Synthetic Genetic Circuits Research Articles

Microfluidic approaches for synthetic gene circuits’ construction and analysis

BackgroundMicrofluidic systems have advantages such as a high throughput, small reaction volume, and precise control of the cellular position and environment. These advantages have allowed microfluidics to be widely used in several fields of synthetic biology in recent years.ResultsIn this article, we reviewed the microfluidic‐based methods for synthetic biology from two aspects: the construction of synthetic gene circuits and the analysis of synthetic gene systems. We used some examples to illuminate the progresses and challenges in the steps of synthetic gene circuits construction and approaches of gene expression analysis with microfluidic systems.ConclusionComparing to traditional methods, microfluidic tools promise great advantages in the synthetic genetic circuit building and analysis process. Moreover, new microfluidic systems together with the mathematical modeling of synthetic circuits or consortiums are desirable to perform complex genetic circuit construction and understand the natural gene regulation in cells and population interactions better.

Synthetic and systems biology principles in the design of programmable oncolytic virus immunotherapies for glioblastoma.

Oncolytic viruses (OVs) are a class of immunotherapeutic agents with promising preclinical results for the treatment of glioblastoma (GBM) but have shown limited success in recent clinical trials. Advanced bioengineering principles from disciplines such as synthetic and systems biology are needed to overcome the current challenges faced in developing effective OV-based immunotherapies for GBMs, including off-target effects and poor clinical responses. Synthetic biology is an emerging field that focuses on the development of synthetic DNA constructs that encode networks of genes and proteins (synthetic genetic circuits) to perform novel functions, whereas systems biology is an analytical framework that enables the study of complex interactions between host pathways and these synthetic genetic circuits. In this review, the authors summarize synthetic and systems biology concepts for developing programmable, logic-based OVs to treat GBMs. Programmable OVs can increase selectivity for tumor cells and enhance the local immunological response using synthetic genetic circuits. The authors discuss key principles for developing programmable OV-based immunotherapies, including how to 1) select an appropriate chassis, a vector that carries a synthetic genetic circuit, and 2) design a synthetic genetic circuit that can be programmed to sense key signals in the GBM microenvironment and trigger release of a therapeutic payload. To illustrate these principles, some original laboratory data are included, highlighting the need for systems biology studies, as well as some preliminary network analyses in preparation for synthetic biology applications. Examples from the literature of state-of-the-art synthetic genetic circuits that can be packaged into leading candidate OV chassis are also surveyed and discussed.

Flapjack: Data Management and Analysis for Genetic Circuit Characterization.

Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different laboratories and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parametrize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting, and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated with circuit part composition via SBOL (Synthetic Biology Open Language). We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.

A Parallel Approach to Perform Threshold Value and Propagation Delay Analyses of Genetic Logic Circuit Models.

Experiments with synthetic genetic logic circuits can be time-consuming and expensive. Accordingly, advances in the field of computer-aided design and simulation of genetic circuits have reduced the cost and time required for experimentation. D-VASim is the first genetic circuit simulation tool that allows users to interact with the model during run-time. In contrast to electronic circuits, genetic circuits have different threshold values for different circuits, which need to be estimated prior to simulation. D-VASim allows the user to perform threshold concentration and propagation delay analysis before simulating the circuit. The algorithm currently used in D-VASim has considerable scope for improvements. Thus, we propose a parallel implementation of the algorithm, significantly faster by up to 16 times. In adddition, we improve the algorithm for consistent runtimes across multiple simulation runs under the same parameter settings, reducing the worst-case standard deviation in runtime from 6.637 to 1.841. Our algorithm also estimates the threshold value more accurately, as evident from experimentation for long runtimes. With these modifications, the utility of D-VASim as a virtual laboratory environment has been significantly enhanced.

An Automated Model Test System for Systematic Development and Improvement of Gene Expression Models.

Gene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions remains a significant challenge. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce model capacity, a new information theoretic metric for correct cross-data-set comparisons. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We then applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.

Quantitative and Predictive Genetic Parts for Plant Synthetic Biology.

