Design Of Genetic Circuits Research Articles

BioRxToolbox: A computational framework to streamline genetic circuit design in molecular data communications

ABSTRACT Developments in bioengineering and nanotechnology have ignited the research on biological and molecular communication systems. Despite potential benefits, engineering communication systems to carry data signals using biological messenger molecules and engineered cells is challenging. Diffusing molecules may fall behind their schedule to arrive at the receiver, interfering with symbols of subsequent time slots and distorting the signal. Existing theoretical molecular communication models often focus solely on the characteristics of a communication channel and fail to provide an end-to-end system response since they assume a simple thresholding process for a receiver cell and overlook how the receiver can detect the incoming distorted molecular signal. In this paper, we present a model-based and computational framework called BioRxToolbox for designing diffusion-based and end-to-end molecular communication systems coupled with synthetic genetic circuits. We describe a novel framework to encode information as a sequence of bits, each transmitted from the sender as a burst of molecules, control cellular behavior at the receiver, and minimize cellular signal interference by employing equalization techniques from communication theory. This approach allows the encoding and decoding of data bits efficiently using two different types of molecules that act as the data carrier and the antagonist to cancel out the heavy tail of the former. Here, BioRxToolbox is demonstrated using a biological design and computational simulations for various communication scenarios. This toolbox facilitates automating the choice of communication parameters and identifying the best communication scenarios that can produce efficient cellular signals.

Energy Aware Technology Mapping of Genetic Logic Circuits.

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers─as caused by the additional burden of artificial genetic circuits─shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state model of gene expression, capturing characteristics like energy dissipation─which we link to the entropy production rate─and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energetic costs of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

Energy Aware Technology Mapping of Genetic Logic Circuits.

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers -as caused by the additional burden of artificial genetic circuits- shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state (NESS) model of gene expression, capturing characteristics like energy dissipation -which we link to the entropy production rate- and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energy efficiency of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

Synonymous codon substitutions modulate transcription and translation of a divergent upstream gene by modulating antisense RNA production

Synonymous codons were originally viewed as interchangeable, with no phenotypic consequences. However, substantial evidence has now demonstrated that synonymous substitutions can perturb a variety of gene expression and protein homeostasis mechanisms, including translational efficiency, translational fidelity, and cotranslational folding of the encoded protein. To date, most studies of synonymous codon-derived perturbations have focused on effects within a single gene. Here, we show that synonymous codon substitutions made far within the coding sequence of Escherichia coli plasmid-encoded chloramphenicol acetyltransferase (cat) can significantly increase expression of the divergent upstream tetracycline resistance gene, tetR. In four out of nine synonymously recoded cat sequences tested, expression of the upstream tetR gene was significantly elevated due to transcription of a long antisense RNA (asRNA) originating from a transcription start site within cat. Surprisingly, transcription of this asRNA readily bypassed the native tet transcriptional repression mechanism. Even more surprisingly, accumulation of the TetR protein correlated with the level of asRNA, rather than total tetR RNA. These effects of synonymous codon substitutions on transcription and translation of a neighboring gene suggest that synonymous codon usage in bacteria may be under selection to both preserve the amino acid sequence of the encoded gene and avoid DNA sequence elements that can significantly perturb expression of neighboring genes. Avoiding such sequences may be especially important in plasmids and prokaryotic genomes, where genes and regulatory elements are often densely packed. Similar considerations may apply to the design of genetic circuits for synthetic biology applications.

Principles and challenges of whole cell microbial biosensors in the food industry.

Whole cell microbial biosensors (WCMB) are mostly genetically modified microorganisms used to detect target molecules as indicators of biological and chemical contaminants as well as in the identification of compounds of interest in the food industry. The specificity and sensitivity of these biosensors are achieved through the design of genetic circuits that make use of genetic sequences such as promoters, terminators, genes encoding regulatory proteins or reporter proteins, among others. Despite the advances of WCMBs for their application, significant challenges are faced, such as cell stability, regulatory restrictions, and the need to optimize response times so that they can be a competitive detection tool in the market. This review explores the technological progress, potential and limitations of WCMBs in the food industry, starting by reviewing the operating principles of biosensors. The importance of selecting appropriate chassis cells and the integration of recognition elements and transducers to maximize their effectiveness in the detection of contaminants and compounds of interest in the food industry is highlighted.

