Designing RNA-based genetic control systems for efficient production from engineered metabolic pathways

Multidimensional Applications and Challenges of Riboswitches in Biosensing and Biotherapy

Riboswitches have received significant attention over the last two decades for their multiple functionalities and great potential for applications in various fields. This article highlights and reviews the recent advances in biosensing and biotherapy. These fields involve a wide range of applications, such as food safety detection, environmental monitoring, metabolic engineering, live cell imaging, wearable biosensors, antibacterial drug targets, and gene therapy. The discovery, origin, and optimization of riboswitches are summarized to help readers better understand their multidimensional applications. Finally, this review discusses the multidimensional challenges and development of riboswitches in order to further expand their potential for novel applications.

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.

Bayesian Optimization for Design of Multiscale Biological Circuits

Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.

Direct evolution of riboflavin kinase significantly enhance flavin mononucleotide synthesis by design and optimization of flavin mononucleotide riboswitch

Flavin mononucleotide (FMN) is the active form of riboflavin. It has a wide range of application scenarios in the pharmaceutical and food additives. However, there are limitations in selecting generic high-throughput screening platforms that improve the properties of enzymes. First, the biosensor in response to FMN concentration was constructed using the FMN riboswitch and confirmed the function of this sensor. Next, the FMN binding site of the sensor was saturated with a mutation that increased its fluorescence range by approximately 127%. Then, the biosensor and the base editing system based on T7RNAP were combined to construct a platform for rapid mutation and screening of riboflavin kinase gene ribC mutants. The mutants screened using this platform increased the yield of FMN by 8-fold. These results indicate that the high-throughput screening platform can rapidly and effectively improve the activity of target enzymes, and provide a new route for screening industrial enzymes.

Transcription factor-based biosensors for screening and dynamic regulation

Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.

Dynamic RNA synthetic biology: new principles, practices and potential

An increased appreciation of the role of RNA dynamics in governing RNA function is ushering in a new wave of dynamic RNA synthetic biology. Here, we review recent advances in engineering dynamic RNA systems across the molecular, circuit and cellular scales for important societal-scale applications in environmental and human health, and bioproduction. For each scale, we introduce the core concepts of dynamic RNA folding and function at that scale, and then discuss technologies incorporating these concepts, covering new approaches to engineering riboswitches, ribozymes, RNA origami, RNA strand displacement circuits, biomaterials, biomolecular condensates, extracellular vesicles and synthetic cells. Considering the dynamic nature of RNA within the engineering design process promises to spark the next wave of innovation that will expand the scope and impact of RNA biotechnologies.

Riboswitch-inspired toehold riboregulators for gene regulation in Escherichia coli

Regulatory RNA molecules have been widely investigated as components for synthetic gene circuits, complementing the use of protein-based transcription factors. Among the potential advantages of RNA-based gene regulators are their comparatively simple design, sequence-programmability, orthogonality, and their relatively low metabolic burden. In this work, we developed a set of riboswitch-inspired riboregulators in Escherichia coli that combine the concept of toehold-mediated strand displacement (TMSD) with the switching principles of naturally occurring transcriptional and translational riboswitches. Specifically, for translational activation and repression, we sequestered anti-anti-RBS or anti-RBS sequences, respectively, inside the loop of a stable hairpin domain, which is equipped with a single-stranded toehold region at its 5' end and is followed by regulated sequences on its 3' side. A trigger RNA binding to the toehold region can invade the hairpin, inducing a structural rearrangement that results in translational activation or deactivation. We also demonstrate that TMSD can be applied in the context of transcriptional regulation by switching RNA secondary structure involved in Rho-dependent termination. Our designs expand the repertoire of available synthetic riboregulators by a set of RNA switches with no sequence limitation, which should prove useful for the development of robust genetic sensors and circuits.

Modeling and Optimization of a Molecular Biocontroller for the Regulation of Complex Metabolic Pathways

Achieving optimal production in microbial cell factories, robustness against changing intracellular and environmental perturbations requires the dynamic feedback regulation of the pathway of interest. Here, we consider a merging metabolic pathway motif, which appears in a wide range of metabolic engineering applications, including the production of phenylpropanoids among others. We present an approach to use a realistic model that accounts for in vivo implementation and then propose a methodology based on multiobjective optimization for the optimal tuning of the gene circuit parts composing the biomolecular controller and biosensor devices for a dynamic regulation strategy. We show how this approach can deal with the trade-offs between the performance of the regulated pathway, robustness to perturbations, and stability of the feedback loop. Using realistic models, our results suggest that the strategies for fine-tuning the trade-offs among performance, robustness, and stability in dynamic pathway regulation are complex. It is not always possible to infer them by simple inspection. This renders the use of the multiobjective optimization methodology valuable and necessary.

