A genome-scale metabolic model (GSMM) is an indispensable tool for the study of metabolism that adopts a systems biology approach to integrate data from genomics, transcriptomics, proteomics and metabolomics. It has been widely used in the analysis of the network properties of metabolism [15], prediction and analysis of organism growth phenotypes [16], model-based interpretation of experimental data [17], and metabolic engineering [18]. Oleaginous organisms such as M. alpina can accumulate large quantities of lipids, but maximizing lipid production is complicated by the complexity of the regulatory mechanisms associated with lipid metabolism. It is generally difficult to identify key metabolic modules contributing to lipid physiology. Using reconstruction GSMM, we can systematically analyze the function of each gene and metabolic reaction and model the effects using flux balance analysis (FBA). Specific pathways can be understood based on the model of the whole metabolic network, and strain design strategies can also be used to guide metabolic engineering experiments. Two GSMM studies on Yarrowia lipolytica (iNL895 [19] and iYL619_PCP [20]) have been published along with recent modeled networks of Mucor circinelloides and M. alpina [21]. GSMM studies therefore provide a new approach to investigating the complex lipid metabolism in M. alpina. Vongsangnak et al. (2013) [21] previously published a M. alpina network model, however, this was a refined network that could only be used to investigate genome annotation and metabolic routes, and not flux distribution or phenotypic behaviors [21]. To systematically study flux distribution and the mechanism of lipid accumulation, we reconstructed a new M. alpina GSMM and used the COBRA Toolbox [22] for subsequent research.