文献类型：期刊文献

英文题名：Reconstruction and In Silico Analysis of Metabolic Network for an Oleaginous Yeast, Yarrowia lipolytica

作者：Pan, Pengcheng[1];Hua, Qiang[1]

机构：[1]E China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2012

卷号：7

期号：12

外文期刊名：PLOS ONE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000312064100130)】；

基金：This study was financially supported by National Basic Research Program of China (973 Program) (2012CB721101), Research Fund for the Doctoral Program of Higher Education of China (20110074110014), Fundamental Research Funds for the Central Universities of China (WF0913005), and partially supported by National Special Fund for State Key Laboratory of Bioreactor Engineering (2060204). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

语种：英文

摘要：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.