Multi-layer CRISPRa/i circuits for dynamic genetic programs in cell-free and bacterial systems

CRISPR-Cas transcriptional circuits hold great promise as platforms for engineering metabolic networks and information processing circuits. Historically, prokaryotic CRISPR control systems have been limited to CRISPRi. Creating approaches to integrate CRISPRa for transcriptional activation with existing CRISPRi-based systems would greatly expand CRISPR circuit design space. Here, we develop design principles for engineering prokaryotic CRISPRa/i genetic circuits with network topologies specified by guide RNAs. We demonstrate that multi-layer CRISPRa/i cascades and feedforward loops can operate through the regulated expression of guide RNAs in cell-free expression systems and E. coli. We show that CRISPRa/i circuits can program complex functions by designing type 1 incoherent feedforward loops acting as fold-change detectors and tunable pulse-generators. By investigating how component characteristics relate to network properties such as depth, width, and speed, this work establishes a framework for building scalable CRISPRa/i circuits as regulatory programs in cell-free expression systems and bacterial hosts. A record of this paper's transparent peer review process is included in the supplemental information.

Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.

Real-Time Optogenetics System for Controlling Gene Expression Using a Model-Based Design

Optimization of engineered biological systems requires precise control over the rates and timing of gene expression. Optogenetics is used to dynamically control gene expression as an alternative to conventional chemical-based methods since it provides a more convenient interface between digital control software and microbial culture. Here, we describe the construction of a real-time optogenetics platform, which performs closed-loop control over the CcaR-CcaS two-plasmid system in Escherichia coli. We showed the first model-based design approach by constructing a nonlinear representation of the CcaR-CcaS system, tuned the model through open-loop experimentation to capture the experimental behavior, and applied the model in silico to inform the necessary changes to build a closed-loop optogenetic control system. Our system periodically induces and represses the CcaR-CcaS system while recording optical density and fluorescence using image processing techniques. We highlight the facile nature of constructing our system and how our model-based design approach will potentially be used to model other systems requiring closed-loop optogenetic control.

Dynamic control in metabolic engineering: Theories, tools, and applications

Metabolic engineering has allowed the production of a diverse number of valuable chemicals using microbial organisms. Many biological challenges for improving bio-production exist which limit performance and slow the commercialization of metabolically engineered systems. Dynamic metabolic engineering is a rapidly developing field that seeks to address these challenges through the design of genetically encoded metabolic control systems which allow cells to autonomously adjust their flux in response to their external and internal metabolic state. This review first discusses theoretical works which provide mechanistic insights and design choices for dynamic control systems including two-stage, continuous, and population behavior control strategies. Next, we summarize molecular mechanisms for various sensors and actuators which enable dynamic metabolic control in microbial systems. Finally, important applications of dynamic control to the production of several metabolite products are highlighted, including fatty acids, aromatics, and terpene compounds. Altogether, this review provides a comprehensive overview of the progress, advances, and prospects in the design of dynamic control systems for improved titer, rate, and yield metrics in metabolic engineering.

Computation of Single-Cell Metabolite Distributions Using Mixture Models

Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories

Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implementing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.

Advances in engineered trans-acting regulatory RNAs and their application in bacterial genome engineering

Small noncoding RNAs, a large class of ancient posttranscriptional regulators, are increasingly recognized and utilized as key modulators of gene expression in a broad range of microorganisms. Owing to their small molecular size and the central role of Watson-Crick base pairing in defining their interactions, structure and function, numerous diverse types of trans-acting RNA regulators that are functional at the DNA, mRNA and protein levels have been experimentally characterized. It has become increasingly clear that most small RNAs play critical regulatory roles in many processes and are, therefore, considered to be powerful tools for genetic engineering and synthetic biology. The trans-acting regulatory RNAs accelerate this ability to establish potential framework for genetic engineering and genome-scale engineering, which allows RNA structure characterization, easier to design and model compared to DNA or protein-based systems. In this review, we summarize recent advances in engineered trans-acting regulatory RNAs that are used in bacterial genome-scale engineering and in novel cellular capabilities as well as their implementation in wide range of biotechnological, biological and medical applications.

