None of the natural fumarate-producing microorganisms seem to be suitable for large-scale commercial production although high FA yields have been obtained 
. S. cerevisiae
is an excellent platform for biologically based chemicals such as organic acids. The aim of the present study was to construct a genetically engineered S. cerevisiae
strain that can produce FA. First, the target gene FUM1
was identified in an extensive literature search and then FBA was used to predict the effect of FUM1
disruption using the S. cerevisiae i
ND750 GSMM. The simulated results revealed that FUM1
deletion could lead to FA accumulation with only a slight influence on cell growth (~1.95% lower). Then gene deletion was carried out and engineered S. cerevisiae
cells produced FA at concentrations up to 610±31 mg L–1
. Meanwhile, cell growth and glucose consumption were slightly lower compared to the parent strain, in accordance with the simulated result. Simulated results also showed that pyruvate carboxylase could be one of the factors limiting higher FA production, and an improved FA yield was obtained when the RoPYC
gene was introduced. Furthermore, a significant improvement in FA production was achieved when the SFC1
gene was introduced. The final FA concentration obtained was 1675±52 mg L–1
. Thus, the engineered strain provides a potential new route for FA production. However, the concentration and yield are low in comparison with R. oryzae
, so further work is required before this approach is economically feasible.
The number of GSMMs available is increasing sharply 
. Because a GSMM represents nearly all the metabolic activities of an organism, it can be of great help in understanding metabolism on a global level 
. Thus, GSMMs are widely used in metabolic engineering 
and can be used to predict and evaluate genetic manipulations in advance (dry experiments) when combined with certain algorithms 
. This can greatly improve the efficiency and directionality of metabolic engineering in various phases by predicting gene targets to be manipulated throughout the whole cellular network. In S. cerevisiae
, metabolic engineering strategies aided by GSMM have led to improved production of various metabolites such as bioethanol, purine, proline/pyrimidines and vanillin 
. In addition to direct improvements in production capacity, GSMMs can also be used to predict cellular properties or phenotypic traits such as growth and glucose consumption. In previous studies, growth behavior, and ethanol, succinate, citrate and fumarate concentrations were determined in various media (rich and minimal media) under aerobic and anaerobic conditions 
, but the effect of FUM1
deletion or fumarase deficiency on fermentation profiles (growth and glucose uptake rate) has not been studied in detail. In the present study, the phenotypic trait of slightly lower growth caused by FUM1
deletion in S. cerevisiae
was successfully predicted by FBA analysis; this trait is important for metabolic engineering because unwanted side effects can be induced. Metabolic models are also useful in identifying targets for further strain improvement. We identified pyruvate carboxylase as a factor restricting higher FA yield. A higher FA yield was obtained by increasing the flux through pyruvate carboxylase. The increased flux induced by overexpression of pyruvate carboxylase is linked to increased transport of cytosolic oxaloacetate into mitochondria and supply to oxidative reactions 
. When pyruvate carboxylase and the transporter encoded by SFC1
were coexpressed, a higher growth rate and FA yield were obtained, which suggests that insufficient FA export is another controlling step that would lead to higher FA production in steady-state metabolism.
The model showed some restrictions; however, the physiological characteristics observed for engineered organisms can be used to update the model. In the present study, there was very good accordance between in silico predictions and experimental results. The discrepancy between experimental and predictive yields was primarily caused by lack of model knowledge for yeast metabolism, regulatory mechanisms and feedback inhibition, which requires specific further experimental investigation.
In conclusion, the metabolic pathways in S. cerevisiae were rationally engineered for FA production with the aid of in silico simulations. The strategy described here can be useful for improved production of organic acids and other metabolites by direct microbial fermentation from renewable resources.