Hailin Meng

Quantitative Design of Regulatory Elements Based on High-Precision Strength Prediction Using Artificial Neural Network

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.

Identification of Novel Knockout Targets for Improving Terpenoids Biosynthesis in Saccharomyces cerevisiae

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.

Construction of polyketide overproducing Escherichia coli strains via synthetic antisense RNAs based on in silico fluxome analysis and comparative transcriptome analysis.

Rapid assessment and optimization of the incompatible metabolic modules remain a challenge. Here, we developed a systematic approach to characterize the module interactions and improve the problematic modules during the 6-deoxyerythronolide B (6dEB) biosynthesis in E. coli. Tremendous differences in the overall trends of flux changes of various metabolic modules were firstly uncovered based on in silico fluxome analysis and comparative transcriptome analysis. Potential targets for improving 6dEB biosynthesis were identified through analyzing these discrepancies. All 25 predicted targets at modules of PP pathway and nucleotide metabolism were firstly tested for improving the 6dEB production in E. coli via synthetic antisense RNAs. Down-regulation of 18 targets genes leads to more than 20% increase in 6dEB yield. Combinatorial repression of targets with greater than 60% increase in 6dEB titer, e.g., anti-guaB/anti-zwf led to a 296.2% increase in 6dEB production (210.4 mg/L in flask) compared to the control (53.1 mg/L). This is the highest yield yet reported for polyketide heterologous biosynthesis in E. coli. This study demonstrates a strategy to enhance the yield of heterologous products in the chassis cell and indicates the effectiveness of antisense RNA for use in metabolic engineering.

Cis-acting regulatory elements: from random screening to quantitative design

The cis-acting regulatory elements, e.g., promoters and ribosome binding sites (RBSs) with various desired properties, are building blocks widely used in synthetic biology for fine tuning gene expression. In the last decade, acquisition of a controllable regulatory element from a random library has been established and applied to control the protein expression and metabolic flux in different chassis cells. However, more rational strategies are still urgently needed to improve the efficiency and reduce the laborious screening and multifaceted characterizations. Building precise computational models that can predict the activity of regulatory elements and quantitatively design elements with desired strength have been demonstrated tremendous potentiality. Here, recent progress on construction of cisacting regulatory element library and the quantitative predicting models for design of such elements are reviewed and discussed in detail.

Synthetic Biology Triggers New Era of Antibiotics Development

As a discipline to design and construct organisms with desired properties, synthetic biology has generated rapid progresses in the last decade. Combined synthetic biology with the traditional process, a new universal workflow for drug development has been becoming more and more attractive. The new methodology exhibits more efficient and inexpensive comparing to traditional methods in every aspect, such as new compounds discovery & screening, process design & drug manufacturing. This article reviews the application of synthetic biology in antibiotics development, including new drug discovery and screening, combinatorial biosynthesis to generate more analogues and heterologous expression of biosynthetic gene clusters with systematic engineering the recombinant microbial systems for large scale production.

Construction of precise support vector machine based models for predicting promoter strength

BackgroundThe prediction of the prokaryotic promoter strength based on its sequence is of great importance not only in the fundamental research of life sciences but also in the applied aspect of synthetic biology. Much advance has been made to build quantitative models for strength prediction, especially the introduction of machine learning methods such as artificial neural network (ANN) has significantly improve the prediction accuracy. As one of the most important machine learning methods, support vector machine (SVM) is more powerful to learn knowledge from small sample dataset and thus supposed to work in this problem.MethodsTo confirm this, we constructed SVM based models to quantitatively predict the promoter strength. A library of 100 promoter sequences and strength values was randomly divided into two datasets, including a training set (⩾10 sequences) for model training and a test set (⩾10 sequences) for model test.ResultsThe results indicate that the prediction performance increases with an increase of the size of training set, and the best performance was achieved at the size of 90 sequences. After optimization of the model parameters, a high-performance model was finally trained, with a high squared correlation coefficient for fitting the training set (R2 > 0.99) and the test set (R2 > 0.98), both of which are better than that of ANN obtained by our previous work.ConclusionsOur results demonstrate the SVM-based models can be employed for the quantitative prediction of promoter strength.

Coupling metagenomics with cultivation to select host-specific probiotic micro-organisms for subtropical aquaculture

To demonstrate a nonempirical workflow to select host‐specific probiotics for aquaculture industry.

Synbiological systems for complex natural products biosynthesis

Natural products (NPs) continue to play a pivotal role in drug discovery programs. The rapid development of synthetic biology has conferred the strategies of NPs production. Synthetic biology is a new engineering discipline that aims to produce desirable products by rationally programming the biological parts and manipulating the pathways. However, there is still a challenge for integrating a heterologous pathway in chassis cells for overproduction purpose due to the limited characterized parts, modules incompatibility, and cell tolerance towards product. Enormous endeavors have been taken for mentioned issues. Herein, in this review, the progresses in naturally discovering novel biological parts and rational design of synthetic biological parts are reviewed, combining with the advanced assembly technologies, pathway engineering, and pathway optimization in global network guidance. The future perspectives are also presented.

Research Advances in Synthetic Biology of Terpenoids

Synthetic biology research has immensely boosted the development and industrial application of microbial terpenoid biosynthesis. Owing to the complexity of plant terpenoid metabolism, a number of terpenoid biosynthetic pathways have remained unknown or been incompletely elucidated. Thus, for most terpenoids, only their simple intermediates can be de novo biosynthesized through heterologous biosynthesis in microbes. Furthermore, the yield of these terpenoid intermediates remains very low, ranging from hundreds of micrograms to several milligrams. To discuss these two issues, this review elucidates the recent advances and new strategies in terpenoid synthetic biology based on three aspects: terpenoid pathway elucidation and artificial assembly, design and construction of microbial chassis with efficient precursor supply, and systematic adaption of chassis metabolism and downstream pathways.