Regulated Expression of sgRNAs Tunes CRISPRi in E. coli

Methods for implementing dynamically-controlled multi-gene programs could expand capabilities to engineer metabolism for efficiently producing high-value compounds. This work explores whether CRISPRi repression can be tuned in E. coli through the regulated expression of the CRISPRi machinery. When dCas9 is not limiting, variations in sgRNA expression alone can lead to CRISPRi repression levels ranging from 5- to 300-fold. Titrating sgRNA expression over a 2.5-fold range results in 16-fold changes in reporter gene expression. Many different classes of genetic controllers can generate 2.5-fold differences in transcription, suggesting they may be integrated into dynamically-regulated CRISPRi circuits. Finally, CRISPRi cannot be reversed for up to 12 hours by expressing a competing sgRNA later in the growth phase, indicating that CRISPR-Cas:DNA interactions can be persistent in vivo. Collectively, these results identify genetic architectures for tuning CRISPRi repression through regulated sgRNA expression and suggest that dynamically-regulated CRISPRi systems targeting multiple genes may be within reach.

Prospects for engineering dynamic CRISPR–Cas transcriptional circuits to improve bioproduction

Dynamic control of gene expression is emerging as an important strategy for controlling flux in metabolic pathways and improving bioproduction of valuable compounds. Integrating dynamic genetic control tools with CRISPR–Cas transcriptional regulation could significantly improve our ability to fine-tune the expression of multiple endogenous and heterologous genes according to the state of the cell. In this mini-review, we combine an analysis of recent literature with examples from our own work to discuss the prospects and challenges of developing dynamically regulated CRISPR–Cas transcriptional control systems for applications in synthetic biology and metabolic engineering.

Dynamic metabolic control: towards precision engineering of metabolism

Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.

Multilevel Regulation of Bacterial Gene Expression with the Combined STAR and Antisense RNA System

Synthetic small RNA regulators have emerged as a versatile tool to predictably control bacterial gene expression. Owing to their simple design principles, small size, and highly orthogonal behavior, these engineered genetic parts have been incorporated into genetic circuits. However, efforts to achieve more sophisticated cellular functions using RNA regulators have been hindered by our limited ability to integrate different RNA regulators into complex circuits. Here, we present a combined RNA regulatory system in Escherichia coli that uses small transcription activating RNA (STAR) and antisense RNA (asRNA) to activate or deactivate target gene expression in a programmable manner. Specifically, we demonstrated that the activated target output by the STAR system can be deactivated by expressing two different types of asRNAs: one binds to and sequesters the STAR regulator, affecting the transcription process, while the other binds to the target mRNA, affecting the translation process. We improved deactivation efficiencies (up to 96%) by optimizing each type of asRNA and then integrating the two optimized asRNAs into a single circuit. Furthermore, we demonstrated that the combined STAR and asRNA system can control gene expression in a reversible way and can regulate expression of a gene in the genome. Lastly, we constructed and simultaneously tested two A AND NOT B logic gates in the same cell to show sophisticated multigene regulation by the combined system. Our approach establishes a methodology for integrating multiple RNA regulators to rationally control multiple genes.

Design and Selection of a Synthetic Feedback Loop for Optimizing Biofuel Tolerance

Feedback control allows cells to dynamically sense and respond to environmental changes. However, synthetic controller designs can be challenging because of implementation issues, such as determining optimal expression levels for circuit components within a feedback loop. Here, we addressed this by coupling rational design with selection to engineer a synthetic feedback circuit to optimize tolerance of Escherichia coli to the biojet fuel pinene. E. coli can be engineered to produce pinene, but it is toxic to cells. Efflux pumps, such as the AcrAB-TolC pump, can improve tolerance, but pump expression impacts growth. To address this, we used feedback to dynamically regulate pump expression in response to stress. We developed a library with thousands of synthetic circuit variants and subjected it to three types of pinene treatment (none, constant, and varying pinene). We were able to select for strains that were biofuel tolerant without a significant growth cost in the absence of biofuel. Using next-generation sequencing, we found common characteristics in the designs and identified controllers that dramatically improved biofuel tolerance.