Therapeutic applications of synthetic gene/genetic circuits: a patent review.

A significant limitation of numerous current genetic engineering therapy approaches is their limited control over the strength, timing, or cellular context of their therapeutic effect. Synthetic gene/genetic circuits are synthetic biology approaches that can control the generation, transformation, or depletion of a specific DNA, RNA, or protein and provide precise control over gene expression and cellular behavior. They can be designed to perform logical operations by carefully selecting promoters, repressors, and other genetic components. Patent search was performed in Espacenet, resulting in 38 selected patents with 15 most frequent international classifications. Patent embodiments were categorized into applications for the delivery of therapeutic molecules, treatment of infectious diseases, treatment of cancer, treatment of bleeding, and treatment of metabolic disorders. The logic gates of selected genetic circuits are described to comprehensively demonstrate their therapeutic applications. Synthetic gene circuits can be customized for precise control of therapeutic interventions, leading to personalized therapies that respond specifically to individual patient needs, enhancing treatment efficacy and minimizing side effects. They can be highly sensitive biosensors that provide real-time therapy by accurate monitoring various biomarkers or pathogens and appropriately synthesizing a therapeutic molecule. Synthetic gene circuits may also lead to the development of advanced regenerative therapies and to implantable biodevices that produce on-demand bioactive molecules. However, this technology faces challenges for commercial profitability. The genetic circuit designs need adjustments for specific applications, and may have disadvantages like toxicity from multiple regulators, homologous recombination, context dependency, resource overuse, and environmental variability.

A modular toolkit for environmental Rhodococcus, Gordonia, and Nocardia enables complex metabolic manipulation.

Soil-dwelling Actinomycetes are a diverse and ubiquitous component of the global microbiome but largely lack genetic tools comparable to those available in model species such as Escherichia coli or Pseudomonas putida, posing a fundamental barrier to their characterization and utilization as hosts for biotechnology. To address this, we have developed a modular plasmid assembly framework, along with a series of genetic control elements for the previously genetically intractable Gram-positive environmental isolate Rhodococcus ruber C208, and demonstrate conserved functionality in 11 additional environmental isolates of Rhodococcus, Nocardia, and Gordonia. This toolkit encompasses five Mycobacteriale origins of replication, five broad-host-range antibiotic resistance markers, transcriptional and translational control elements, fluorescent reporters, a tetracycline-inducible system, and a counter-selectable marker. We use this toolkit to interrogate the carotenoid biosynthesis pathway in Rhodococcus erythropolis N9T-4, a weakly carotenogenic environmental isolate and engineer higher pathway flux toward the keto-carotenoid canthaxanthin. This work establishes several new genetic tools for environmental Mycobacteriales and provides a synthetic biology framework to support the design of complex genetic circuits in these species.IMPORTANCESoil-dwelling Actinomycetes, particularly the Mycobacteriales, include both diverse new hosts for sustainable biomanufacturing and emerging opportunistic pathogens. Rhodococcus, Gordonia, and Nocardia are three abundant genera with particularly flexible metabolisms and untapped potential for natural product discovery. Among these, Rhodococcus ruber C208 was shown to degrade polyethylene; Gordonia paraffinivorans can assimilate carbon from solid hydrocarbons; and Nocardia neocaledoniensis (and many other Nocardia spp.) possesses dual isoprenoid biosynthesis pathways. Many species accumulate high levels of carotenoid pigments, indicative of highly active isoprenoid biosynthesis pathways which may be harnessed for fermentation of terpenes and other commodity isoprenoids. Modular genetic toolkits have proven valuable for both fundamental and applied research in model organisms, but such tools are lacking for most Actinomycetes. Our suite of genetic tools and DNA assembly framework were developed for broad functionality and to facilitate rapid prototyping of genetic constructs in these organisms.

Transcriptional regulation of living materials via extracellular electron transfer.

