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
•This mini-review updates the literature on almost all published GSMMs since 1999.
•Detailed insights into the development of the first- and next-generation GSMMs.
•The application of GSMMs is summarized, and the prospects of integrating machine learning are emphasized.
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This work was supported by grants from the National Key R & D Program of China (2021YFC2100700) and the National Natural Science Foundation of China (31801522, 21808196).
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CY, XYW, NX, and WZ wrote the manuscript. TQS and XMS generated the figures and tables. CG proofread the manuscript. All authors read and approved the final manuscript.
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Ye, C., Wei, X., Shi, T. et al. Genome-scale metabolic network models: from first-generation to next-generation. Appl Microbiol Biotechnol 106, 4907–4920 (2022). https://doi.org/10.1007/s00253-022-12066-y
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DOI: https://doi.org/10.1007/s00253-022-12066-y