Reconstruction And In Silico Analysis Of Genome-scale Metabolic Network Model Of Yarrowia Lipolytica

With energy scarcity and environmental pollution becoming more and more serious,biodiesel,a new-type energy,has been regarded as a substitute of non-renewable resources.Compared with animal fat and vegetable oil,microbial lipid is an ideal material of producing biodiesel for its efficient process cycle turnover and lower cost.Moreover,Yarrowia lipolytica is widely studied as a non-conventional model yeast owing to high level of lipid accumulation.Triacylglycerol?TAG?is a major component of liposome,and optimization of TAG synthesis pathway in Y.lipolytica can improve the production of lipid efficiently.In recent,with the rapid development of gene sequencing technology,genome-scale metabolic network model has been applied as a significant tool to systematically study physiological characteristics and metabolic mechanism.And genome-scale metabolic model of Y.lipolytica need to be modified continually for the updating of metabolism data.Therefore,a genome-scale metabolic model of Y.lipolytica was reconstructed based on a previous model iYL619_PCP published by our lab and another model iYali4.The novel model iYL₂.0 contains 645 genes,1083 metabolites and 1471 reactions,which was validated more effective than the two above models on simulations of specific growth rate and different carbon sources utilities.In addition,gene-level strategy and reaction-level strategy were systematically compared on feasibility and improvement of target production,which showed gene-level strategy was more effective than reaction-level strategy.Additionally,the model iYL₂.0 was employed to simulate overproduction of TAG in Y.lipolytica and 6 knockout gene targets and 6 overexpression gene targets were predicted by three in silico methods,namely OptGeneKnock,IdealKnock and APGC.And these potential strategies were mostly related with the supply and consumption of precursor,balance of redox and growth of cell.Based on analysis of metabolic pathway and relevant references,the potential gene-level targets were showed a high feasibility,which could provide efficient guidance for optimizing the biosynthesis of TAG in metabolic engineering.