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Lab automation has facilitated synthetic biology applications in an automated workflow, and biofoundry facilities have enabled automated high-throughput experiments of gene cloning and genome engineering to be conducted following a precise experimental design and protocol. However, before-experiment procedures in biofoundry applications have been underdetermined. We aimed to develop a Python-based planning-assistant software, namely Biofoundry Palette, for liquid handler-based experimentation and operation in the biofoundry workflow. Depending on the synthetic biology project, variable information and content information may vary; the Biofoundry Palette provides precise information for the before-experiment units for each process module in the biofoundry workflow. As a demonstration, more than 200 unique information sets, generated by Biofoundry Palette, were used in automated gene cloning or pathway construction. The information on planning and management can potentially help the operator faithfully execute the biofoundry workflow after securing the before-experiment unit, thereby lowering the risk of human errors and performing successful biofoundry operations for synthetic biology applications.

Controlling translational elongation is essential for efficient protein synthesis. Ribosome profiling has revealed that the speed of ribosome movement is correlated with translational efficiency in the translational elongation ramp. In this work, we present a new deep learning model, called DeepTESR, to predict the degree of translational elongation short ramp (TESR) from mRNA sequence. The proposed deep learning model exhibited superior performance in predicting the TESR scores for 226 981 TESR sequences, resulting in the mean absolute error (MAE) of 0.285 and a coefficient of determination R 2 of 0.627, superior to the conventional machine learning models (e.g., MAE of 0.335 and R 2 of 0.571 for LightGBM). We experimentally validated that heterologous fluorescence expression of proteins with randomly selected TESR was moderately correlated with the predictions. Furthermore, a genome-wide analysis of TESR prediction in the 4305 coding sequences of Escherichia coli showed conserved TESRs over the clusters of orthologous groups. In this sense, DeepTESR can be used to predict the degree of TESR for gene expression control and to decipher the mechanism of translational control with ribosome profiling. DeepTESR is available at https://github.com/fmblab/DeepTESR.

Efficient and versatile DNA assembly frameworks have had an impact on promoting synthetic biology to build complex biological systems. To accelerate system development, laboratory automation (or biofoundry) provides an opportunity to construct organisms and DNA assemblies via computer-aided design. However, a modular cloning (MoClo) system for multiple DNA assemblies limits the biofoundry workflow in terms of simplicity and feasibility by preparing the number of cloning materials such as destination vectors prior to the automation process. Herein, we propose robot-assisted MoClo (RoboMoClo) to accelerate a synthetic biology project with multiple gene expressions at the biofoundry. The architecture of the RoboMoClo framework provides a hybrid strategy of hierarchical gene assembly and iterative gene assembly, and fewer destination vectors compared with other MoClo systems. An industrial bacterium, Corynebacterium glutamicum , was used as a model host for RoboMoClo. After building a biopart library (promoter and terminator; level 0) and evaluating its features (level 1), various transcriptional directions in multiple gene assemblies (level 2) were studied using the RoboMoClo vectors. Among the constructs, the convergent construct exhibited potential transcriptional interference through the collision of RNA polymerases. To study design of experiment-guided lycopene biosynthesis in C. glutamicum (levels 1, 2, and 3), the biofoundry-assisted multiple gene assembly was demonstrated as a proof-of-concept by constructing various sub-pathway units (level 2) and pathway units (level 3) for C. glutamicum . The RoboMoClo framework provides an improved MoClo toolkit for laboratory automation in a synthetic biology application.

To enable circadian control of gene expression in cyanobacteria, we constructed a genetic logic gate (NAND) using orthogonal promoters via modular CRISPR interference. The NAND gates were tested in Synechococcus elongatus PCC 7942 using a fluorescent reporter. The NAND gate dynamics were characterized based on the affinity of the dCas9 complex to the output element. Upon connecting tight gene repressions with the circadian promoter (the purF gene; peak expression at dawn), inversed peak expressions were obtained as an output of the NAND gate although the retroactivities were shown in the ON and OFF states. A dark-responsive genetic element of the NAND gate was also expanded to an AND gate in S. elongatus PCC 7942. These cyanobacterial NAND and AND gates could facilitate the control of gene expressions in dynamic metabolic engineering technologies, thereby enabling the cyanobacteria to serve as biosolar cell factories.

To supply the sustainable calorie-free sweetener stevioside, synthetic photosynthetic bacteria were developed to produce ent -kaurenoic acid as a precursor of stevioside directly from CO 2 . By the use of a combinatorial and modular approach for gene expression, including a cytochrome P450 and the corresponding reductase, engineered Synechoccous elongatus PCC 7942 as a model cyanobacterium enabled the biosynthesis of ent -kaurenoic acid at 2.9 ± 0.01 mg L -1 from CO 2 . We found that the order of genes for expression was critical, producing ent -kaurenoic acid by balancing gene expressions and accumulation of the toxic intermediate in a cell. The engineered bacteria allowed the complete biosynthesis of ent -kaurenoic acid, and it will be used for stevioside biosynthesis from CO 2 as a controlled fermentation.

In cyanobacteria, metabolic engineering using synthetic biology tools is limited to build a biosolar cell factory that converts CO 2 to value-added chemicals, as repression of essential genes has not been achieved. In this study, we developed a dCas12a-mediated CRISPR interference system (CRISPRi-dCas12a) in cyanobacteria that effectively blocked the transcriptional initiation by means of a CRISPR-RNA (crRNA) and 19-nt direct repeat, resulting in 53-94% gene repression. The repression of multiple genes in a single crRNA array was also successfully achieved without a loss in repression strength. In addition, as a demonstration of the dCas12a-mediated CRISPRi for metabolic engineering, photosynthetic squalene production was improved by repressing the essential genes of either acnB encoding for aconitase or cpcB 2 encoding for phycocyanin β-subunit in Synechococcus elongatus PCC 7942. The ability to regulate gene repression will promote the construction of biosolar cell factories to produce value-added chemicals.

The push-and-pull strategy for metabolic engineering was successfully demonstrated in Synechococcus elongatus PCC 7942, a model photosynthetic bacterium, to produce squalene from CO 2 . Squalene synthase (SQS) was fused to either a key enzyme (farnesyl diphosphate synthase) of the methylerythritol phosphate pathway or the β-subunit of phycocyanin (CpcB1). Engineered cyanobacteria with expression of a fusion CpcB1-SQS protein showed a squalene production level (7.16 ± 0.05 mg/L/OD 730 ) that was increased by 1.8-fold compared to that of the control strain expressing SQS alone. To increase squalene production further, the gene dosage for CpcB1·SQS protein expression was increased and the fusion protein was expressed under a strong promoter, yielding 11.98 ± 0.49 mg/L/OD 730 of squalene, representing a 3.1-fold increase compared to the control. Subsequently, the best squalene producer was cultivated in a scalable photobioreactor (6 L) with light optimization, which produced 7.08 ± 0.5 mg/L/OD 730 squalene (equivalent to 79.2 mg per g dry cell weight). Further optimization for photobioprocessing and strain development will promote the construction of a solar-to-chemical platform.