Currently, predictive translation tuning of regulatory elements to the desired output of transcription factor (TF)-based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e. fold change in gene expression between the presence and absence of inducer) by adjusting the translation level of the TF and reporter. However, existing TF-based biosensors generally suffer from unpredictable dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation level, protein folding and dynamic range, and presented a design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). In doing so, a library containing 7053 designed cRBSs was divided into five sub-libraries through fluorescence-activated cell sorting to establish a classification model based on convolutional neural network in deep learning. Finally, the present work exhibited a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.

Metabolic engineering consistently demands to produce the maximum carbon and energy flux to target chemicals. To balance metabolic flux, gene expression levels of artificially synthesized pathways usually fine-tuned using multimodular optimization strategy. However, forward construction is an engineering conundrum because a vast number of possible pathway combinations need to be constructed and analyzed.Here, an iterative high-throughput balancing (IHTB) strategy was established to thoroughly fine-tune the (2S)-naringenin biosynthetic pathway. A series of gradient constitutive promoters from Escherichia coli were randomly cloned upstream of pathway genes, and the resulting library was screened using an ultraviolet spectrophotometry-fluorescence spectrophotometry high-throughput method, which was established based on the interactions between AlCl 3 and (2S)-naringenin. The metabolic flux of the screened high-titer strains was analyzed and iterative rounds of screening were performed based on the analysis results. After several rounds, the metabolic flux of the (2S)-naringenin synthetic pathway was balanced, reaching a final titer of 191.9 mg/L with 29.2 mg/L p-coumaric acid accumulation. Chalcone synthase was speculated to be the rate-limiting enzyme because its expression level was closely related to the production of both (2S)-naringenin and p-coumaric acid. The established IHTB strategy can be used to efficiently balance multigene pathways, which will accelerate the development of efficient recombinant strains. K E Y W O R D S flavonoids, metabolic engineering, modular optimization, promoter Biotechnology and Bioengineering. 2019;116:1392-1404. wileyonlinelibrary.com/journal/bit 1392 |

A promoter is one of the most important and basic tools used to achieve diverse synthetic biology goals. Escherichia coli is one of the most commonly used model organisms in synthetic biology to produce useful target products and establish complicated regulation networks. During the fine-tuning of metabolic or regulation networks, the limited number of well-characterized inducible promoters has made implementing complicated strategies difficult. In this study, 104 native promoter-5'-UTR complexes (PUTR) from E. coli were screened and characterized based on a series of RNA-seq data. The strength of the 104 PUTRs varied from 0.007% to 4630% of that of the P promoter in the transcriptional level and from 0.1% to 137% in the translational level. To further upregulate gene expression, a series of combinatorial PUTRs and cascade PUTRs were constructed by integrating strong transcriptional promoters with strong translational 5'-UTRs. Finally, two combinatorial PUTRs (P-UTR and P-UTR) and two cascade PUTRs (PUTR-PUTR and PUTR-PUTR) were identified as having the highest activity, with expression outputs of 170%, 137%, 409%, and 203% of that of the P promoter, respectively. These engineered PUTRs are stable for the expression of different genes, such as the red fluorescence protein gene and the β-galactosidase gene. These results show that the PUTRs characterized and constructed in this study may be useful as a plug-and-play synthetic biology toolbox to achieve complicated metabolic engineering goals in fine-tuning metabolic networks to produce target products.

Transcription-factor-based biosensors (TFBs) are often used for metabolite detection, adaptive evolution, and metabolic flux control. However, designing TFBs with superior performance for applications in synthetic biology remains challenging. Specifically, natural TFBs often do not meet real-time detection requirements owing to their slow response times and inappropriate dynamic ranges, detection ranges, sensitivity, and selectivity. Furthermore, designing and optimizing complex dynamic regulation networks is time-consuming and labor-intensive. This Review highlights TFB-based applications and recent engineering strategies ranging from traditional trial-and-error approaches to novel computer-model-based rational design approaches. The limitations of the applications and these engineering strategies are additionally reviewed.

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