De novo design of programmable inducible promoters

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.

CRISPR Tools for Engineering Prokaryotic Systems: Recent Advances and New Applications

In the past decades, the broad selection of CRISPR-Cas systems has revolutionized biotechnology by enabling multimodal genetic manipulation in diverse organisms. Rooted in a molecular engineering perspective, we recapitulate the different CRISPR components and how they can be designed for specific genetic engineering applications. We first introduce the repertoire of Cas proteins and tethered effectors used to program new biological functions through gene editing and gene regulation. We review current guide RNA (gRNA) design strategies and computational tools and how CRISPR-based genetic circuits can be constructed through regulated gRNA expression. Then, we present recent advances in CRISPR-based biosensing, bioproduction, and biotherapeutics across in vitro and in vivo prokaryotic systems. Finally, we discuss forthcoming applications in prokaryotic CRISPR technology that will transform synthetic biology principles in the near future.

Utilizing 5' UTR Engineering Enables Fine-Tuning of Multiple Genes within Operons to Balance Metabolic Flux in Bacillus subtilis

The application of synthetic biology tools to modulate gene expression to increase yield has been thoroughly demonstrated as an effective and convenient approach in industrial production. In this study, we employed a high-throughput screening strategy to identify a 5' UTR sequence from the genome of B. subtilis 168. This sequence resulted in a 5.8-fold increase in the expression level of EGFP. By utilizing the 5' UTR sequence to overexpress individual genes within the rib operon, it was determined that the genes ribD and ribAB serve as rate-limiting enzymes in the riboflavin synthesis pathway. Constructing a 5' UTR library to regulate EGFP expression resulted in a variation range in gene expression levels exceeding 100-fold. Employing the same 5' UTR library to regulate the expression of EGFP and mCherry within the operon led to a change in the expression ratio of these two genes by over 10,000-fold. So, employing a 5' UTR library to modulate the expression of the rib operon gene and construct a synthetic rib operon resulted in a 2.09-fold increase in riboflavin production. These results indicate that the 5' UTR sequence identified and characterized in this study can serve as a versatile synthetic biology toolkit for achieving complex metabolic network reconstruction. This toolkit can facilitate the fine-tuning of gene expression to produce target products.

A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits

Within a cell, synthetic and native genes compete for expression machinery, influencing cellular process dynamics through resource couplings. Models that simplify competitive resource binding kinetics can guide the design of strategies for countering these couplings. However, in bacteria resource availability and cell growth rate are interlinked, which complicates resource-aware biocircuit design. Capturing this interdependence requires coarse-grained bacterial cell models that balance accurate representation of metabolic regulation against simplicity and interpretability. We propose a coarse-grained E. coli cell model that combines the ease of simplified resource coupling analysis with appreciation of bacterial growth regulation mechanisms and the processes relevant for biocircuit design. Reliably capturing known growth phenomena, it provides a unifying explanation to disparate empirical relations between growth and synthetic gene expression. Considering a biomolecular controller that makes cell-wide ribosome availability robust to perturbations, we showcase our model's usefulness in numerically prototyping biocircuits and deriving analytical relations for design guidance.

Synthetic Homoserine Lactone Sensors for Gram-Positive Bacillus subtilis Using LuxR-Type Regulators

A universal biochemical signal for bacterial cell-cell communication could facilitate programming dynamic responses in diverse bacterial consortia. However, the classical quorum sensing paradigm is that Gram-negative and Gram-positive bacteria generally communicate via homoserine lactones (HSLs) or oligopeptide molecular signals, respectively, to elicit population responses. Here, we create synthetic HSL sensors for Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and CinR) and synthetic promoters. Promoters were combinatorially designed from different sequence elements (-35, -16, -10, and transcriptional start regions). We quantified the effects of these combinatorial promoters on sensor activity and determined how regulator expression affects its activation, achieving up to 293-fold activation. Using the statistical design of experiments, we identified significant effects of promoter regions and pairwise interactions on sensor activity, which helped to understand the sequence-function relationships for synthetic promoter design. We present the first known set of functional HSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p-coumaroyl-HSL, 3-oxohexanoyl-HSL, n-butyryl-HSL, and n-(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors for a Gram-positive bacterium can pave the way for designable interspecies communication within microbial consortia.