Genetic tool development and systemic regulation in biosynthetic technology

With the increased development in research, innovation, and policy interest in recent years, biosynthetic technology has developed rapidly, which combines engineering, electronics, computer science, mathematics, and other disciplines based on classical genetic engineering and metabolic engineering. It gives a wider perspective and a deeper level to perceive the nature of life via cell mechanism, regulatory networks, or biological evolution. Currently, synthetic biology has made great breakthrough in energy, chemical industry, and medicine industries, particularly in the programmable genetic control at multiple levels of regulation to perform designed goals. In this review, the most advanced and comprehensive developments achieved in biosynthetic technology were represented, including genetic engineering as well as synthetic genomics. In addition, the superiority together with the limitations of the current genome-editing tools were summarized.

Transcription control engineering and applications in synthetic biology

In synthetic biology, researchers assemble biological components in new ways to produce systems with practical applications. One of these practical applications is control of the flow of genetic information (from nucleic acid to protein), a.k.a. gene regulation. Regulation is critical for optimizing protein (and therefore activity) levels and the subsequent levels of metabolites and other cellular properties. The central dogma of molecular biology posits that information flow commences with transcription, and accordingly, regulatory tools targeting transcription have received the most attention in synthetic biology. In this mini-review, we highlight many past successes and summarize the lessons learned in developing tools for controlling transcription. In particular, we focus on engineering studies where promoters and transcription terminators (cis-factors) were directly engineered and/or isolated from DNA libraries. We also review several well-characterized transcription regulators (trans-factors), giving examples of how cis- and trans-acting factors have been combined to create digital and analogue switches for regulating transcription in response to various signals. Last, we provide examples of how engineered transcription control systems have been used in metabolic engineering and more complicated genetic circuits. While most of our mini-review focuses on the well-characterized bacterium Escherichia coli, we also provide several examples of the use of transcription control engineering in non-model organisms. Similar approaches have been applied outside the bacterial kingdom indicating that the lessons learned from bacterial studies may be generalized for other organisms.

Whole-cell biocatalysts by design

Whole-cell biocatalysts provide unique advantages and have been widely used for the efficient biosynthesis of value-added fine and bulk chemicals, as well as pharmaceutically active ingredients. What is more, advances in synthetic biology and metabolic engineering, together with the rapid development of molecular genetic tools, have brought about a renaissance of whole-cell biocatalysis. These rapid advancements mean that whole-cell biocatalysts can increasingly be rationally designed. Genes of heterologous enzymes or synthetic pathways are increasingly being introduced into microbial hosts, and depending on the complexity of the synthetic pathway or the target products, they can enable the production of value-added chemicals from cheap feedstock. Metabolic engineering and synthetic biology efforts aimed at optimizing the existing microbial cell factories concentrate on improving heterologous pathway flux, precursor supply, and cofactor balance, as well as other aspects of cellular metabolism, to enhance the efficiency of biocatalysts. In the present review, we take a critical look at recent developments in whole-cell biocatalysis, with an emphasis on strategies applied to designing and optimizing the organisms that are increasingly modified for efficient production of chemicals.

Synthetic Riboswitches: From Plug and Pray toward Plug and Play

In synthetic biology, metabolic engineering, and gene therapy, there is a strong demand for orthogonal or externally controlled regulation of gene expression. Here, RNA-based regulatory devices represent a promising emerging alternative to proteins, allowing a fast and direct control of gene expression, as no synthesis of regulatory proteins is required. Besides programmable ribozyme elements controlling mRNA stability, regulatory RNA structures in untranslated regions are highly interesting for engineering approaches. Riboswitches are especially well suited, as they show a modular composition of sensor and response elements, allowing a free combination of different modules in a plug-and-play-like mode. The sensor or aptamer domain specifically interacts with a trigger molecule as a ligand, modulating the activity of the adjacent response domain that controls the expression of the genes located downstream, in most cases at the level of transcription or translation. In this review, we discuss the recent advances and strategies for designing such synthetic riboswitches based on natural or artificial components and readout systems, from trial-and-error approaches to rational design strategies. As the past several years have shown dramatic development in this fascinating field of research, we can give only a limited overview of the basic riboswitch design principles that is far from complete, and we apologize for not being able to consider every successful and interesting approach described in the literature.

Turning It Up to 11: Modular Proteins Amplify RNA Sensors for Sophisticated Circuitry

Ligand-sensing RNA switches can be enhanced using protein-based amplifiers to deliver sophisticated signal-processing genetic circuitry.

