Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems

Tuning Ultrasensitivity in Genetic Logic Gates Using Antisense RNA Feedback

Inverting genetic logic gates fueled by transcriptional repression is an established building block in genetic circuit design. Often, the gates' dose-response curves require large changes in dose to transition between logic ON and OFF states, potentially leading to logically indeterminate intermediate states when gates are connected. Additionally, leakage in the OFF state is a general concern, especially at the output stages of a circuit. This study explores the potential to improve inverting logic gates through the introduction of an additional sequestration reaction between the input and output chemical species of the gate. As a mechanism of study, we employ antisense RNAs (asRNAs) expressed alongside the mRNA (mRNA) of the logic gate within single transcripts. These asRNAs target mRNAs of adjacent gates and create additional feedback that supports the protein-mediated repression of the gates. Numerical and symbolic analysis indicates that the sequestration steepens the gate's dose-response curve, reduces leakage, and can potentially be used to adjust the location of logic transition. To leverage these effects, we demonstrate how design parameters can be tuned to obtain desired dose-response curves and outline how arbitrary combinational circuits can be assembled using the improved gates. Finally, we also discuss an implementation using split transcripts.

Customization of Ethylene Glycol (EG)-Induced BmoR-Based Biosensor for the Directed Evolution of PET Degrading Enzymes

The immense volume of plastic waste poses continuous threats to the ecosystem and human health. Despite substantial efforts to enhance the catalytic activity, robustness, expression, and tolerance of plastic-degrading enzymes, the lack of high-throughput screening (HTS) tools hinders efficient enzyme engineering for industrial applications. Herein, we develop a novel fluorescence-based HTS tool for evolving polyethylene terephthalate (PET) degrading enzymes by constructing an engineered BmoR-based biosensor targeting the PET breakdown product, ethylene glycol (EG). The EG-responsive biosensors, with notably enhanced dynamic range and operation range, are customized by fluorescence-activated cell sorting (FACS)-assisted transcription factor engineering. The ingeniously designed SUMO-MHETase-FastPETase (SMF) chimera successfully addresses the functional soluble expression of MHETase in Escherichia coli and mitigates the inhibitory effect of mono-(2-hydroxyethyl) terephthalic acid (MHET) intermediate commonly observed with PETase alone. The obtained SMM3F mutant demonstrates 1.59-fold higher terephthalic acid (TPA) production, with a 1.18-fold decrease in Km, a 1.29-fold increase in Vmax, and a 1.52-fold increase in kcat/Km, indicating stronger affinity and catalytic activity toward MHET. Furthermore, the SMM3F crude extract depolymerizes 5 g L-1 bis-(2-hydroxyethyl) terephthalic acid (BHET) into TPA completely at 37 °C within 10 h, which is then directedly converted into value-added protocatechuic acid (PCA) (997.16 mg L-1) and gallic acid (GA) (411.69 mg L-1) at 30 °C, establishing an eco-friendly 'PET-BHET-MHET-TPA-PCA-GA' upcycling route. This study provides a valuable HTS tool for screening large-scale PET and MHET hydrolases candidates or metagenomic libraries, and propels the complete biodegradation and upcycling of PET waste.

Gene syntaxes modulate gene expression and circuit behavior on plasmids

Achieving consistent and predictable gene expression from plasmids remains challenging. While much attention has focused on intra-genetic elements like promoters and ribosomal binding sites, the spatial arrangement of genes within plasmids-referred to as gene syntax-also plays a crucial role in shaping gene expression dynamics. This study addresses the largely overlooked impact of gene syntaxes on gene expression variability and accuracy. Utilizing a dual-fluorescent protein system, we systematically investigated how different gene orientations and orders affect expression profiles including mean levels, relative expression ratios, and cell-to-cell variations. We found that arbitrary gene placement on a plasmid can cause significantly different expression means and ratios. Genes aligned in the same direction as a plasmid's origin of replication (Ori) typically exhibit higher expression levels; adjacent genes in the divergent orientation tend to suppress each other's expression; altering gene order without changing orientation can yield varied expression. Despite unchanged total cell-to-cell variation across different syntaxes, gene syntaxes can also influence intrinsic and extrinsic noise. Interestingly, cell-to-cell variation appears to depend on the reporter proteins, with RFP consistently showing higher variation than GFP. Moreover, the effects of gene syntax can propagate to downstream circuits, strongly affecting the performance of incoherent feedforward loops and contributing to unpredictable outcomes in genetic networks. Our findings reveal that gene syntaxes on plasmids modulate gene expression and circuit behavior, providing valuable insights for the rational design of plasmids and genetic circuits.