Plant synthetic biology aims to harness the natural abilities of plants and to turn them to new purposes. A primary goal of plant synthetic biology is to produce predictable and programmable genetic circuits from simple regulatory elements and well-characterized genetic components. The number of available DNA parts for plants is increasing, and the methods for rapid quantitative characterization are being developed, but the field of plant synthetic biology is still in its early stages. We here describe methods used to describe the quantitative properties of genetic components needed for plant synthetic biology. Once the quantitative properties and transfer function of a variety of genetic parts are known, computers can select the optimal components to assemble into functional devices, such as toggle switches and positive feedback circuits. However, while the variety of circuits and traits that can be put into plants are limitless, doing synthetic biology in plants poses unique challenges. Plants are composed of differentiated cells and tissues, each representing potentially unique regulatory or developmental contexts to introduced synthetic genetic circuits. Further, plants have evolved to be highly sensitive to environmental influences, such as light or temperature, any of which can affect the quantitative function of individual parts or whole circuits. Measuring the function of plant components within the context of a plant cell and, ideally, in a living plant, will be essential to using these components in gene circuits with predictable function. Mathematical modeling will be needed to account for the variety of contexts a genetic part will experience in different plant tissues or environments. With such understanding in hand, it may be possible to redesign plant traits to serve human and environmental needs.

Novel Tunable Spatio-Temporal Patterns From a Simple Genetic Oscillator Circuit.

Multicellularity, the coordinated collective behavior of cell populations, gives rise to the emergence of self-organized phenomena at many different spatio-temporal scales. At the genetic scale, oscillators are ubiquitous in regulation of multicellular systems, including during their development and regeneration. Synthetic biologists have successfully created simple synthetic genetic circuits that produce oscillations in single cells. Studying and engineering synthetic oscillators in a multicellular chassis can therefore give us valuable insights into how simple genetic circuits can encode complex multicellular behaviors at different scales. Here we develop a study of the coupling between the repressilator synthetic genetic ring oscillator and constraints on cell growth in colonies. We show in silico how mechanical constraints generate characteristic patterns of growth rate inhomogeneity in growing cell colonies. Next, we develop a simple one-dimensional model which predicts that coupling the repressilator to this pattern of growth rate via protein dilution generates traveling waves of gene expression. We show that the dynamics of these spatio-temporal patterns are determined by two parameters; the protein degradation and maximum expression rates of the repressors. We derive simple relations between these parameters and the key characteristics of the traveling wave patterns: firstly, wave speed is determined by protein degradation and secondly, wavelength is determined by maximum gene expression rate. Our analytical predictions and numerical results were in close quantitative agreement with detailed individual based simulations of growing cell colonies. Confirming published experimental results we also found that static ring patterns occur when protein stability is high. Our results show that this pattern can be induced simply by growth rate dilution and does not require transition to stationary phase as previously suggested. Our method generalizes easily to other genetic circuit architectures thus providing a framework for multi-scale rational design of spatio-temporal patterns from genetic circuits. We use this method to generate testable predictions for the synthetic biology design-build-test-learn cycle.

Long-Term Rewritable Report and Recording of Environmental Stimuli in Engineered Bacterial Populations.

DNA writing (living sensing recorders) based whole-cell biosensors can capture transient signals and then convert them into readable genomic DNA changes. The primitive signals can be easily obtained by sequencing technology or analysis of protein activity (such as fluorescent protein). However, the functions of the current living sensing recorders still need to be expanded, and the difficulty of rewriting in complex biological environments has further limited their applications. In this study, we designed a long-term rewritable recording system using a CRISPR base editor-based synthetic genetic circuit, named CRISPR-istop. This system can convert stimuli into changes in the fluorescence intensity (reporter) and single-base mutations in genomic DNA (recording). Furthermore, we updated the biological circuit through the strategy of coupling the single-base mutation (record site) and the loss-of-function of the targeted protein (translation stopped by stop codon introduction), and we can remove edited bacteria from a population through selective sweeps upon applying a selective pressure. It successfully conducted the rewritable reporter and recording of the nutrient arabinose and pollutant arsenite with two rounds of continuous operation (10 passages/round, 12 h/passage). These observations indicated that the CRISPR-istop system can report and record stimuli over time; moreover, the recording can be manually erased and rewritten as needed. This method has great potential to be extended to more complicated recording systems to execute sophisticated tasks in inaccessible environments for synthetic biology and biomedical applications, such as monitoring disease-relevant physiological markers or other molecules.