Engineered living materials combine the advantages of biological and synthetic systems by leveraging genetic and metabolic programming to control material-wide properties. Here, we demonstrate that extracellular electron transfer (EET), a microbial respiration process, can serve as a tunable bridge between live cell metabolism and synthetic material properties. In this system, EET flux from Shewanella oneidensis to a copper catalyst controls hydrogel cross-linking via two distinct chemistries to form living synthetic polymer networks. We first demonstrate that synthetic biology-inspired design rules derived from fluorescence parameterization can be applied toward EET-based regulation of polymer network mechanics. We then program transcriptional Boolean logic gates to govern EET gene expression, which enables design of computational polymer networks that mechanically respond to combinations of molecular inputs. Finally, we control fibroblast morphology using EET as a bridge for programmed material properties. Our results demonstrate how rational genetic circuit design can emulate physiological behavior in engineered living materials.

Synthetic bacteria designed using ars operons: a promising solution for arsenic biosensing and bioremediation.

The global concern over arsenic contamination in water due to its natural occurrence and human activities has led to the development of innovative solutions for its detection and remediation. Microbial metabolism and mobilization play crucial roles in the global cycle of arsenic. Many microbial arsenic-resistance systems, especially the ars operons, prevalent in bacterial plasmids and genomes, play vital roles in arsenic resistance and are utilized as templates for designing synthetic bacteria. This review novelty focuses on the use of these tailored bacteria, engineered with ars operons, for arsenic biosensing and bioremediation. We discuss the advantages and disadvantages of using synthetic bacteria in arsenic pollution treatment. We highlight the importance of genetic circuit design, reporter development, and chassis cell optimization to improve biosensors' performance. Bacterial arsenic resistances involving several processes, such as uptake, transformation, and methylation, engineered in customized bacteria have been summarized for arsenic bioaccumulation, detoxification, and biosorption. In this review, we present recent insights on the use of synthetic bacteria designed with ars operons for developing tailored bacteria for controlling arsenic pollution, offering a promising avenue for future research and application in environmental protection.

Understanding and computational design of genetic circuits of metabolic networks.

The expression of metabolic proteins is controlled by genetic circuits, matching metabolic demands and changing environmental conditions. Ideally, this regulation brings about a competitive level of metabolic fitness. Understanding how cells can achieve a robust (close-to-optimal) functioning of metabolism by appropriate control of gene expression aids synthetic biology by providing design criteria of synthetic circuits for biotechnological purposes. It also extends our understanding of the designs of genetic circuitry found in nature such as metabolite control of transcription factor activity, promoter architectures and transcription factor dependencies, and operon composition (in bacteria). Here, we review, explain and illustrate an approach that allows for the inference and design of genetic circuitry that steers metabolic networks to achieve a maximal flux per unit invested protein across dynamic conditions. We discuss how this approach and its understanding can be used to rationalize Escherichia coli's strategy to regulate the expression of its ribosomes and infer the design of circuitry controlling gene expression of amino-acid biosynthesis enzymes. The inferred regulation indeed resembles E. coli's circuits, suggesting that these have evolved to maximize amino-acid production fluxes per unit invested protein. We end by an outlook of the use of this approach in metabolic engineering applications.

Effects of mRNA conformational switching on translational noise in gene circuits.

Intragenic translational heterogeneity describes the variation in translation at the level of transcripts for an individual gene. A factor that contributes to this source of variation is the mRNA structure. Both the composition of the thermodynamic ensemble, i.e., the stationary distribution of mRNA structures, and the switching dynamics between those play a role. The effect of the switching dynamics on intragenic translational heterogeneity remains poorly understood. We present a stochastic translation model that accounts for mRNA structure switching and is derived from a Markov model via approximate stochastic filtering. We assess the approximation on various timescales and provide a method to quantify how mRNA structure dynamics contributes to translational heterogeneity. With our approach, we allow quantitative information on mRNA switching from biophysical experiments or coarse-grain molecular dynamics simulations of mRNA structures to be included in gene regulatory chemical reaction network models without an increase in the number of species. Thereby, our model bridges a gap between mRNA structure kinetics and gene expression models, which we hope will further improve our understanding of gene regulatory networks and facilitate genetic circuit design.

Enzymatic Assembly of Small Synthetic Genes with Repetitive Elements.