Biosensor characterization: formal methods from the perspective of proteome fractions

Many studies characterize transcription factors and other regulatory elements to control gene expression in recombinant systems. However, most lack a formal approach to analyse the inherent and context-specific variations of these regulatory components. This study addresses this gap by establishing a formal framework from which convenient methods are inferred to characterize regulatory circuits. We modelled the bacterial cell as a collection of proteome fractions. Deriving the time-dependent proteome fraction, we obtained a general theorem that describes its change as a function of its expression fraction, a specific portion of the total biosynthesis flux of the cell. Formal deduction reveals that when the proteome fraction reaches a maximum, it becomes equivalent to its expression fraction. This equation enables the reliable measurement of the expression fraction through direct protein quantification. In addition, the experimental data demonstrate a linear correlation between protein production rate and specific growth rate over a significant time period. This suggests a constant expression fraction within this window. For an Isopropyl β- d-1-thiogalactopyranoside (IPTG) biosensor, in five cellular contexts, expression fractions determined by the maximum method and the slope method produced strikingly similar dose-response parameters when independently fit to a Hill function. Furthermore, by analysing two more biosensors, for mercury and cumate detection, we demonstrate that the slope method can be applied effectively to various systems. Therefore, the concepts presented here provide convenient methods for obtaining dose-response parameters, clearly defining the time interval of their validity and offering a framework for interpreting typical biosensor outputs in terms of bacterial physiology. Graphical Abstract Nutrients, transformed by the action of the Nutrient Fixators (purple arrow), are used at a rate of ρ for Protein biosynthesis. The total rate ρ is multiplied by expression fractions fR, fC, fH, and fQ to obtain the biosynthesis rate (black arrows) of each proteome fraction ΦR, ΦC, ΦH, ΦQ, respectively. In a graph of Growth rate versus Proteome Fraction Production Rate, a linear function (green lines) can be observed, and its slope is equal to the expression fraction at each condition.

Fine-Tuning Genetic Circuits via Host Context and RBS Modulation

The choice of organism to host a genetic circuit, the chassis, is often defaulted to model organisms due to their amenability. The chassis-design space has therefore remained underexplored as an engineering variable. In this work, we explored the design space of a genetic toggle switch through variations in nine ribosome binding site compositions and three host contexts, creating 27 circuit variants. Characterization of performance metrics in terms of toggle switch output and host growth dynamics unveils a spectrum of performance profiles from our circuit library. We find that changes in host context cause large shifts in overall performance, while modulating ribosome binding sites leads to more incremental changes. We find that a combined ribosome binding site and host context modulation approach can be used to fine-tune the properties of a toggle switch according to user-defined specifications, such as toward greater signaling strength, inducer sensitivity, or both. Other auxiliary properties, such as inducer tolerance, are also exclusively accessed through changes in the host context. We demonstrate here that exploration of the chassis-design space can offer significant value, reconceptualizing the chassis organism as an important part in the synthetic biologist's toolbox with important implications for the field of synthetic biology.

Mitigating Winner-Take-All Resource Competition through Antithetic Control Mechanism

Competition among genes for limited transcriptional and translational resources impairs the functionality and modularity of synthetic gene circuits. Traditional control mechanisms, such as feedforward and negative feedback loops, have been proposed to alleviate these challenges, but they often focus on individual modules or inadvertently increase the burden on the system. In this study, we introduce three novel multimodule control strategies─local regulation, global regulation, and negatively competitive regulation (NCR)─that employ an antithetic regulatory mechanism to mitigate resource competition. Our systematic analysis reveals that while all three control mechanisms can alleviate resource competition to some extent, the NCR controller consistently outperforms both the global and local controllers. This superior performance stems from the unique architecture of the NCR controller, which is independent of specific parameter choices. Notably, the NCR controller not only facilitates the activation of less active modules through cross-activation mechanisms but also effectively utilizes the resource consumption within the controller itself. These findings emphasize the critical role of carefully designing the topology of multimodule controllers to ensure robust performance.

