Precise Prediction of Promoter Strength Based on a De Novo Synthetic Promoter Library Coupled with Machine Learning

From natural to synthetic: Promoter engineering in yeast expression systems

Synthetic promoters are particularly relevant for application not only in yeast expression systems designed for high-level heterologous protein production but also in other applications such as metabolic engineering, cell biological research, and stage-specific gene expression control. By designing synthetic promoters, researcher can create customized expression systems tailored to specific needs, whether it is maximizing protein production or precisely controlling gene expression at different stages of a process. While recognizing the limitations of endogenous promoters, they also provide important information needed to design synthetic promoters. In this review, emphasis will be placed on some key approaches to identify endogenous, and to generate synthetic promoters in yeast expression systems. It shows the connection between endogenous and synthetic promoters, highlighting how their interplay contributes to promoter development. Furthermore, this review illustrates recent developments in biotechnological advancements and discusses how this field will evolve in order to develop custom-made promoters for diverse applications. This review offers detailed information, explores the transition from endogenous to synthetic promoters, and presents valuable perspectives on the next generation of promoter design strategies.

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution

Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine learning or deep learning methods have shown some success in this task, but their outcomes were not satisfactory enough, primarily due to the neglect of evolutionary information. In this paper, we introduce the Chaos-Attention net for Promoter Evolution (CAPE) to address the limitations of existing methods. We comprehensively extract evolutionary information within promoters using merged chaos game representation and process the overall information with modified DenseNet and Transformer structures. Our model achieves state-of-the-art results on two kinds of distinct tasks related to prokaryotic promoter strength prediction. The incorporation of evolutionary information enhances the model's accuracy, with transfer learning further extending its adaptability. Furthermore, experimental results confirm CAPE's efficacy in simulating in silico directed evolution of promoters, marking a significant advancement in predictive modeling for prokaryotic promoter strength. Our paper also presents a user-friendly website for the practical implementation of in silico directed evolution on promoters. The source code implemented in this study and the instructions on accessing the website can be found in our GitHub repository https://github.com/BobYHY/CAPE.

Engineering microbial metabolic homeostasis for chemicals production

Microbial-based bio-refining promotes the development of a biotechnology revolution to encounter and tackle the enormous challenges in petroleum-based chemical production by biomanufacturing, biocomputing, and biosensing. Nevertheless, microbial metabolic homeostasis is often incompatible with the efficient synthesis of bioproducts mainly due to: inefficient metabolic flow, robust central metabolism, sophisticated metabolic network, and inevitable environmental perturbation. Therefore, this review systematically summarizes how to optimize microbial metabolic homeostasis by strengthening metabolic flux for improving biotransformation turnover, redirecting metabolic direction for rewiring bypass pathway, and reprogramming metabolic network for boosting substrate utilization. Future directions are also proposed for providing constructive guidance on the development of industrial biotechnology.

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

Diffusion-Based Generative Network for de Novo Synthetic Promoter Design

Computer-aided promoter design is a major development trend in synthetic promoter engineering. Various deep learning models have been used to evaluate or screen synthetic promoters, but there have been few works on de novo promoter design. To explore the potential ability of generative models in promoter design, we established a diffusion-based generative model for promoter design in Escherichia coli. The model was completely driven by sequence data and could study the essential characteristics of natural promoters, thus generating synthetic promoters similar to natural promoters in structure and component. We also improved the calculation method of FID indicator, using a convolution layer to extract the feature matrix of the promoter sequence instead. As a result, we got an FID equal to 1.37, which meant synthetic promoters have a distribution similar to that of natural ones. Our work provides a fresh approach to de novo promoter design, indicating that a completely data-driven generative model is feasible for promoter design.

Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis

As a sustainable and renewable alternative to petroleum fuels, advanced biofuels shoulder the responsibility of energy saving, emission reduction and environmental protection. Traditional engineering of cell factories for production of advanced biofuels lacks efficient high-throughput screening tools and regulating systems, impeding the improvement of cellular productivity and yield. Transcription factor-based biosensors have been widely applied to monitor and regulate microbial cell factory products due to the advantages of fast detection and in-situ screening. This review updates the design and application of transcription factor-based biosensors tailored for advanced biofuels and related intermediates. The construction and genetic parts selection principle of biosensors are discussed. Strategies to enhance the performance of biosensor, including regulating promoter strength and RBS strength, optimizing plasmid copy number, implementing genetic amplifier, and modulating the structure of transcription factor, have also been summarized. We further review the application of biosensors in high-throughput screening of new metabolic engineering targets, evolution engineering, confirmation of protein function, and dynamic regulation of metabolic flux for higher production of advanced biofuels. At last, we discuss the current limitations and future trends of transcription factor-based biosensors.

