Mortierella alpina is an oleaginous fungus used in the industrial scale production of arachidonic acid (ARA). In order to investigate the metabolic characteristics at a systems level and to explore potential strategies for enhanced lipid production, a genome-scale metabolic model of M. alpina was reconstructed. This model included 1106 genes, 1854 reactions and 1732 metabolites. On minimal growth medium, 86 genes were identified as essential, whereas 49 essential genes were identified on yeast extract medium. A series of sequential desaturase and elongase catalysed steps are involved in the synthesis of polyunsaturated fatty acids (PUFAs) from acetyl-CoA precursors, with concomitant NADPH consumption, and these steps were investigated in this study. Oxygen is known to affect the degree of unsaturation of PUFAs, and robustness analysis determined that an oxygen uptake rate of 2.0 mmol gDW−1 h−1 was optimal for ARA accumulation. The flux of 53 reactions involving NADPH was significantly altered at different ARA levels. Of these, malic enzyme (ME) was confirmed as a key component in ARA production and NADPH generation. When using minimization of metabolic adjustment, a knock-out of ME led to a 38.28% decrease in ARA production. The simulation results confirmed the model as a useful tool for future research on the metabolism of PUFAs.

Fumaric acid (FA) is a promising biomass-derived building-block chemical. Bio-based FA production from renewable feedstock is a promising and sustainable alternative to petroleum-based chemical synthesis. Here we report on FA production by direct fermentation using metabolically engineered Saccharomyces cerevisiae with the aid of in silico analysis of a genome-scale metabolic model. First, FUM1 was selected as the target gene on the basis of extensive literature mining. Flux balance analysis (FBA) revealed that FUM1 deletion can lead to FA production and slightly lower growth of S. cerevisiae. The engineered S. cerevisiae strain obtained by deleting FUM1 can produce FA up to a concentration of 610±31 mg L^–1 without any apparent change in growth in fed-batch culture. FT-IR and ¹H and ¹³C NMR spectra confirmed that FA was synthesized by the engineered S. cerevisiae strain. FBA identified pyruvate carboxylase as one of the factors limiting higher FA production. When the RoPYC gene was introduced, S. cerevisiae produced 1134±48 mg L^–1 FA. Furthermore, the final engineered S. cerevisiae strain was able to produce 1675±52 mg L^–1 FA in batch culture when the SFC1 gene encoding a succinate–fumarate transporter was introduced. These results demonstrate that the model shows great predictive capability for metabolic engineering. Moreover, FA production in S. cerevisiae can be efficiently developed with the aid of in silico metabolic engineering.

Aspergillus terreus is widely used for industrial production of important chemical compounds such as itaconate and lovastatin. To improve understanding of its physiology, we reconstructed a genome-scale metabolic model, iJL1454, consisting of 1454 genes, 1451 reactions and 1155 metabolites, based on genome annotation and literature mining. The model accurately predicted the growth phenotype of A. terreus on different carbon and nitrogen sources, and after deletion of essential genes. In iJL1454, 69 and 20 (a subset of the 69) genes were identified as growth essential in minimal and rich mediums, respectively. Ten of the genes had previously been suggested as essential by experimental observations. The biosynthetic pathway of itaconate from glucose, in particular the coding genes and the transporters, was elucidated. The pathway included 29 reactions and 55 genes. To guide metabolic engineering, we investigated bottleneck reactions in itaconate production and the effect of perturbations of nutrient and environmental conditions. The results illustrate the value of model iJL1454 as a tool to understand and optimize A. terreus metabolism.