NVD-BM-mediated genetic biosensor triggers accumulation of 7-dehydrocholesterol and inhibits melanoma via Akt1/NF-ĸB signaling.

Aberrant activation of the cholesterol biosynthesis supports tumor cell growth. In recent years, significant progress has been made by targeting rate-limiting enzymes in cholesterol biosynthesis pathways to prevent carcinogenesis. However, precise mechanisms behind cholesterol degradation in cancer cells have not been comprehensively investigated. Here, we report that codon optimization of the orthologous cholesterol 7-desaturase, NVD-BM from Bombyx mori, significantly slowed melanoma cell proliferation and migration, and inhibited cancer cell engraftment in nude mice, by converting cholesterol to toxic 7-dehydrocholesterol. Based on these observations, we established a synthetic genetic circuit to induce melanoma cell regression by sensing tumor specific signals in melanoma cells. The dual-input signals, RELA proto-oncogene (RELA) and signal transducer and activator of transcription 1 (STAT1), activated NVD-BM expression and repressed melanoma cell proliferation and migration. Mechanically, we observed that NVD-BM decreased Akt1-ser473 phosphorylation and inhibited cytoplasmic RELA translocation. Taken together, NVD-BM was identified as a tumor suppressor in malignant melanoma, and we established a dual-input biosensor to promote cancer cell regression, via Akt1/NF-κB signaling. Our results demonstrate the potential therapeutic effects of cholesterol 7-desaturase in melanoma metabolism, and provides insights for genetic circuits targeting 7-dehydrocholesterol accumulation in tumors.

Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories.

SummaryTranscription 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.

How IoT Can Integrate Biotechnological Approaches for City Applications—Review of Recent Advancements, Issues, and Perspectives

There are a number of significant changes taking place in modern city development and most of them are based on the number of recent technological progress. This paper provides a review and analysis of recent approaches of biotechnology that can find a place in today’s cities and discusses how those technologies can be integrated into a city’s Internet of Things (IoT). Firstly, several biotechnologies that focus on rain gardens, urban vertical farming systems, and city photobioreactors are discussed in the context of their integration in a city’s IoT. The next possible application of biofuel cells to the sensor network’s energy supply is discussed. It is shown that such devices can influence the low-power sensor network structure as an additional energy source for transmitters. This paper shows the possibility of bioelectrochemical biosensor applications, discusses self-powered biosensors, and shows that such a system can be widely applied to rainwater monitoring in rain gardens and green streets. Significant attention is paid to recent approaches in synthetic biology. Both cell-based biosensors and bioactuators with synthetic genetic circuits are discussed. The development of cell-based biosensors can significantly enhance the sensing possibilities of a city’s IoT. We show the possible ways to develop cyber-physical systems (CPSs) with the systems mentioned above. Aspects of data handling for the discussed biotechnologies and the methods of intelligent systems, including those that are machine learning-based, applied to the IoT in a city are presented.

Point-of-Care Devices to Detect Zika and Other Emerging Viruses.

Rapid diagnostic tests (point-of-care devices) are critical components of informed patient care and public health monitoring (surveillance applications). We propose that among the many rapid diagnostics platforms that have been tested or are in development, lateral flow immunoassays and synthetic biology-based diagnostics (including CRISPR-based diagnostics) represent the best overall options given their ease of use, scalability for manufacturing, sensitivity, and specificity. This review describes the identification of lateral flow immunoassay monoclonal antibody pairs that detect and distinguish between closely related pathogens and that are used in combination with functionalized multicolored nanoparticles and computational methods to deconvolute data. We also highlight the promise of synthetic biology-based diagnostic tests, which use synthetic genetic circuits that activate upon recognition of a pathogen-associated nucleic acid sequence, and discuss how the combined or parallel use of lateral flow immunoassays and synthetic biology tools may represent the future of scalable rapid diagnostics.

Alternative Strategies for Microbial Remediation of Pollutants via Synthetic Biology.