Promoter engineering of natural product biosynthetic gene clusters in actinomycetes: concepts and applications

Covering 2011 to 2022Low titers of natural products in laboratory culture or fermentation conditions have been one of the challenging issues in natural products research. Many natural product biosynthetic gene clusters (BGCs) are also transcriptionally silent in laboratory culture conditions, making it challenging to characterize the structures and activities of their metabolites. Promoter engineering offers a potential solution to this problem by providing tools for transcriptional activation or optimization of biosynthetic genes. In this review, we summarize the 10 years of progress in promoter engineering approaches in natural products research focusing on the most metabolically talented group of bacteria actinomycetes.

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

Standardized Iterative Genome Editing Method for Escherichia coli Based on CRISPR-Cas9

The introduction of complex biosynthetic pathways into the hosts' chromosomes is gaining attention with the development of synthetic biology. While CRISPR-Cas9 has been widely employed for gene knock-in, the process of multigene insertion remains cumbersome due to laborious and empirical gene cloning procedures. To address this, we devised a standardized iterative genome editing system for Escherichia coli, harnessing the power of CRISPR-Cas9 and MetClo assembly. This comprehensive toolkit comprises two fundamental elements based on the Golden Gate standard for modular assembly of sgRNA or CRISPR arrays and donor DNAs. We achieved a gene insertion efficiency of up to 100%, targeting a single locus. Expression of tracrRNA using a strong promoter enhances multiplex genomic insertion efficiency to 7.3%, compared with 0.76% when a native promoter is used. To demonstrate the robust capabilities of this genome editing toolbox, we successfully integrated 5-10 genes from the coenzyme B12 biosynthetic pathway ranging from 5.3 to 8 Kb in length into the chromosome of E. coli chassis cells, resulting in 14 antibiotic-free, plasmid-free producers. Following an extensive screening process involving genes from diverse sources, cistronic design modifications, and chromosome repositioning, we obtained a recombinant strain yielding 1.49 mg L-1 coenzyme B12, the highest known titer achieved by using E. coli as the producer. Illuminating its user-friendliness, this genome editing system is an exceedingly versatile tool for expediently integrating complex biosynthetic pathway genes into hosts' genomes, thus facilitating pathway optimization for chemical production.

The principle of uncertainty in biology: Will machine learning/artificial intelligence lead to the end of mechanistic studies?

Molecular Biology has long tried to discover mechanisms, considering that unless we understand the principles, we cannot develop applications. Now machine learning and artificial intelligence enable direct leaps to application without understanding the principles. Will this herald a decline in mechanistic studies?

AI and Knowledge-Based Method for Rational Design of Escherichia coli Sigma70 Promoters

Expanding sigma70 promoter libraries can support the engineering of metabolic pathways and enhance recombinant protein expression. Herein, we developed an artificial intelligence (AI) and knowledge-based method for the rational design of sigma70 promoters. Strong sigma70 promoters were identified by using high-throughput screening (HTS) with enhanced green fluorescent protein (eGFP) as a reporter gene. The features of these strong promoters were adopted to guide promoter design based on our previous reported deep learning model. In the following case study, the obtained strong promoters were used to express collagen and microbial transglutaminase (mTG), resulting in increased expression levels by 81.4% and 33.4%, respectively. Moreover, these constitutive promoters achieved soluble expression of mTG-activating protease and contributed to active mTG expression in Escherichia coli. The results suggested that the combined method may be effective for promoter engineering.