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 and synthetic biology strategies for producing high-value natural pigments in Microalgae

Microalgae are microorganisms capable of producing bioactive compounds using photosynthesis. Microalgae contain a variety of high value-added natural pigments such as carotenoids, phycobilins, and chlorophylls. These pigments play an important role in many areas such as food, pharmaceuticals, and cosmetics. Natural pigments have a health value that is unmatched by synthetic pigments. However, the current commercial production of natural pigments from microalgae is not able to meet the growing market demand. The use of metabolic engineering and synthetic biological strategies to improve the production performance of microalgal cell factories is essential to promote the large-scale production of high-value pigments from microalgae. This paper reviews the health and economic values, the applications, and the synthesis pathways of microalgal pigments. Overall, this review aims to highlight the latest research progress in metabolic engineering and synthetic biology in constructing engineered strains of microalgae with high-value pigments and the application of CRISPR technology and multi-omics in this context. Finally, we conclude with a discussion on the bottlenecks and challenges of microalgal pigment production and their future development prospects.

Applications of artificial intelligence and machine learning in dynamic pathway engineering

Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.

Deep flanking sequence engineering for efficient promoter design using DeepSEED

Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.

A lentiviral vector for the production of T cells with an inducible transgene and a constitutively expressed tumour-targeting receptor

Vectors that facilitate the engineering of T cells that can better harness endogenous immunity and overcome suppressive barriers in the tumour microenvironment would help improve the safety and efficacy of T-cell therapies for more patients. Here we report the design, production and applicability, in T-cell engineering, of a lentiviral vector leveraging an antisense configuration and comprising a promoter driving the constitutive expression of a tumour-directed receptor and a second promoter enabling the efficient activation-inducible expression of a genetic payload. The vector allows for the delivery of a variety of genes to human T cells, as we show for interleukin-2 and a microRNA-based short hairpin RNA for the knockdown of the gene coding for haematopoietic progenitor kinase 1, a negative regulator of T-cell-receptor signalling. We also show that a gene encoded under an activation-inducible promoter is specifically expressed by tumour-redirected T cells on encountering a target antigen in the tumour microenvironment. The single two-gene-encoding vector can be produced at high titres under an optimized protocol adaptable to good manufacturing practices.

Cell factory engineering: Challenges and opportunities for synthetic biology applications

The production of high-quality recombinant proteins is critical to maintaining a continuous supply of biopharmaceuticals, such as therapeutic antibodies. Engineering mammalian cell factories presents a number of limitations typically associated with the proteotoxic stress induced upon aberrant accumulation of off-pathway protein folding intermediates, which eventually culminate in the induction of apoptosis. In this review, we will discuss advances in cell engineering and their applications at different hierarchical levels of control of the expression of recombinant proteins, from transcription and translational to posttranslational modifications and subcellular trafficking. We also highlight challenges and unique opportunities to apply modern synthetic biology tools to the design of programmable cell factories for improved biomanufacturing of therapeutic proteins.

Engineering activatable promoters for scalable and multi-input CRISPRa/i circuits

Dynamic, multi-input gene regulatory networks (GRNs) are ubiquitous in nature. Multilayer CRISPR-based genetic circuits hold great promise for building GRNs akin to those found in naturally occurring biological systems. We develop an approach for creating high-performing activatable promoters that can be assembled into deep, wide, and multi-input CRISPR-activation and -interference (CRISPRa/i) GRNs. By integrating sequence-based design and in vivo screening, we engineer activatable promoters that achieve up to 1,000-fold dynamic range in an Escherichia coli-based cell-free system. These components enable CRISPRa GRNs that are six layers deep and four branches wide. We show the generalizability of the promoter engineering workflow by improving the dynamic range of the light-dependent EL222 optogenetic system from 6-fold to 34-fold. Additionally, high dynamic range promoters enable CRISPRa systems mediated by small molecules and protein-protein interactions. We apply these tools to build input-responsive CRISPRa/i GRNs, including feedback loops, logic gates, multilayer cascades, and dynamic pulse modulators. Our work provides a generalizable approach for the design of high dynamic range activatable promoters and enables classes of gene regulatory functions in cell-free systems.