Noise Reduction in Resource-Coupled Multi-Module Gene Circuits through Antithetic Feedback Control

Gene circuits within the same host cell often experience coupling, stemming from the competition for limited resources during transcriptional and translational processes. This resource competition introduces an additional layer of noise to gene expression. Here we present three multi-module antithetic control strategies: negatively competitive regulation (NCR) controller, alongside local and global controllers, aimed at reducing the gene expression noise within the context of resource competition. Through stochastic simulations and fluctuation-dissipation theorem (FDT) analysis, our findings highlight the superior performance of the NCR antithetic controller in reducing noise levels. Our research provides an effective control strategy for attenuating resource-driven noise and offers insight into the development of robust gene circuits.

Engineering biology and automation-Replicability as a design principle

Applications in engineering biology increasingly share the need to run operations on very large numbers of biological samples. This is a direct consequence of the application of good engineering practices, the limited predictive power of current computational models and the desire to investigate very large design spaces in order to solve the hard, important problems the discipline promises to solve. Automation has been proposed as a key component for running large numbers of operations on biological samples. This is because it is strongly associated with higher throughput, and with higher replicability (thanks to the reduction of human input). The authors focus on replicability and make the point that, far from being an additional burden for automation efforts, replicability should be considered central to the design of the automated pipelines processing biological samples at scale-as trialled in biofoundries. There cannot be successful automation without effective error control. Design principles for an IT infrastructure that supports replicability are presented. Finally, the authors conclude with some perspectives regarding the evolution of automation in engineering biology. In particular, they speculate that the integration of hardware and software will show rapid progress, and offer users a degree of control and abstraction of the robotic infrastructure on a level significantly greater than experienced today.

Rational Design of Untranslated Regions to Enhance Gene Expression

How to improve gene expression by optimizing mRNA structures is a crucial question for various medical and biotechnological applications. Previous efforts focus largely on investigation of the 5' UTR hairpin structures. In this study, we present a rational strategy that enhances mRNA stability and translation by engineering both the 5' and 3' UTR sequences. We have successfully demonstrated this strategy using green fluorescent protein (GFP) as a model in Escherichia coli and across different expression vectors. We further validated it with luciferase and Plasmodium falciparum lactate dehydrogenase (PfLDH). To elucidate the underlying mechanism, we have quantitatively analyzed both protein, mRNA levels and half-life time. We have identified several key aspects of UTRs that significantly influence mRNA stability and protein expression in our system: (1) The optimal length of the single-stranded spacer between the stabilizer hairpin and ribosome binding site (RBS) in the 5' UTR is 25-30 nucleotide (nt) long. An optimal 32% GC content in the spacer yielded the highest levels of GFP protein production. (2) The insertion of a homodimerdizable, G-quadruplex structure containing RNA aptamer, "Corn", in the 3' UTR markedly increased the protein expression. Our findings indicated that the carefully engineered 5' UTRs and 3' UTRs significantly boosted gene expression. Specifically, the inclusion of 5 × Corn in the 3' UTR appeared to facilitate the local aggregation of mRNA, leading to the formation of mRNA condensates. Aside from shedding light on the regulation of mRNA stability and expression, this study is expected to substantially increase biological protein production.

Rewiring native post-transcriptional global regulators to achieve designer, multi-layered genetic circuits

As synthetic biology expands, creating "drag-and-drop" regulatory tools that can achieve diverse regulatory outcomes are paramount. Herein, we develop a approach for engineering complex post-transcriptional control by rewiring the Carbon Storage Regulatory (Csr) Network of Escherichia coli. We co-opt native interactions of the Csr Network to establish post-transcriptional logic gates and achieve complex bacterial regulation. First, we rationally engineer RNA-protein interactions to create a genetic toolbox of 12 BUFFER Gates that achieves a 15-fold range of expression. Subsequently, we develop a Csr-regulated NOT Gate by integrating a cognate 5' UTR that is natively Csr-activated into our platform. We then deploy the BUFFER and NOT gates to build a bi-directional regulator, two input Boolean Logic gates OR, NOR, AND and NAND and a pulse-generating circuit. Last, we port our Csr-regulated BUFFER Gate into three industrially relevant bacteria simply by leveraging the conserved Csr Network in each species.