Fine-Tuning Gene Expression in Bacteria by Synthetic Promoters

Promoters are key genetic elements in the initiation and regulation of gene expression. A limited number of natural promoters has been described for the control of gene expression in synthetic biology applications. Therefore, synthetic promoters have been developed to fine-tune the transcription for the desired amount of gene product. Mostly, synthetic promoters are characterized using promoter libraries that are constructed via mutagenesis of promoter sequences. The strength of promoters in the library is determined according to the expression of a reporter gene such as gfp encoding green fluorescent protein. Gene expression can be controlled using inducers. The majority of the studies on gram-negative bacteria are conducted using the expression system of the model organism Escherichia coli while that of the model organism Bacillus subtilis is mostly used in the studies on gram-positive bacteria. Additionally, synthetic promoters for the cyanobacteria, which are phototrophic microorganisms, are evaluated, especially using the model cyanobacterium Synechocystis sp. PCC 6803. Moreover, a variety of algorithms based on machine learning methods were developed to characterize the features of promoter elements. Some of these in silico models were verified using in vitro or in vivo experiments. Identification of novel synthetic promoters with improved features compared to natural ones contributes much to the synthetic biology approaches in terms of fine-tuning gene expression.

Deep Learning-Assisted Design of Novel Promoters in Escherichia coli

Deep learning (DL) approaches have the ability to accurately recognize promoter regions and predict their strength. Here, the potential for controllably designing active Escherichia coli promoter is explored by combining multiple deep learning models. First, "DRSAdesign," which relies on a diffusion model to generate different types of novel promoters is created, followed by predicting whether they are real or fake and strength. Experimental validation showed that 45 out of 50 generated promoters are active with high diversity, but most promoters have relatively low activity. Next, "Ndesign," which relies on generating random sequences carrying functional -35 and -10 motifs of the sigma70 promoter is introduced, and their strength is predicted using the designed DL model. The DL model is trained and validated using 200 and 50 generated promoters, and displays Pearson correlation coefficients of 0.49 and 0.43, respectively. Taking advantage of the DL models developed in this work, possible 6-mers are predicted as key functional motifs of the sigma70 promoter, suggesting that promoter recognition and strength prediction mainly rely on the accommodation of functional motifs. This work provides DL tools to design promoters and assess their functions, paving the way for DL-assisted metabolic engineering.

Metabolic engineering combined with enzyme engineering for overproduction of ectoine in Escherichia coli

Ectoine, a natural protective agent, is naturally synthesized at low titers by some extreme environment microorganisms that are usually difficult to culture. There is a need for an efficient and eco-friendly ectoine production process. In this study, Escherichia coli BL21(DE3) with the ectABC gene cluster from Halomonas venusta achieved 1.7 g/L ectoine. After optimizing the expression plasmid, 2.1 g/L ectoine was achieved. Besides, the aspartate kinase mutant LysCT311I from Corynebacterium glutamicum and aspartate semialdehyde dehydrogenase from Halomonas elongata were overexpressed to increase precursors supply. Furthermore, the rate-limiting enzyme EctB was semirationally engineered, and the E407D mutation enhanced ectoine production by 13.8 %. To improve acetyl-CoA supply, the non-oxidative glycolysis pathway was introduced. Overall, the optimized strain ECT9-5 produced 67.1 g/L ectoine by fed-batch fermentation with a 0.3 g/g of glucose and the kinetic model resulted in a good fit.

Engineering of the hypoxia-induced Pichia stipitis ADH2 promoter to construct a promoter library for Pichia pastoris

Hypoxia-inducible promoters of a wide range of activities are desirable for fine-tuning gene expression in response to oxygen limitation, especially for the Crabtree negative yeast Pichia pastoris (Komagataella phaffii) with a high oxygen consumption rate in large-scale fermentations. Here we constructed a hypoxia-inducible promoter library for P. pastoris through error-prone PCR of Pichia stipitis ADH2 promoter (PsADH2). The library of 30 selected promoters showing 0.4- to 5.5-fold of the PsADH2 activity was obtained through high-throughput screening in microplates using the reporter yeast-enhanced green fluorescent protein. Two strong promoters, AM23 and AM30, were further characterized in shake flask cultures at high and low dissolved oxygen levels. They responded more sensitively to the low dissolved oxygen level, achieving a 4.6-, 7.9-fold and 3.6-, 7.7-fold higher fluorescence intensity and transcript level, respectively, than the wild-type PsADH2. Their hypoxia-inducible properties were confirmed with two additional reporters: β-galactosidase and Vitreoscilla hemoglobin, to demonstrate the broad applicability of the promoter library. During the typical fermentation process in shake flasks, the promoter AM30 showed strong expression with cell growth and decreased oxygen levels, without any additional chemical inducers or operations. Since the potent industrial host P. pastoris is recognized as an easy to scale-up system, it is reasonable to expect that the obtained hypoxia-inducible promoter library may have great potential to enable convenient regulation of gene expression under industrial fermentations which are usually run under oxygen limitation due to high cell density cultivations.

