Limonene, a monocyclic monoterpene, is known for its using as an important precursor of many flavoring, pharmaceutical, and biodiesel products. Currently, d-limonene has been produced via fractionation from essential oils or as a byproduct of orange juice production, however, considering the increasing need for limonene and a certain amount of pesticides may exist in the limonene obtained from the citrus industry, some other methods should be explored to produce limonene. To construct the limonene synthetic pathway in Yarrowia lipolytica, two genes encoding neryl diphosphate synthase 1 (NDPS1) and limonene synthase (LS) were codon-optimized and heterologously expressed in Y. lipolytica. Furthermore, to maximize limonene production, several genes involved in the MVA pathway were overexpressed, either in different copies of the same gene or in combination. Finally with the optimized pyruvic acid and dodecane concentration in flask culture, a maximum limonene titer and content of 23.56 mg/L and 1.36 mg/g DCW were achieved in the final engineered strain Po1f-LN-051, showing approximately 226-fold increase compared with the initial yield 0.006 mg/g DCW. This is the first report on limonene biosynthesis in oleaginous yeast Y. lipolytica by heterologous expression of codon-optimized tLS and tNDPS1 genes. To our knowledge, the limonene production 23.56 mg/L, is the highest limonene production level reported in yeast. In short, we demonstrate that Y. lipolytica provides a compelling platform for the overproduction of limonene derivatives, and even other monoterpenes.

Genome-scale metabolic models (GEMs) have become a popular tool for systems biology, and they have been used in many fields such as industrial biotechnology and systems medicine. Since more and more studies are being conducted using GEMs, they have recently received considerable attention. However, the reliability of GEM-based predictions and analyses has remained questionable. In this review, we introduce the basic concept of GEMs and provide an overview of their applications in biotechnology, systems medicine, and some other fields. In addition, we describe the general principle of the applications and analyses built on GEMs. The purpose of this review is to introduce the application of GEMs in biological analysis and to promote its wider use by biologists.