ArticlePloS one2012
Reconstruction and in silico analysis of metabolic network for an oleaginous yeast, Yarrowia lipolytica.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Construction and application of the genome-scale metabolic model ofFrontiers in bioengineering and biotechnology · 2023Article
- Pareto optimal metabolic engineering for the growth-coupled overproduction of sustainable chemicals.Biotechnology and bioengineering · 2022Article
- Dissecting carbon metabolism ofComputational and structural biotechnology journal · 2022Article
- Review
- Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species.FEMS yeast research · 2021Article
- Fine-tuning mitochondrial activity in Yarrowia lipolytica for citrate overproduction.Scientific reports · 2021Article
- Genome-scale metabolic modeling underscores the potential of Cutaneotrichosporon oleaginosus ATCC 20509 as a cell factory for biofuel production.Biotechnology for biofuels · 2021Article
- Genome-scale metabolic reconstruction of the non-model yeastMetabolic engineering communications · 2020Article
- Synthetic biology, systems biology, and metabolic engineering of Yarrowia lipolytica toward a sustainable biorefinery platform.Journal of industrial microbiology & biotechnology · 2020Review
- Genome-wide sequencing and metabolic annotation of Pythium irregulare CBS 494.86: understanding Eicosapentaenoic acid production.BMC biotechnology · 2019Article
- In silico identification of metabolic engineering strategies for improved lipid production inBiotechnology for biofuels · 2019Article
- Article
- Synthetic biology tools for engineering Yarrowia lipolytica.Biotechnology advances · 2018Review
- Engineering Yarrowia lipolytica for Use in Biotechnological Applications: A Review of Major Achievements and Recent Innovations.Molecular biotechnology · 2018Review
- Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism ofProcesses (Basel, Switzerland) · 2018Article
- Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica.BMC systems biology · 2018Article
- Biotechnological production of bio-based long-chain dicarboxylic acids with oleogenious yeasts.World journal of microbiology & biotechnology · 2017Review
- Genome-scale modeling of yeast: chronology, applications and critical perspectives.FEMS yeast research · 2017Review
- Comprehensive reconstruction and evaluation ofBioresources and bioprocessing · 2017Article
- Metabolic Engineering of Oleaginous Yeasts for Production of Fuels and Chemicals.Frontiers in microbiology · 2017Review
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Authors and funding
2 authors.
Funding
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Abstract
With the emergence of energy scarcity, the use of renewable energy sources such as biodiesel is becoming increasingly necessary. Recently, many researchers have focused their minds on Yarrowia lipolytica, a model oleaginous yeast, which can be employed to accumulate large amounts of lipids that could be further converted to biodiesel. In order to understand the metabolic characteristics of Y. lipolytica at a systems level and to examine the potential for enhanced lipid production, a genome-scale compartmentalized metabolic network was reconstructed based on a combination of genome annotation and the detailed biochemical knowledge from multiple databases such as KEGG, ENZYME and BIGG. The information about protein and reaction associations of all the organisms in KEGG and Expasy-ENZYME database was arranged into an EXCEL file that can then be regarded as a new useful database to generate other reconstructions. The generated model iYL619_PCP accounts for 619 genes, 843 metabolites and 1,142 reactions including 236 transport reactions, 125 exchange reactions and 13 spontaneous reactions. The in silico model successfully predicted the minimal media and the growing abilities on different substrates. With flux balance analysis, single gene knockouts were also simulated to predict the essential genes and partially essential genes. In addition, flux variability analysis was applied to design new mutant strains that will redirect fluxes through the network and may enhance the production of lipid. This genome-scale metabolic model of Y. lipolytica can facilitate system-level metabolic analysis as well as strain development for improving the production of biodiesels and other valuable products by Y. lipolytica and other closely related oleaginous yeasts.
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