Synthetic biology to access and expand nature's chemical diversity

Bacterial genomes encode the biosynthetic potential to produce hundreds of thousands of complex molecules with diverse applications, from medicine to agriculture and materials. Accessing these natural products promises to reinvigorate drug discovery pipelines and provide novel routes to synthesize complex chemicals. The pathways leading to the production of these molecules often comprise dozens of genes spanning large areas of the genome and are controlled by complex regulatory networks with some of the most interesting molecules being produced by non-model organisms. In this Review, we discuss how advances in synthetic biology--including novel DNA construction technologies, the use of genetic parts for the precise control of expression and for synthetic regulatory circuits--and multiplexed genome engineering can be used to optimize the design and synthesis of pathways that produce natural products.

Label-free selection of RNA aptamers for metabolic engineering

RNA aptamers can be assembled into genetic regulatory devices that sense and respond to levels of specific cellular metabolites and thus serve an integral part of designing dynamic control into engineered metabolic pathways. Here, we describe a practical method for generating specific and high affinity aptamers to enable the wider use of in vitro selection and a broader application of aptamers for metabolic engineering. Conventional selection methods involving either radioactive labeling of RNA or the use of label-free methods such as SPR to track aptamer enrichment require resources that are not widely accessible to research groups. We present a label-free selection method that uses small volume spectrophotometers to track RNA enrichment paired with previously characterized affinity chromatography methods. Borrowing techniques used in solid phase peptide synthesis, we present an approach for immobilizing a wide range of metabolites to an amino PEGA matrix. As an illustration, we detail laboratory techniques employed to generate aptamers that bind p-aminophenylalanine, a metabolic precursor for bio-based production of plastics and the pristinamycin family of antibiotics. We focused on the development of methods for ligand immobilization, selection via affinity chromatography, and nucleic acid quantification that can be performed with common laboratory equipment.

Antisense transcription as a tool to tune gene expression

A surprise that has emerged from transcriptomics is the prevalence of genomic antisense transcription, which occurs counter to gene orientation. While frequent, the roles of antisense transcription in regulation are poorly understood. We built a synthetic system in Escherichia coli to study how antisense transcription can change the expression of a gene and tune the response characteristics of a regulatory circuit. We developed a new genetic part that consists of a unidirectional terminator followed by a constitutive antisense promoter and demonstrate that this part represses gene expression proportionally to the antisense promoter strength. Chip‐based oligo synthesis was applied to build a large library of 5,668 terminator–promoter combinations that was used to control the expression of three repressors (PhlF, SrpR, and TarA) in a simple genetic circuit (NOT gate). Using the library, we demonstrate that antisense promoters can be used to tune the threshold of a regulatory circuit without impacting other properties of its response function. Finally, we determined the relative contributions of antisense RNA and transcriptional interference to repressing gene expression and introduce a biophysical model to capture the impact of RNA polymerase collisions on gene repression. This work quantifies the role of antisense transcription in regulatory networks and introduces a new mode to control gene expression that has been previously overlooked in genetic engineering.

Generating Effective Models and Parameters for RNA Genetic Circuits

RNA genetic circuitry is emerging as a powerful tool to control gene expression. However, little work has been done to create a theoretical foundation for RNA circuit design. A prerequisite to this is a quantitative modeling framework that accurately describes the dynamics of RNA circuits. In this work, we develop an ordinary differential equation model of transcriptional RNA genetic circuitry, using an RNA cascade as a test case. We show that parameter sensitivity analysis can be used to design a set of four simple experiments that can be performed in parallel using rapid cell-free transcription-translation (TX-TL) reactions to determine the 13 parameters of the model. The resulting model accurately recapitulates the dynamic behavior of the cascade, and can be easily extended to predict the function of new cascade variants that utilize new elements with limited additional characterization experiments. Interestingly, we show that inconsistencies between model predictions and experiments led to the model-guided discovery of a previously unknown maturation step required for RNA regulator function. We also determine circuit parameters in two different batches of TX-TL, and show that batch-to-batch variation can be attributed to differences in parameters that are directly related to the concentrations of core gene expression machinery. We anticipate the RNA circuit models developed here will inform the creation of computer aided genetic circuit design tools that can incorporate the growing number of RNA regulators, and that the parametrization method will find use in determining functional parameters of a broad array of natural and synthetic regulatory systems.

