Background: While Saccharomyces cerevisiae is a promising host for cost-effective biorefinary processes due to its tolerance to various stresses during fermentation, the metabolically engineered S. cerevisiae strains exhibited rather limited production of higher alcohols than that of Escherichia coli. Since the structure of the central metabolism of S. cerevisiae is distinct from that of E. coli, there might be a problem in the structure of the central metabolism of S. cerevisiae. In this study, the potential production of higher alcohols by S. cerevisiae is compared to that of E. coli by employing metabolic simulation techniques. Based on the simulation results, novel metabolic engineering strategies for improving higher alcohol production by S. cerevisiae were investigated by in silico modifications of the metabolic models of S. cerevisiae.
Results: The metabolic simulations confirmed that the high production of butanols and propanols by the metabolically engineered E. coli strains is derived from the flexible behavior of their central metabolism. Reducing this flexibility by gene deletion is an effective strategy to restrict the metabolic states for producing target alcohols. In contrast, the lower yield using S. cerevisiae originates from the structurally limited flexibility of its central metabolism in which gene deletions severely reduced cell growth.
Conclusions: The metabolic simulation demonstrated that the poor productivity of S. cerevisiae was improved by the introduction of E. coli genes to compensate the structural difference. This suggested that gene supplementation is a promising strategy for the metabolic engineering of S. cerevisiae to produce higher alcohols which should be the next challenge for the synthetic bioengineering of S. cerevisiae for the efficient production of higher alcohols.
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Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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Site | Modification | Modifier | Source | Reference |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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Evidence ID | Analyze ID | File | Description |
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