Pangenomic landscapes shape performances of a synthetic genetic circuit across Stutzerimonas species

Engineering identical genetic circuits into different species typically results in large differences in performance due to the unique cellular environmental context of each host, a phenomenon known as the "chassis-effect" or "context-dependency". A better understanding of how genomic and physiological contexts underpin the chassis-effect will improve biodesign strategies across diverse microorganisms. Here, we combined a pangenomic-based gene expression analysis with quantitative measurements of performance from an engineered genetic inverter device to uncover how genome structure and function relate to the observed chassis-effect across six closely related Stutzerimonas hosts. Our results reveal that genome architecture underpins divergent responses between our chosen non-model bacterial hosts to the engineered device. Specifically, differential expression of the core genome, gene clusters shared between all hosts, was found to be the main source of significant concordance to the observed differential genetic device performance, whereas specialty genes from respective accessory genomes were not significant. A data-driven investigation revealed that genes involved in denitrification and components of trans-membrane transporter proteins were among the most differentially expressed gene clusters between hosts in response to the genetic device. Our results show that the chassis-effect can be traced along differences among the most conserved genome-encoded functions and that these differences create a unique biodesign space among closely related species.IMPORTANCEContemporary synthetic biology endeavors often default to a handful of model organisms to host their engineered systems. Model organisms such as Escherichia coli serve as attractive hosts due to their tractability but do not necessarily provide the ideal environment to optimize performance. As more novel microbes are domesticated for use as biotechnology platforms, synthetic biologists are urged to explore the chassis-design space to optimize their systems and deliver on the promises of synthetic biology. The consequences of the chassis-effect will therefore only become more relevant as the field of biodesign grows. In our work, we demonstrate that the performance of a genetic device is highly dependent on the host environment it operates within, promoting the notion that the chassis can be considered a design variable to tune circuit function. Importantly, our results unveil that the chassis-effect can be traced along similarities in genome architecture, specifically the shared core genome. Our study advocates for the exploration of the chassis-design space and is a step forward to empowering synthetic biologists with knowledge for more efficient exploration of the chassis-design space to enable the next generation of broad-host-range synthetic biology.

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence

Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.

Context-dependent redesign of robust synthetic gene circuits

Cells provide dynamic platforms for executing exogenous genetic programs in synthetic biology, resulting in highly context-dependent circuit performance. Recent years have seen an increasing interest in understanding the intricacies of circuit-host relationships, their influence on the synthetic bioengineering workflow, and in devising strategies to alleviate undesired effects. We provide an overview of how emerging circuit-host interactions, such as growth feedback and resource competition, impact both deterministic and stochastic circuit behaviors. We also emphasize control strategies for mitigating these unwanted effects. This review summarizes the latest advances and the current state of host-aware and resource-aware design of synthetic gene circuits.

Noise Reduction in Resource-Coupled Multi-Module Gene Circuits through Antithetic Feedback Control

Gene circuits within the same host cell often experience coupling, stemming from the competition for limited resources during transcriptional and translational processes. This resource competition introduces an additional layer of noise to gene expression. Here we present three multi-module antithetic control strategies: negatively competitive regulation (NCR) controller, alongside local and global controllers, aimed at reducing the gene expression noise within the context of resource competition. Through stochastic simulations and fluctuation-dissipation theorem (FDT) analysis, our findings highlight the superior performance of the NCR antithetic controller in reducing noise levels. Our research provides an effective control strategy for attenuating resource-driven noise and offers insight into the development of robust gene circuits.

Design and Model-Driven Analysis of Synthetic Circuits with the Staphylococcus aureus Dead-Cas9 (sadCas9) as a Programmable Transcriptional Regulator in Bacteria