Engineering an AI-based forward-reverse platform for the design of cross-ribosome binding sites of a transcription factor biosensor

A cross-ribosome binding site (cRBS) adjusts the dynamic range of transcription factor-based biosensors (TFBs) by controlling protein expression and folding. The rational design of a cRBS with desired TFB dynamic range remains an important issue in TFB forward and reverse engineering. Here, we report a novel artificial intelligence (AI)-based forward-reverse engineering platform for TFB dynamic range prediction and de novo cRBS design with selected TFB dynamic ranges. The platform demonstrated superior in processing unbalanced minority-class datasets and was guided by sequence characteristics from trained cRBSs. The platform identified correlations between cRBSs and dynamic ranges to mimic bidirectional design between these factors based on Wasserstein generative adversarial network (GAN) with a gradient penalty (GP) (WGAN-GP) and balancing GAN with GP (BAGAN-GP). For forward and reverse engineering, the predictive accuracy was up to 98% and 82%, respectively. Collectively, we generated an AI-based method for the rational design of TFBs with desired dynamic ranges.

Characterizing and utilizing oxygen-dependent promoters for efficient dynamic metabolic engineering

Promoters adjust cellular gene expression in response to internal or external signals and are key elements for implementing dynamic metabolic engineering concepts in fermentation processes. One useful signal is the dissolved oxygen content of the culture medium, since production phases often proceed in anaerobic conditions. Although several oxygen-dependent promoters have been described, a comprehensive and comparative study is missing. The goal of this work is to systematically test and characterize 15 promoter candidates that have been previously reported to be induced upon oxygen depletion in Escherichia coli. For this purpose, we developed a microtiter plate-level screening using an algal oxygen-independent flavin-based fluorescent protein and additionally employed flow cytometry analysis for verification. Various expression levels and dynamic ranges could be observed, and six promoters (nar-strong, nar-medium, nar-weak, nirB-m, yfiD-m, and fnrF8) appear particularly suited for dynamic metabolic engineering applications. We demonstrate applicability of these candidates for dynamic induction of enforced ATP wasting, a metabolic engineering approach to increase productivity of microbial strains that requires a narrow level of ATPase expression for optimal function. The selected candidates exhibited sufficient tightness under aerobic conditions while, under complete anaerobiosis, driving expression of the cytosolic F₁-subunit of the ATPase from E. coli to levels that resulted in unprecedented specific glucose uptake rates. We finally utilized the nirB-m promoter to demonstrate the optimization of a two-stage lactate production process by dynamically enforcing ATP wasting, which is automatically turned on in the anaerobic (growth-arrested) production phase to boost the volumetric productivity. Our results are valuable for implementing metabolic control and bioprocess design concepts that use oxygen as signal for regulation and induction.

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.

De novo design of the global transcriptional factor Cra-regulated promoters enables highly sensitive glycolysis flux biosensor for dynamic metabolic control

Glycolytic flux is a fundamental index in microbial cell factories. A glycolytic flux biosensor that can monitor glucose metabolism efficiency is a promising strategy in rewiring metabolic flux to balance growth and biosynthesis. A key design feature of the glycolytic flux biosensors is the interaction between the global transcriptional factor Cra and its regulated promoters. However, overexpression and mutation of Cra has unpredictable effects on global metabolism in Escherichia coli. Therefore, new orthogonal biosensor design strategies should be developed to circumvent metabolic issues. In this report, the promoters in glycolytic flux biosensor were replaced with synthetic promoters of varying strengths or phage-derived promoters, and the Cra DNA-binding sites were deployed into promoters at different positions and distances to yield biosensors. The de nova biosensors that depended on Cra could sense Fructose-1,6-diphosphate (FBP) with broad dynamic ranges and low basal leakage. Then the negative-response biosensors were applied to fine-tune the target ATP synthesis gene, leading to the desired increase in pyruvate production (the highest 9.66 g/L) and cell growth. Moreover, the membrane synthesis gene plsC was also dynamically activated by the positive-response biosensor, leading to effective accumulation of lycopene in the cell membrane and a 50-fold increase in lycopene titre (100.3 mg/L) when compared with the control strain, demonstrating the effective and broader usages of our biosensors.

Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications

Genetically encoded biosensors are the vital components of synthetic biology and metabolic engineering, as they are regarded as powerful devices for the dynamic control of genotype metabolism and evolution/screening of desirable phenotypes. This review summarized the recent advances in the construction and applications of different genetically encoded biosensors, including fluorescent protein-based biosensors, nucleic acid-based biosensors, allosteric transcription factor-based biosensors and two-component system-based biosensors. First, the construction frameworks of these biosensors were outlined. Then, the recent progress of biosensor applications in creating versatile microbial cell factories for the bioproduction of high-value chemicals was summarized. Finally, the challenges and prospects for constructing robust and sophisticated biosensors were discussed. This review provided theoretical guidance for constructing genetically encoded biosensors to create desirable microbial cell factories for sustainable bioproduction.

Inducible plasmid copy number control for synthetic biology in commonly used E. coli strains

The ability to externally control gene expression has been paradigm shifting for all areas of biological research, especially for synthetic biology. Such control typically occurs at the transcriptional and translational level, while technologies enabling control at the DNA copy level are limited by either (i) relying on a handful of plasmids with fixed and arbitrary copy numbers; or (ii) require multiple plasmids for replication control; or (iii) are restricted to specialized strains. To overcome these limitations, we present TULIP (TUnable Ligand Inducible Plasmid): a self-contained plasmid with inducible copy number control, designed for portability across various Escherichia coli strains commonly used for cloning, protein expression, and metabolic engineering. Using TULIP, we demonstrate through multiple application examples that flexible plasmid copy number control accelerates the design and optimization of gene circuits, enables efficient probing of metabolic burden, and facilitates the prototyping and recycling of modules in different genetic contexts.

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.

De Novo Design of the ArsR Regulated Pars Promoter Enables a Highly Sensitive Whole-Cell Biosensor for Arsenic Contamination

Whole-cell biosensors for arsenic contamination are typically designed based on natural bacterial sensing systems, which are often limited by their poor performance for precisely tuning the genetic response to environmental stimuli. Promoter design remains one of the most important approaches to address such issues. Here, we use the arsenic-responsive ArsR-P_ars regulation system from Escherichia coli MG1655 as the sensing element and coupled gfp or lacZ as the reporter gene to construct the genetic circuit for characterizing the refactored promoters. We first analyzed the ArsR binding site and a library of RNA polymerase binding sites to mine potential promoter sequences. A set of tightly regulated P_ars promoters by ArsR was designed by placing the ArsR binding sites into the promoter's core region, and a novel promoter with maximal repression efficiency and optimal fold change was obtained. The fluorescence sensor P_lacV-P_arsOC2 constructed with the optimized P_arsOC2 promoter showed a fold change of up to 63.80-fold (with green fluorescence visible to the naked eye) at 9.38 ppb arsenic, and the limit of detection was as low as 0.24 ppb. Further, the optimized colorimetric sensor P_lacV-P_arsOC2-lacZ with a linear response between 0 and 5 ppb was used to perform colorimetric reactions in 24-well plates combined with a smartphone application for the quantification of the arsenic level in groundwater. This study offers a new approach to improve the performance of bacterial sensing promoters and will facilitate the on-site application of arsenic whole-cell biosensors.

Engineering a Dynamic Controllable Infectivity Switch in Bacteriophage T7

Transcriptional repressors play an important role in regulating phage life cycle. Here, we examine how synthetic transcription repressors can be used in bacteriophage T7 to create a dynamic, controllable infectivity switch. We engineered T7 phage by replacing a large region of the early phage genome with different combinations of ligand-responsive promoters and ribosome binding sites (RBS) designed to control the phage RNA polymerase, gp1. Phages with engineered infectivity switch are fully viable at levels comparable to wildtype T7, when not repressed, indicating the phage can be engineered without loss of fitness. The most effective switch used a TetR-responsive promoter and an attenuated RBS, resulting in a 2-fold increase in latent period and a 10-fold decrease in phage titer when repressed. Phage activity can be further tuned using different inducer concentrations. Our study provides a proof of concept for how a simple synthetic circuit introduced into the phage genome enables user control over phage infectivity.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Transcription-Factor-based Biosensor Engineering for Applications in Synthetic Biology

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.

Recent Progress Using De Novo Design to Study Protein Structure, Design and Binding Interactions

De novo protein design is a powerful methodology used to study natural functions in an artificial-protein context. Since its inception, it has been used to reproduce a plethora of reactions and uncover biophysical principles that are often difficult to extract from direct studies of natural proteins. Natural proteins are capable of assuming a variety of different structures and subsequently binding ligands at impressively high levels of both specificity and affinity. Here, we will review recent examples of de novo design studies on binding reactions for small molecules, nucleic acids, and the formation of protein-protein interactions. We will then discuss some new structural advances in the field. Finally, we will discuss some advancements in computational modeling and design approaches and provide an overview of some modern algorithmic tools being used to design these proteins.