Optimizing Ethanol Production in Saccharomyces cerevisiae at Ambient and Elevated Temperatures through Machine Learning-Guided Combinatorial Promoter Modifications

Bioethanol has gained popularity in recent decades as an ecofriendly alternative to fossil fuels due to increasing concerns about global climate change. However, economically viable ethanol fermentation remains a challenge. High-temperature fermentation can reduce production costs, but Saccharomyces cerevisiae yeast strains normally ferment poorly under high temperatures. In this study, we present a machine learning (ML) approach to optimize bioethanol production in S. cerevisiae by fine-tuning the promoter activities of three endogenous genes. We created 216 combinatorial strains of S. cerevisiae by replacing native promoters with five promoters of varying strengths to regulate ethanol production. Promoter replacement resulted in a 63% improvement in ethanol production at 30 °C. We created an ML-guided workflow by utilizing XGBoost to train high-performance models based on promoter strengths and cellular metabolite concentrations obtained from ethanol production of 216 combinatorial strains at 30 °C. This strategy was then applied to optimize ethanol production at 40 °C, where we selected 31 strains for experimental fermentation. This reduced experimental load led to a 7.4% increase in ethanol production in the second round of the ML-guided workflow. Our study offers a comprehensive library of promoter strength modifications for key ethanol production enzymes, showcasing how machine learning can guide yeast strain optimization and make bioethanol production more cost-effective and efficient. Furthermore, we demonstrate that metabolic engineering processes can be accelerated and optimized through this approach.

A Blue Light-Responsive Strong Synthetic Promoter Based on Rational Design in Chlamydomonas reinhardtii

Chlamydomonas reinhardtii (C. reinhardtii) is a single-cell green alga that can be easily genetically manipulated. With its favorable characteristics of rapid growth, low cost, non-toxicity, and the ability for post-translational protein modification, C. reinhardtii has emerged as an attractive option for the biosynthesis of various valuable products. To enhance the expression level of exogenous genes and overcome the silencing of foreign genes by C. reinhardtii, synthetic promoters such as the chimeric promoter AR have been constructed and evaluated. In this study, a synthetic promoter GA was constructed by hybridizing core fragments from the natural promoters of the acyl carrier protein gene (ACP2) and the glutamate dehydrogenase gene (GDH2). The GA promoter exhibited a significant increase (7 times) in expressing GUS, over the AR promoter as positive control. The GA promoter also displayed a strong responsiveness to blue light (BL), where the GUS expression was doubled compared to the white light (WL) condition. The ability of the GA promoter was further tested in the expression of another exogenous cadA gene, responsible for catalyzing the decarboxylation of lysine to produce cadaverine. The cadaverine yield driven by the GA promoter was increased by 1-2 times under WL and 2-3 times under BL as compared to the AR promoter. This study obtained, for the first time, a blue light-responsive GDH2 minimal fragment in C. reinhardtii, which delivered a doubling effect under BL when used alone or in hybrid. Together with the strong GA synthetic promoter, this study offered useful tools of synthetic biology to the algal biotechnology field.

Biosensor-based high-throughput screening enabled efficient adipic acid production

Adipic acid is an industrially important chemical, but the current approach to synthesize it can be of serious pollution to the environment. Rencently, bio-based production of adipic acid has significantly advanced with the development of metabolic engineering and synthetic biology. However, genetic heterogeneity-caused decrease of product titer has largely limited the industrialization of chemicals like adipic acid. Therefore, in the attempt to overcome this challenge, we constitutively expressed the reverse adipate degradation pathway, designed and optimized an adipic acid biosensor, and established a high-throughput screening platform to screen for high-performance strains based on the optimized biosensor. Using this platform, we successfully screened a strain with an adipic acid titer of 188.08 mg·L^-1. Coupling the screening platform with fermentation optimization, the titer of adipic acid reached 531.88 mg·L^-1 under shake flask fermentation, which achieved an 18.78-fold improvement comparing to the initial strain. Scale-up fermentation in a 5-L fermenter utilizing the screened high-performance strain was eventually conducted, in which the adipic acid titer reached 3.62 g·L^-1. Overall, strategies developed in this study proved to be a potentially efficient method in reducing the genetic heterogeneity and was expected to provide guidance in helping to build a more efficient industrial screening process. KEY POINTS: • Developed a fine-tuned adipic acid biosensor. • Established a high-throughput screening platform to screen high-performance strains. • The titer of adipic acid reached 3.62 g·L^-1 in a 5-L fermenter.