Synthetic circuit design is crucial for engineering microbes that process environmental cues and provide biologically relevant outputs. To reliably scale-up circuit complexity, the availability of parts toolkits is central. Streptococcus pyogenes (sp)-derived CRISPR interference/dead-Cas9 (CRISPRi/spdCas9) is widely adopted for implementing programmable regulations in synthetic circuits, and alternative CRISPRi systems will further expand our toolkits of orthogonal components. Here, we showcase the potential of CRISPRi using the engineered dCas9 from Staphylococcus aureus (sadCas9), not previously used in bacterial circuits, that is attractive for its low size and high specificity. We designed a collection of ∼20 increasingly complex circuits and variants in Escherichia coli, including circuits with static function like one-/two-input logic gates (NOT, NAND), circuits with dynamic behavior like incoherent feedforward loops (iFFLs), and applied sadCas9 to fix a T7 polymerase-based cascade. Data demonstrated specific and efficient target repression (100-fold) and qualitatively successful functioning for all circuits. Other advantageous features included low sadCas9-borne cell load and orthogonality with spdCas9. However, different circuit variants showed quantitatively unexpected and previously unreported steady-state responses: the dynamic range, switch point, and slope of NOT/NAND gates changed for different output promoters, and a multiphasic behavior was observed in iFFLs, differing from the expected bell-shaped or sigmoidal curves. Model analysis explained the observed curves by complex interplays among components, due to reporter gene-borne cell load and regulator competition. Overall, CRISPRi/sadCas9 successfully expanded the available toolkit for bacterial engineering. Analysis of our circuit collection depicted the impact of generally neglected effects modulating the shape of component dose-response curves, to avoid drawing wrong conclusions on circuit functioning.

Specific codons control cellular resources and fitness

As cellular engineering progresses from simply overexpressing proteins to imparting complex phenotypes through multigene expression, judicious appropriation of cellular resources is essential. Since codon use is degenerate and biased, codons may control cellular resources at a translational level. We investigate how partitioning transfer RNA (tRNA) resources by incorporating dissimilar codon usage can drastically alter interdependence of expression level and burden on the host. By isolating the effect of individual codons' use during translation elongation while eliminating confounding factors, we show that codon choice can trans-regulate fitness of the host and expression of other heterologous or native genes. We correlate specific codon usage patterns with host fitness and derive a coding scheme for multigene expression called the Codon Health Index (CHI, χ). This empirically derived coding scheme (χ) enables the design of multigene expression systems that avoid catastrophic cellular burden and is robust across several proteins and conditions.

High-throughput investigation of genetic design constraints in domesticated Influenza A Virus for transient gene delivery

Replication-incompetent single cycle infectious Influenza A Virus (sciIAV) has demonstrated utility as a research and vaccination platform. Protein-based therapeutics are increasingly attractive due to their high selectivity and potent efficacy but still suffer from low bioavailability and high manufacturing cost. Transient RNA-mediated delivery is a safe alternative that allows for expression of protein-based therapeutics within the target cells or tissues but is limited by delivery efficiency. Here, we develop recombinant sciIAV as a platform for transient gene delivery in vivo and in vitro for therapeutic, research, and manufacturing applications (in vivo antimicrobial production, cell culture contamination clearance, and production of antiviral proteins in vitro). While adapting the system to deliver new protein cargo we discovered expression differences presumably resulting from genetic context effects. We applied a high-throughput screen to map these within the 3'-untranslated and coding regions of the hemagglutinin-encoding segment 4. This screen revealed permissible mutations in the 3'-UTR and depletion of RNA level motifs in the N-terminal coding region.

Enhancing circuit stability under growth feedback with supplementary repressive regulation

The field of synthetic biology and biosystems engineering increasingly acknowledges the need for a holistic design approach that incorporates circuit-host interactions into the design process. Engineered circuits are not isolated entities but inherently entwined with the dynamic host environment. One such circuit-host interaction, 'growth feedback', results when modifications in host growth patterns influence the operation of gene circuits. The growth-mediated effects can range from growth-dependent elevation in protein/mRNA dilution rate to changes in resource reallocation within the cell, which can lead to complete functional collapse in complex circuits. To achieve robust circuit performance, synthetic biologists employ a variety of control mechanisms to stabilize and insulate circuit behavior against growth changes. Here we propose a simple strategy by incorporating one repressive edge in a growth-sensitive bistable circuit. Through both simulation and in vitro experimentation, we demonstrate how this additional repressive node stabilizes protein levels and increases the robustness of a bistable circuit in response to growth feedback. We propose the incorporation of repressive links in gene circuits as a control strategy for desensitizing gene circuits against growth fluctuations.

Open-endedness in synthetic biology: A route to continual innovation for biological design

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

Using design of experiments to guide genetic optimization of engineered metabolic pathways

Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges.

ONE-SENTENCE SUMMARY

This is a review of literature related to applying Design of Experiments for genetic optimization.

