A metabolic flux model of an organism can be developed from the genome-scale metabolic network (GEM) for quantitatively understanding and simulating the phenotypes of metabolic systems. For the development of the flux model, the GEM serves as a framework that integrate all of the massive "omics" data derived from systems biology research, comprising (i) gene detection (genomics), (ii) gene expression (transcriptomics), (iii) protein expression and modifications (proteomics), (iv) primary and secondary metabolites production (metabolomics), (v) measurement and estimation reaction rates (fluxes) for a network of reactions that occur in an organism (fluxomics) [1], and (vi) large-scale literature mining (bibliomics) [1][2][3].Due to the advances in the high-throughput "omics" technologies and computer capabilities, there has been an exponential increase in GEMs reconstructed for a wide variety of organisms since the first GEM was built in 1999 [4]. As the GEMs intend to include as large part of the cell metabolism as possible and as much biological information as possible [5], they provide a detailed representation of biological reaction networks and their functional states [6], and can be used as analysis platforms for computational systems approaches such as constraint-based modeling [3], to characterize the flux profiles of the microbial phenotypes. This type of analysis can generate new knowledge that facilitates metabolic engineering of interesting biotechnological processes at the whole-cell level and can overcome the difficulties experienced in reductionist investigative strategies. Therefore, using in silico modeling approaches to develop strains has been considered as a promising area in the field of systems biology.