EVMP: enhancing machine learning models for synthetic promoter strength prediction by Extended Vision Mutant Priority framework

Introduction

In metabolic engineering and synthetic biology applications, promoters with appropriate strengths are critical. However, it is time-consuming and laborious to annotate promoter strength by experiments. Nowadays, constructing mutation-based synthetic promoter libraries that span multiple orders of magnitude of promoter strength is receiving increasing attention. A number of machine learning (ML) methods are applied to synthetic promoter strength prediction, but existing models are limited by the excessive proximity between synthetic promoters.

Methods

In order to enhance ML models to better predict the synthetic promoter strength, we propose EVMP(Extended Vision Mutant Priority), a universal framework which utilize mutation information more effectively. In EVMP, synthetic promoters are equivalently transformed into base promoter and corresponding k-mer mutations, which are input into BaseEncoder and VarEncoder, respectively. EVMP also provides optional data augmentation, which generates multiple copies of the data by selecting different base promoters for the same synthetic promoter.

Results

In Trc synthetic promoter library, EVMP was applied to multiple ML models and the model effect was enhanced to varying extents, up to 61.30% (MAE), while the SOTA(state-of-the-art) record was improved by 15.25% (MAE) and 4.03% (R²). Data augmentation based on multiple base promoters further improved the model performance by 17.95% (MAE) and 7.25% (R²) compared with non-EVMP SOTA record.

Discussion

In further study, extended vision (or k-mer) is shown to be essential for EVMP. We also found that EVMP can alleviate the over-smoothing phenomenon, which may contributes to its effectiveness. Our work suggests that EVMP can highlight the mutation information of synthetic promoters and significantly improve the prediction accuracy of strength. The source code is publicly available on GitHub: https://github.com/Tiny-Snow/EVMP.

A novel toolbox for precise regulation of gene expression and metabolic engineering in Bacillus licheniformis

Despite industrial bio-manufacturing progress using Bacillus licheniformis, the absence of a well-characterized toolbox allowing precise regulation of multiple genes limits its expansion for basic research and application. Here, a novel gene expression toolbox (GET) was developed for precise regulation of gene expression and high-level production of 2-phenylethanol. Firstly, we established a novel promoter core region mosaic combination model to combine, characterize and analyze different core regions. Characterization and orthogonal design of promoter ribbons allowed convenient construction of an adaptable and robust GET, gene gfp expression intensity was 0.64%-16755.77%, with a dynamic range of 2.61 × 104 times, which is the largest regulatory range of GET in Bacillus based on modification of promoter P43. Then we verified the protein and species universality of GET using different proteins expressed in B. licheniformis and Bacillus subtilis. Finally, the GET for 2-phenylethanol metabolic breeding, resulting in a plasmid-free strain producing 6.95 g/L 2-phenylethanol with a yield and productivity of 0.15 g/g glucose and 0.14 g/L/h, respectively, the highest de novo synthesis yield of 2-phenylethanol reported. Taken together, this is the first report elucidating the impact of mosaic combination and tandem of multiple core regions to initiate transcription and improve the output of proteins and metabolites, which provides strong support for gene regulation and diversified product production in Bacillus.

Machine learning for metabolic pathway optimization: A review

Optimizing the metabolic pathways of microbial cell factories is essential for establishing viable biotechnological production processes. However, due to the limited understanding of the complex setup of cellular machinery, building efficient microbial cell factories remains tedious and time-consuming. Machine learning (ML), a powerful tool capable of identifying patterns within large datasets, has been used to analyze biological datasets generated using various high-throughput technologies to build data-driven models for complex bioprocesses. In addition, ML can also be integrated with Design-Build-Test-Learn to accelerate development. This review focuses on recent ML applications in genome-scale metabolic model construction, multistep pathway optimization, rate-limiting enzyme engineering, and gene regulatory element designing. In addition, we have discussed some limitations of these methods as well as potential solutions.