Translational control of enzyme scavenger expression with toxin-induced micro RNA switches

Biological computation requires in vivo control of molecular behavior to progress development of autonomous devices. miRNA switches represent excellent, easily engineerable synthetic biology tools to achieve user-defined gene regulation. Here we present the construction of a synthetic network to implement detoxification functionality. We employed a modular design strategy by engineering toxin-induced control of an enzyme scavenger. Our miRNA switch results show moderate synthetic expression control over a biologically active detoxification enzyme molecule, using an established design protocol. However, following a new design approach, we demonstrated an evolutionarily designed miRNA switch to more effectively activate enzyme activity than synthetically designed versions, allowing markedly improved extrinsic user-defined control with a toxin as inducer. Our straightforward new design approach is simple to implement and uses easily accessible web-based databases and prediction tools. The ability to exert control of toxicity demonstrates potential for modular detoxification systems that provide a pathway to new therapeutic and biocomputing applications.

Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems

A crucial step towards engineering biological systems is the ability to precisely tune the genetic response to environmental stimuli. In the case of Escherichia coli inducible promoters, our incomplete understanding of the relationship between sequence composition and gene expression hinders our ability to predictably control transcriptional responses. Here, we profile the expression dynamics of 8269 rationally designed, IPTG-inducible promoters that collectively explore the individual and combinatorial effects of RNA polymerase and LacI repressor binding site strengths. We then fit a statistical mechanics model to measured expression that accurately models gene expression and reveals properties of theoretically optimal inducible promoters. Furthermore, we characterize three alternative promoter architectures and show that repositioning binding sites within promoters influences the types of combinatorial effects observed between promoter elements. In total, this approach enables us to deconstruct relationships between inducible promoter elements and discover practical insights for engineering inducible promoters with desirable characteristics.

Cell-free biosensors for rapid detection of water contaminants

Lack of access to safe drinking water is a global problem, and methods to reliably and easily detect contaminants could be transformative. We report the development of a cell-free in vitro transcription system that uses RNA Output Sensors Activated by Ligand Induction (ROSALIND) to detect contaminants in water. A combination of highly processive RNA polymerases, allosteric protein transcription factors and synthetic DNA transcription templates regulates the synthesis of a fluorescence-activating RNA aptamer. The presence of a target contaminant induces the transcription of the aptamer, and a fluorescent signal is produced. We apply ROSALIND to detect a range of water contaminants, including antibiotics, small molecules and metals. We also show that adding RNA circuitry can invert responses, reduce crosstalk and improve sensitivity without protein engineering. The ROSALIND system can be freeze-dried for easy storage and distribution, and we apply it in the field to test municipal water supplies, demonstrating its potential use for monitoring water quality.

Strategies for enhancing gene expression in Escherichia coli

Regulation of gene expression is fundamental for cellular function. Upon manipulation of the mechanism of gene expression in Escherichia coli, various bioproducts have been developed that are valuable industrially and medically in the last four decades. To efficiently produce bioproducts, numerous molecular tools are used for enhancing expression at the transcriptional and translational levels. Our recent discovery identified a new approach that enhances the gene expression in E. coli using the gene sequence of the eukaryote, Dictyostelium discoideum. In this review, we highlight the current molecular strategies used for high-level gene expression techniques commonly utilized in basic and applied microbiology.

An autoinducible trp-T7 expression system for production of proteins and biochemicals in Escherichia coli

Inducible expression systems can be applied to control the expression of proteins or biochemical pathways in cell factories. However, several of the established systems require the addition of expensive inducers, making them unfeasible for large‐scale production. Here, we establish a genome integrated trp‐T7 expression system where tryptophan can be used to control the induction of a gene or a metabolic pathway. We show that the initiation of gene expression from low‐ and high‐copy vectors can be tuned by varying the initial concentration of tryptophan or yeast extract, and that expression is tightly regulated and homogenous when compared with the commonly used lac‐T7 system. Finally, we apply the trp‐T7 expression system for the production of l‐serine, where we reach titers of 26 g/L in fed‐batch fermentation.