Genome-scale Reconstruction And In Silico Analysis Of The Metabolic Network Model For An Oleaginous Yeast, Yarrowia Lipolytica

With the rapid development of global economy, a series of problems especially energy scarcity becomes more and more serious. The use of renewable energy sources is becoming increasingly necessary. Recently many researches have been focused on identifying suitable microorganisms those are able to store additional energy in the form of lipids, which can be employed to make renewable energy to replace conventional fossil fuels.The yeast Yarrowia lipolytica is often found in environments rich in hydrophobic substrates, such as alkanes or lipids, and has developed sophisticated mechanisms for the use of hydrophobic substrates (HS) as the sole carbon source. As an oleaginous yeast, Y. lipolytica is able to accumulate large amounts of lipids, in some cases, more than50%of its dry weight [DW].The availability of hundreds of sequenced genomes has ushered in a new era in biology, allowing the study of cellular life at a systems level. Metabolic network reconstruction has become an indispensable tool for studying the systems biology of metabolism. Recently, the complete genome sequence of Y. lipolytica has been determined and the omics data of Y. lipolytica are accumulated day by day. 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 which can then be regarded as a new useful database to generate other reconstructions. The generated model iYL619_PCP accounts for619genes.843metabolites and1142reactions including236transport reactions.125exchange reactions and13spontaneous reactions. The in-silico model successfully predicted the minimal media and the growing abilities on different substrates. With flux balance analysis, batch culture of Y. lipolytica was simulated. Single gene knockouts and double gene knockouts were also simulated to predict the essential genes, non-essential genes and lethal gene pairs. 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.