A renaissance in RNA synthetic biology: new mechanisms, applications and tools for the future

Since our ability to engineer biological systems is directly related to our ability to control gene expression, a central focus of synthetic biology has been to develop programmable genetic regulatory systems. Researchers are increasingly turning to RNA regulators for this task because of their versatility, and the emergence of new powerful RNA design principles. Here we review advances that are transforming the way we use RNAs to engineer biological systems. First, we examine new designable RNA mechanisms that are enabling large libraries of regulators with protein-like dynamic ranges. Next, we review emerging applications, from RNA genetic circuits to molecular diagnostics. Finally, we describe new experimental and computational tools that promise to accelerate our understanding of RNA folding, function and design.

Dynamic metabolic engineering: New strategies for developing responsive cell factories

Metabolic engineering strategies have enabled improvements in yield and titer for a variety of valuable small molecules produced naturally in microorganisms, as well as those produced via heterologous pathways. Typically, the approaches have been focused on up- and downregulation of genes to redistribute steady-state pathway fluxes, but more recently a number of groups have developed strategies for dynamic regulation, which allows rebalancing of fluxes according to changing conditions in the cell or the fermentation medium. This review highlights some of the recently published work related to dynamic metabolic engineering strategies and explores how advances in high-throughput screening and synthetic biology can support development of new dynamic systems. Dynamic gene expression profiles allow trade-offs between growth and production to be better managed and can help avoid build-up of undesired intermediates. The implementation is more complex relative to static control, but advances in screening techniques and DNA synthesis will continue to drive innovation in this field.

A Sense of Balance: Experimental Investigation and Modeling of a Malonyl-CoA Sensor in Escherichia coli

Production of value-added chemicals in microorganisms is regarded as a viable alternative to chemical synthesis. In the past decade, several engineered pathways producing such chemicals, including plant secondary metabolites in microorganisms have been reported; upscaling their production yields, however, was often challenging. Here, we analyze a modular device designed for sensing malonyl-CoA, a common precursor for both fatty acid and flavonoid biosynthesis. The sensor can be used either for high-throughput pathway screening in synthetic biology applications or for introducing a feedback circuit to regulate production of the desired chemical. Here, we used the sensor to compare the performance of several predicted malonyl-CoA-producing pathways, and validated the utility of malonyl-CoA reductase and malonate-CoA transferase for malonyl-CoA biosynthesis. We generated a second-order dynamic linear model describing the relation of the fluorescence generated by the sensor to the biomass of the host cell representing a filter/amplifier with a gain that correlates with the level of induction. We found the time constants describing filter dynamics to be independent of the level of induction but distinctively clustered for each of the production pathways, indicating the robustness of the sensor. Moreover, by monitoring the effect of the copy-number of the production plasmid on the dose–response curve of the sensor, we managed to coarse-tune the level of pathway expression to maximize malonyl-CoA synthesis. In addition, we provide an example of the sensor’s use in analyzing the effect of inducer or substrate concentrations on production levels. The rational development of models describing sensors, supplemented with the power of high-throughput optimization provide a promising potential for engineering feedback loops regulating enzyme levels to maximize productivity yields of synthetic metabolic pathways.

Kinetic folding design of aptazyme-regulated expression devices as riboswitches for metabolic engineering

Recent developments in the fields of synthetic biology and metabolic engineering have opened the doors for the microbial production of biofuels and other valuable organic compounds. There remain, however, significant metabolic hurdles to the production of these compounds in cost-effective quantities. This is due, in part, to mismatches between the metabolic engineer's desire for high yields and the microbe's desire to survive. Many valuable compounds, or the intermediates necessary for their biosynthesis, prove deleterious at the desired production concentrations. One potential solution to these toxicity-related issues is the implementation of nonnative dynamic genetic control mechanisms that sense excessively high concentrations of metabolic intermediates and respond accordingly to alleviate their impact. One potential class of dynamic regulator is the riboswitch: cis-acting RNA elements that regulate the expression of downstream genes based on the presence of an effector molecule. Here, we present combined methods for constructing aptazyme-regulated expression devices (aREDs) through computational cotranscriptional kinetic folding design and experimental validation. These approaches can be used to engineer aREDs within novel genetic contexts for the predictable, dynamic regulation of gene expression in vivo.