No abstract

Accurate and controllable regulatory elements such as promoters and ribosome binding sites (RBSs) are indispensable tools to quantitatively regulate gene expression for rational pathway engineering. Therefore, de novo designing regulatory elements is brought back to the forefront of synthetic biology research. Here we developed a quantitative design method for regulatory elements based on strength prediction using artificial neural network (ANN). One hundred mutated Trc promoter & RBS sequences, which were finely characterized with a strength distribution from 0 to 3.559 (relative to the strength of the original sequence which was defined as 1), were used for model training and test. A precise strength prediction model, NET90_19_576, was finally constructed with high regression correlation coefficients of 0.98 for both model training and test. Sixteen artificial elements were in silico designed using this model. All of them were proved to have good consistency between the measured strength and our desired strength. The functional reliability of the designed elements was validated in two different genetic contexts. The designed parts were successfully utilized to improve the expression of BmK1 peptide toxin and fine-tune deoxy-xylulose phosphate pathway in Escherichia coli. Our results demonstrate that the methodology based on ANN model can de novo and quantitatively design regulatory elements with desired strengths, which are of great importance for synthetic biology applications.

Many terpenoids have important pharmacological activity and commercial value; however, application of these terpenoids is often limited by problems associated with the production of sufficient amounts of these molecules. The use of Saccharomyces cerevisiae (S. cerevisiae) for the production of heterologous terpenoids has achieved some success. The objective of this study was to identify S. cerevisiae knockout targets for improving the synthesis of heterologous terpeniods. On the basis of computational analysis of the S. cerevisiae metabolic network, we identified the knockout sites with the potential to promote terpenoid production and the corresponding single mutant was constructed by molecular manipulations. The growth rates of these strains were measured and the results indicated that the gene deletion had no adverse effects. Using the expression of amorphadiene biosynthesis as a testing model, the gene deletion was assessed for its effect on the production of exogenous terpenoids. The results showed that the dysfunction of most genes led to increased production of amorphadiene. The yield of amorphadiene produced by most single mutants was 8–10-fold greater compared to the wild type, indicating that the knockout sites can be engineered to promote the synthesis of exogenous terpenoids.