Transcriptional biomarker discovery toward building a load stress reporting system for engineered Escherichia coli strains

Foreign proteins are produced by introducing synthetic constructs into host bacteria for biotechnology applications. This process can cause resource competition between synthetic circuits and host cells, placing a metabolic burden on the host cells which may result in load stress and detrimental physiological changes. Consequently, the host bacteria can experience slow growth, and the synthetic system may suffer from suboptimal function. To help in the detection of bacterial load stress, we developed machine-learning strategies to select a minimal number of genes that could serve as biomarkers for the design of load stress reporters. We identified pairs of biomarkers that showed discriminative capacity to detect the load stress states induced in 41 engineered Escherichia coli strains.

Competition and evolutionary selection among core regulatory motifs in gene expression control

Gene products that are beneficial in one environment may become burdensome in another, prompting the emergence of diverse regulatory schemes that carry their own bioenergetic cost. By ensuring that regulators are only expressed when needed, we demonstrate that autoregulation generally offers an advantage in an environment combining mutation and time-varying selection. Whether positive or negative feedback emerges as dominant depends primarily on the demand for the target gene product, typically to ensure that the detrimental impact of inevitable mutations is minimized. While self-repression of the regulator curbs the spread of these loss-of-function mutations, self-activation instead facilitates their propagation. By analyzing the transcription network of multiple model organisms, we reveal that reduced bioenergetic cost may contribute to the preferential selection of autoregulation among transcription factors. Our results not only uncover how seemingly equivalent regulatory motifs have fundamentally different impact on population structure, growth dynamics, and evolutionary outcomes, but they can also be leveraged to promote the design of evolutionarily robust synthetic gene circuits.

Golden Standard: a complete standard, portable, and interoperative MoClo tool for model and non-model proteobacteria

Modular cloning has become a benchmark technology in synthetic biology. However, a notable disparity exists between its remarkable development and the need for standardization to facilitate seamless interoperability among systems. The field is thus impeded by an overwhelming proliferation of organism-specific systems that frequently lack compatibility. To overcome these issues, we present Golden Standard (GS), a Type IIS assembly method underpinned by the Standard European Vector Architecture. GS unlocks modular cloning applications for most bacteria, and delivers combinatorial multi-part assembly to create genetic circuits of up to twenty transcription units (TUs). Reliance on MoClo syntax renders GS fully compatible with many existing tools and it sets the path towards efficient reusability of available part libraries and assembled TUs. GS was validated in terms of DNA assembly, portability, interoperability and phenotype engineering in α-, β-, γ- and δ-proteobacteria. Furthermore, we provide a computational pipeline for parts characterization that was used to assess the performance of GS parts. To promote community-driven development of GS, we provide a dedicated web-portal including a repository of parts, vectors, and Wizard and Setup tools that guide users in designing constructs. Overall, GS establishes an open, standardized framework propelling the progress of synthetic biology as a whole.

Noise properties of adaptation-conferring biochemical control modules

A key goal of synthetic biology is to develop functional biochemical modules with network-independent properties. Antithetic integral feedback (AIF) is a recently developed control module in which two control species perfectly annihilate each other's biological activity. The AIF module confers robust perfect adaptation to the steady-state average level of a controlled intracellular component when subjected to sustained perturbations. Recent work has suggested that such robustness comes at the unavoidable price of increased stochastic fluctuations around average levels. We present theoretical results that support and quantify this trade-off for the commonly analyzed AIF variant in the idealized limit with perfect annihilation. However, we also show that this trade-off is a singular limit of the control module: Even minute deviations from perfect adaptation allow systems to achieve effective noise suppression as long as cells can pay the corresponding energetic cost. We further show that a variant of the AIF control module can achieve significant noise suppression even in the idealized limit with perfect adaptation. This atypical configuration may thus be preferable in synthetic biology applications.

Revealing the Host-Dependent Nature of an Engineered Genetic Inverter in Concordance with Physiology

Broad-host-range synthetic biology is an emerging frontier that aims to expand our current engineerable domain of microbial hosts for biodesign applications. As more novel species are brought to "model status," synthetic biologists are discovering that identically engineered genetic circuits can exhibit different performances depending on the organism it operates within, an observation referred to as the "chassis effect." It remains a major challenge to uncover which genome-encoded and biological determinants will underpin chassis effects that govern the performance of engineered genetic devices. In this study, we compared model and novel bacterial hosts to ask whether phylogenomic relatedness or similarity in host physiology is a better predictor of genetic circuit performance. This was accomplished using a comparative framework based on multivariate statistical approaches to systematically demonstrate the chassis effect and characterize the performance dynamics of a genetic inverter circuit operating within 6 Gammaproteobacteria. Our results solidify the notion that genetic devices are strongly impacted by the host context. Furthermore, we formally determined that hosts exhibiting more similar metrics of growth and molecular physiology also exhibit more similar performance of the genetic inverter, indicating that specific bacterial physiology underpins measurable chassis effects. The result of this study contributes to the field of broad-host-range synthetic biology by lending increased predictive power to the implementation of genetic devices in less-established microbial hosts.

