructed. These GSMMs have been widely applied in various fields, ranging from network interaction analysis, to cell phenotype prediction. However, due to the lack of constraints, the prediction accuracy of first-generation GSMMs was limited. To overcome these limitations, the next-generation GSMMs were developed by integrating omics data, adding constrain condition, integrating different biological models, and constructing whole-cell models. Here, we review recent advances of GSMMs from the first generation to the next generation. Then, we discuss the major application of GSMMs in industrial biotechnology, such as predicting phenotypes and guiding metabolic engineering. In addition, human health applications, including understanding biological mechanisms, discovering biomarkers and drug targets, are also summarized. Finally, we address the challenges and propose new trend of GSMMs. KEY POINTS â¢This mini-review updates the literature on almost all published GSMMs since 1999. â¢Detailed insights into the development of the first- and next-generation GSMMs. â¢The application of GSMMs is summarized, and the prospects of integrating machine learning are emphasized.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes e Vias Metabólicas
/
Engenharia Metabólica
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Appl Microbiol Biotechnol
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China