Continuous contamination of the environment with xenobiotics and related recalcitrant compounds has emerged as a serious pollution threat. Bioremediation is the key to eliminating persistent contaminants from the environment. Traditional bioremediation processes show limitations, therefore it is necessary to discover new bioremediation technologies for better results. In this review we provide an outlook of alternative strategies for bioremediation via synthetic biology, including exploring the prerequisites for analysis of research data for developing synthetic biological models of microbial bioremediation. Moreover, cell coordination in synthetic microbial community, cell signaling, and quorum sensing as engineered for enhanced bioremediation strategies are described, along with promising gene editing tools for obtaining the host with target gene sequences responsible for the degradation of recalcitrant compounds. The synthetic genetic circuit and two-component regulatory system (TCRS)-based microbial biosensors for detection and bioremediation are also briefly explained. These developments are expected to increase the efficiency of bioremediation strategies for best results.

Tunable genetic devices through simultaneous control of transcription and translation

Synthetic genetic circuits allow us to modify the behavior of living cells. However, changes in environmental conditions and unforeseen interactions with the host cell can cause deviations from a desired function, resulting in the need for time-consuming reassembly to fix these issues. Here, we use a regulatory motif that controls transcription and translation to create genetic devices whose response functions can be dynamically tuned. This allows us, after construction, to shift the on and off states of a sensor by 4.5- and 28-fold, respectively, and modify genetic NOT and NOR logic gates to allow their transitions between states to be varied over a >6-fold range. In all cases, tuning leads to trade-offs in the fold-change and the ability to distinguish cellular states. This work lays the foundation for adaptive genetic circuits that can be tuned after their physical assembly to maintain functionality across diverse environments and design contexts.

A Synthetic Genetic Circuit Enables Precise Quantification of Direct Repeat Deletion in Bacteria.

Quantification of the rate of direct repeat deletion (DRD) is an important aspect in the research of DNA rearrangement. The widely used tetracycline selection method usually introduces antibiotic pressure to the tested organism, which may interfere with the DRD process. Also the length of repeat arm (LRA) is limited by the length of the TetR coding sequence. On the basis of the fluorescent microscopy and high-throughput imaging processing, here we developed a two-module genetic circuit, termed TFDEC (which stands for three-color fluorescence-based deletion event counter), to quantify the DRD rate under neutral conditions. DRD events were determined from the state of a three-state fluorescent logic gate constructed through coupling of an OR gate and an AND gate. TFDEC was applied in Pseudomonas aeruginosa, and we found that the DRD rate was RecA-dependent for long repeat arms (>500 bp) and RecA-independent for short repeat arms (<500 bp), which was consistent with the case in Escherichia coli. In addition, the increase of DRD rate followed an S-shaped curve with the increase of LRA, while treating cells with ciprofloxacin did not change the LRA-dependence of DRD. We also detected a significant increased DRD rate for long repeat arms in the uvrD (8-fold) and radA (4-fold) mutants. Our results show that the TFDEC method could be used as a complement tool for quantification of the DRD rate in the future.

Blue Light-Directed Cell Migration, Aggregation, and Patterning

Bacterial motility is related to many cellular activities, such as cell migration, aggregation, and biofilm formations. The ability to control motility and direct the bacteria to certain location could be used to guide the bacteria in applications such as seeking for and killing pathogen, forming various population-level patterns, and delivering of drugs and vaccines. Currently, bacteria motility is mainly controlled by chemotaxis (prescribed chemical stimuli), which needs physical contact with the chemical inducer. This lacks the flexibility for pattern formation as it has limited spatial control.To overcome the limitations, we developed blue light-regulated synthetic genetic circuit to control bacterial directional motility, by taking the advantage that light stimulus can be delivered to cells in different patterns with precise spatial control. The circuit developed enables programmed Escherichia coli cells to increase directional motility and move away from the blue light, i.e., that negative phototaxis is utilized. This further allows the control of the cells to form aggregation with different patterns. Further, we showed that the circuit can be used to separate two different strains. The demonstrated ability of blue light-controllable gene circuits to regulate a CheZ expression could give researchers more means to control bacterial motility and pattern formation.