Spatiotemporal Gene Expression by a Genetic Circuit for Chemical Production in Escherichia coli

Gene expression in spatiotemporal distribution improves the ability of cells to respond to changing environments. For microbial cell factories in artificial environments, reconstruction of the target compound's biosynthetic pathway in a new spatiotemporal dimension/scale promotes the production of chemicals. Here, a genetic circuit based on the Esa quorum sensing and lac operon was designed to achieve the dynamic temporal gene expression. Meanwhile, the pathway was regulated by an l-cysteine-specific sensor and relocalized to the plasma membrane for further flux enhancement to l-cysteine and toxicity reduction on a spatial scale. Finally, the integrated spatiotemporal regulation circuit for l-cysteine biosynthesis enabled a 14.16 g/L l-cysteine yield in Escherichia coli. Furthermore, this spatiotemporal regulation circuit was also applied in our previously constructed engineered strain for pantothenic acid, methionine, homoserine, and 2-aminobutyric acid production, and the titer increased by 29, 33, 28, and 41%, respectively. These results highlighted the applicability of our spatiotemporal regulation circuit to enhance the performance of microbial cell factories.

Designing artificial synthetic promoters for accurate, smart, and versatile gene expression in plants

With the development of high-throughput biology techniques and artificial intelligence, it has become increasingly feasible to design and construct artificial biological parts, modules, circuits, and even whole systems. To overcome the limitations of native promoters in controlling gene expression, artificial promoter design aims to synthesize short, inducible, and conditionally controlled promoters to coordinate the expression of multiple genes in diverse plant metabolic and signaling pathways. Synthetic promoters are versatile and can drive gene expression accurately with smart responses; they show potential for enhancing desirable traits in crops, thereby improving crop yield, nutritional quality, and food security. This review first illustrates the importance of synthetic promoters, then introduces promoter architecture and thoroughly summarizes advances in synthetic promoter construction. Restrictions to the development of synthetic promoters and future applications of such promoters in synthetic plant biology and crop improvement are also discussed.

Synthetic Biology Meets Machine Learning

This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.

Recent advances in machine learning applications in metabolic engineering

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

Sequence and thermodynamic characteristics of terminators revealed by FlowSeq and the discrimination of terminators strength

The intrinsic terminator in prokaryotic forms secondary RNA structure and terminates the transcription. However, leaking transcription is common due to varied terminator strength. Besides of the representative hairpin and U-tract structure, detailed sequence and thermodynamic features of terminators were not completely clear, and the effect of terminator on the upstream gene expression was unclearly. Thus, it is still challenging to use terminator to control expression with higher precision. Here, in E. Coli, we firstly determined the effect of the 3′-end sequences including spacer sequences and terminator sequences on the expression of upstream and downstream genes. Secondly, terminator mutation library was constructed, and the thermodynamic and sequence features differing in the termination efficiency were analyzed using the FlowSeq technique. The result showed that under the regulation of terminators, a negative correlation was presented between the expression of upstream and downstream genes (r=−0.60), and the terminators with lower free energy corelated with higher upstream gene expression. Meanwhile, the terminator with longer stem length, more compact loop and perfect U-tract structure was benefit to the transcription termination. Finally, a terminator strength classification model was established, and the verification experiment based on 20 synthetic terminators indicated that the model can distinguish strong and weak terminators to certain extent. The results help to elucidate the role of terminators in gene expression, and the key factors identified are crucial for rational design of terminators, and the model provided a method for terminator strength prediction.

Advances in engineering and optimization of transcription factor-based biosensors for plug-and-play small molecule detection

Transcription factor (TF)-based biosensors have been applied in biotechnology for a variety of functions, including protein engineering, dynamic control, environmental detection, and point-of-care diagnostics. Such biosensors are promising analytical tools due to their wide range of detectable ligands and modular nature. However, designing biosensors tailored for applications of interest with the desired performance parameters, including ligand specificity, remains challenging. Biosensors often require significant engineering and tuning to meet desired specificity, sensitivity, dynamic range, and operating range parameters. Another limitation is the orthogonality of biosensors across hosts, given the role of the cellular context. Here, we describe recent advances and examples in the engineering and optimization of TF-based biosensors for plug-and-play small molecule detection. We highlight novel developments in TF discovery and biosensor design, TF specificity engineering, and biosensor tuning, with emphasis on emerging computational methods.