Gene synthesis efficiency has greatly improved in recent years but is limited when it comes to repetitive sequences, which results in synthesis failure or delays by DNA synthesis vendors. This represents a major obstacle for the development of synthetic biology since repetitive elements are increasingly being used in the design of genetic circuits and design of biomolecular nanostructures. Here, we describe a method for the assembly of small synthetic genes with repetitive elements: First, a gene of interest is split in silico into small synthons of up to 80 base pairs flanked by Golden-Gate-compatible overhangs. Then, synthons are made by oligo extension and finally assembled into a synthetic gene by Golden Gate Assembly. We demonstrate the method by constructing eight challenging genes with repetitive elements, e.g., multiple repeats of RNA aptamers and RNA origami scaffolds with multiple identical aptamers. The genes range in size from 133 to 456 base pairs and are assembled with fidelities of up to 87.5%. The method was developed to facilitate our own specific research but may be of general use for constructing challenging and repetitive genes and, thus, a valuable addition to the molecular cloning toolbox.

An integrative database and its application for plant synthetic biology research

Plant synthetic biology research requires diverse bioparts that facilitate the redesign and construction of new-to-nature biological devices or systems in plants. Limited by few well-characterized bioparts for plant chassis, the development of plant synthetic biology lags behind that of its microbial counterpart. Here, we constructed a web-based Plant Synthetic BioDatabase (PSBD), which currently categorizes 1677 catalytic bioparts and 384 regulatory elements and provides information on 309 species and 850 chemicals. Online bioinformatics tools including local BLAST, chem similarity, phylogenetic analysis, and visual strength are provided to assist with the rational design of genetic circuits for manipulation of gene expression in planta. We demonstrated the utility of the PSBD by functionally characterizing taxadiene synthase 2 and its quantitative regulation in tobacco leaves. More powerful synthetic devices were then assembled to amplify the transcriptional signals, enabling enhanced expression of flavivirus non-structure 1 proteins in plants. The PSBD is expected to be an integrative and user-centered platform that provides a one-stop service for diverse applications in plant synthetic biology research.

Mechanism-based and data-driven modeling in cell-free synthetic biology.

Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.

A Computational Modeling Approach for the Design of Genetic Control Systems that Respond to Transcriptional Activity.

Recent progress in synthetic biology has enabled the design of complex genetic circuits that interface with innate cellular functions, such as gene transcription, and control user-defined outputs. Implementing these genetic networks in mammalian cells, however, is a cumbersome process that requires several steps of optimization and benefits from the use of predictive modeling. Combining deterministic mathematical models with software-based numerical computing platforms allows researchers to quickly design, evaluate, and optimize multiple circuit topologies to establish experimental constraints that generate the desired control systems. In this chapter, we present a systematic approach based on predictive mathematical modeling to guide the design and construction of gene activity-based sensors. This approach enables user-driven circuit optimization through iterations of sensitivity analyses and parameter scans, providing a universal method to engineer sense and respond cells for diverse applications.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for High-Throughput Profiling of Dose-Response Curves of Metabolite Biosensors.

Metabolite biosensors, through which the intracellular metabolite concentrations could be converted to changes in gene expression, are widely used in a variety of applications according to the different output signals. However, it remains challenging to fine-tune the dose-response relationships of biosensors to meet the needs of various scenarios. On the other hand, the short read length of next-generation sequencing (NGS) has greatly limited the design capability of sequence libraries. To address these issues, we describe a DNA trackable assembly method, coupled with fluorescence-activated cell sorting and NGS (Sort-Seq), to achieve the characterization of dose-response curves in a massively parallel manner. As a proof of the concept, we constructed a malonyl-CoA biosensor library containing 5184 combinations with six levels of transcription factor dosage, four different operator positions, and 216 possible upstream enhancer sequence (UAS) designs in Saccharomyces cerevisiae BY4700. By using Sort-Seq and machine learning approach, we obtained comprehensive dose-response relationships of the combinatorial sequence space. Therefore, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

A Genetic Circuit Design for Targeted Viral RNA Degradation.