The Impact of Global Transcriptional Regulation on Bacterial Gene Order.

SummaryBacterial gene expression depends on the allocation of limited transcriptional resources provided a particular growth rate and growth condition. Early studies in a few genes suggested this global regulation to generate a unifying hyperbolic expression pattern. Here, we developed a large-scale method that generalizes these experiments to quantify the response to growth of over 700 genes that a priori do not exhibit any specific control. We distinguish a core subset following a promoter-specific hyperbolic response. Within this group, we sort genes with regard to their responsiveness to the global regulatory program to show that those with a particularly sensitive linear response are located near the origin of replication. We then find evidence that this genomic architecture is biologically significant by examining position conservation of E. coli genes in 100 bacteria. The response to the transcriptional resources of the cell results in an additional feature contributing to bacterial genome organization.

Chromosome-free bacterial cells are safe and programmable platforms for synthetic biology

A type of chromosome-free cell called SimCells (simple cells) has been generated from Escherichia coli, Pseudomonas putida, and Ralstonia eutropha. The removal of the native chromosomes of these bacteria was achieved by double-stranded breaks made by heterologous I-CeuI endonuclease and the degradation activity of endogenous nucleases. We have shown that the cellular machinery remained functional in these chromosome-free SimCells and was able to process various genetic circuits. This includes the glycolysis pathway (composed of 10 genes) and inducible genetic circuits. It was found that the glycolysis pathway significantly extended longevity of SimCells due to its ability to regenerate ATP and NADH/NADPH. The SimCells were able to continuously express synthetic genetic circuits for 10 d after chromosome removal. As a proof of principle, we demonstrated that SimCells can be used as a safe agent (as they cannot replicate) for bacterial therapy. SimCells were used to synthesize catechol (a potent anticancer drug) from salicylic acid to inhibit lung, brain, and soft-tissue cancer cells. SimCells represent a simplified synthetic biology chassis that can be programmed to manufacture and deliver products safely without interference from the host genome.

Synthetic mRNA-Based Systems in Mammalian Cells.

Living organisms are programmed to perform multiple functions by sensing intra- and extra-cellular environments and by controlling gene expressions. Synthetic biologists aim to program cells by mimicking, designing, and constructing genetic circuits. Synthetic mRNA-based genetic switches and circuits have attracted attention for future therapeutic applications because of their safety and functional diversity. Here, the mRNA-based switches and circuits that detect specific microRNAs or proteins expressed in a target cell to control transgene expression and cell fate are reviewed. Future perspectives of artificial RNA systems for cell engineering will also be addressed.

Data-driven statistical modeling of the emergent behavior of biohybrid microrobots.

Multi-agent biohybrid microrobotic systems, owing to their small size and distributed nature, offer powerful solutions to challenges in biomedicine, bioremediation, and biosensing. Synthetic biology enables programmed emergent behaviors in the biotic component of biohybrid machines, expounding vast potential benefits for building biohybrid swarms with sophisticated control schemes. The design of synthetic genetic circuits tailored toward specific performance characteristics is an iterative process that relies on experimental characterization of spatially homogeneous engineered cell suspensions. However, biohybrid systems often distribute heterogeneously in complex environments, which will alter circuit performance. Thus, there is a critically unmet need for simple predictive models that describe emergent behaviors of biohybrid systems to inform synthetic gene circuit design. Here, we report a data-driven statistical model for computationally efficient recapitulation of the motility dynamics of two types of Escherichia coli bacteria-based biohybrid swarms—NanoBEADS and BacteriaBots. The statistical model was coupled with a computational model of cooperative gene expression, known as quorum sensing (QS). We determined differences in timescales for programmed emergent behavior in BacteriaBots and NanoBEADS swarms, using bacteria as a comparative baseline. We show that agent localization and genetic circuit sensitivity strongly influence the timeframe and the robustness of the emergent behavior in both systems. Finally, we use our model to design a QS-based decentralized control scheme wherein agents make independent decisions based on their interaction with other agents and the local environment. We show that synergistic integration of synthetic biology and predictive modeling is requisite for the efficient development of biohybrid systems with robust emergent behaviors.