Advances in synthetic biology have led to the design of biological parts that can be assembled in different ways to perform specific functions. For example, genetic circuits can be designed to execute specific therapeutic functions, including gene therapy or targeted detection and the destruction of invading viruses. Viral infections are difficult to manage through drug treatment. Due to their high mutation rates and their ability to hijack the host's ribosomes to make viral proteins, very few therapeutic options are available. One approach to addressing this problem is to disrupt the process of converting viral RNA into proteins, thereby disrupting the mechanism for assembling new viral particles that could infect other cells. This can be done by ensuring precise control over the abundance of viral RNA (vRNA) inside host cells by designing biological circuits to target vRNA for degradation. RNA-binding proteins (RBPs) have become important biological devices in regulating RNA processing. Incorporating naturally upregulated RBPs into a gene circuit could be advantageous because such a circuit could mimic the natural pathway for RNA degradation. This review highlights the process of viral RNA degradation and different approaches to designing genetic circuits. We also provide a customizable template for designing genetic circuits that utilize RBPs as transcription activators for viral RNA degradation, with the overall goal of taking advantage of the natural functions of RBPs in host cells to activate targeted viral RNA degradation.

Evolutionary Algorithms in a Bacterial Consortium of Synthetic Bacteria

At present, synthetic biology applications are based on the programming of synthetic bacteria with custom-designed genetic circuits through the application of a top-down strategy. These genetic circuits are the programs that implement a certain algorithm, the bacterium being the agent or shell responsible for the execution of the program in a given environment. In this work, we study the possibility that instead of programming synthesized bacteria through a custom-designed genetic circuit, it is the circuit itself which emerges as a result of the evolution simulated through an evolutionary algorithm. This study is conducted by performing in silico experiments in a community composed of synthetic bacteria in which one species or strain behaves as pathogenic bacteria against the rest of the non-pathogenic bacteria that are also part of the bacterial consortium. The goal is the eradication of the pathogenic strain through the evolutionary programming of the agents or synthetic bacteria. The results obtained suggest the plausibility of the evolutionary design of the appropriate genetic circuit resulting from the application of a bottom-up strategy and therefore the experimental feasibility of the evolutionary programming of synthetic bacteria.

Creation of Architecturally Minimal Transcriptionally Activating Riboswitches Responsive to Theophylline Reveals an Unconventional Design Strategy.

Riboswitches are noncoding RNA switches that are largely utilized in bacteria and play a significant role in synthetic biology. Nonetheless, their natural counterparts possess lengthy sequences and intricate structures, posing challenges for their modular integration into complex gene circuits. Consequently, it is imperative to develop simplified synthetic riboswitches that can be effortlessly incorporated into gene circuits. The conventional approach to generate synthetic riboswitches entails tedious library construction and extensive screening, which frequently yields suboptimal performance. To overcome this obstacle, alternative methods are urgently needed. In this study, we created a novel approach to designing a diverse set of transcription-activating riboswitches that exhibit high performance and broad compatibility. The strategy involved starting with a synthetic theophylline RNA aptamer and designing an expression platform that forms a transcriptional terminator in its inactive state but switches to an antiterminator when it is activated. Several sequences were designed, constructed, and subjected to virtual screening, resulting in the identification of two transcription-activating riboswitches. These riboswitches were then engineered to reduce the basal leakage and increase the activation level through extending the hairpin region using a screened random sequence. These architecturally minimal synthetic riboswitches were highly adapted to different constitutive promoters in a modular manner, generating a differentially responsive output to theophylline. As a proof-of-principle, the synthetic riboswitches were applied to rewire a synthetic quorum-sensing circuit (QSC). The reprogrammed QSC successfully modulated the temporal responsive profile against the activation. This strategy is expected to expand the variety of high-performance riboswitches that are responsive to different ligands, thereby further facilitating the design of complex genetic circuits.

Synthetic genetic circuits to uncover the OCT4 trajectories of successful reprogramming of human fibroblasts.

Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. We develop a system that accurately reports OCT4 protein levels in live cells and use it to reveal the trajectories of OCT4 in successful reprogramming. Our system comprises a synthetic genetic circuit that leverages noise to generate a wide range of OCT4 trajectories and a microRNA targeting endogenous OCT4 to set total cellular OCT4 protein levels. By fusing OCT4 to a fluorescent protein, we are able to track OCT4 trajectories with clonal resolution via live-cell imaging. We discover that a supraphysiological, stable OCT4 level is required, but not sufficient, for efficient iPSC colony formation. Our synthetic genetic circuit design and high-throughput live-imaging pipeline are generalizable for investigating TF dynamics for other cell fate programming applications.