Regulation strategies for two-output biomolecular networks

Feedback control theory facilitates the development of self-regulating systems with desired performance which are predictable and insensitive to disturbances. Feedback regulatory topologies are found in many natural systems and have been of key importance in the design of reliable synthetic bio-devices operating in complex biological environments. Here, we study control schemes for biomolecular processes with two outputs of interest, expanding previously described concepts based on single-output systems. Regulation of such processes may unlock new design possibilities but can be challenging due to coupling interactions; also potential disturbances applied on one of the outputs may affect both. We therefore propose architectures for robustly manipulating the ratio/product and linear combinations of the outputs as well as each of the outputs independently. To demonstrate their characteristics, we apply these architectures to a simple process of two mutually activated biomolecular species. We also highlight the potential for experimental implementation by exploring synthetic realizations both in vivo and in vitro. This work presents an important step forward in building bio-devices capable of sophisticated functions.

Engineering tRNA abundances for synthetic cellular systems

Routinizing the engineering of synthetic cells requires specifying beforehand how many of each molecule are needed. Physics-based tools for estimating desired molecular abundances in whole-cell synthetic biology are missing. Here, we use a colloidal dynamics simulator to make predictions for how tRNA abundances impact protein synthesis rates. We use rational design and direct RNA synthesis to make 21 synthetic tRNA surrogates from scratch. We use evolutionary algorithms within a computer aided design framework to engineer translation systems predicted to work faster or slower depending on tRNA abundance differences. We build and test the so-specified synthetic systems and find qualitative agreement between expected and observed systems. First principles modeling combined with bottom-up experiments can help molecular-to-cellular scale synthetic biology realize design-build-work frameworks that transcend tinker-and-test.

Prolonging genetic circuit stability through adaptive evolution of overlapping genes

The development of synthetic biological circuits that maintain functionality over application-relevant time scales remains a significant challenge. Here, we employed synthetic overlapping sequences in which one gene is encoded or 'entangled' entirely within an alternative reading frame of another gene. In this design, the toxin-encoding relE was entangled within ilvA, which encodes threonine deaminase, an enzyme essential for isoleucine biosynthesis. A functional entanglement construct was obtained upon modification of the ribosome-binding site of the internal relE gene. Using this optimized design, we found that the selection pressure to maintain functional IlvA stabilized the production of burdensome RelE for >130 generations, which compares favorably with the most stable kill-switch circuits developed to date. This stabilizing effect was achieved through a complete alteration of the allowable landscape of mutations such that mutations inactivating the entangled genes were disfavored. Instead, the majority of lineages accumulated mutations within the regulatory region of ilvA. By reducing baseline relE expression, these more 'benign' mutations lowered circuit burden, which suppressed the accumulation of relE-inactivating mutations, thereby prolonging kill-switch function. Overall, this work demonstrates the utility of sequence entanglement paired with an adaptive laboratory evolution campaign to increase the evolutionary stability of burdensome synthetic circuits.

In Vivo Sampling of Intracellular Heterogeneity of Pseudomonas putida Enables Multiobjective Optimization of Genetic Devices

The inner physicochemical heterogeneity of bacterial cells generates three-dimensional (3D)-dependent variations of resources for effective expression of given chromosomally located genes. This fact has been exploited for adjusting the most favorable parameters for implanting a complex device for optogenetic control of biofilm formation in the soil bacterium Pseudomonas putida. To this end, a DNA segment encoding a superactive variant of the Caulobacter crescendus diguanylate cyclase PleD expressed under the control of the cyanobacterial light-responsive CcaSR system was placed in a mini-Tn5 transposon vector and randomly inserted through the chromosome of wild-type and biofilm-deficient variants of P. putida lacking the wsp gene cluster. This operation delivered a collection of clones covering a whole range of biofilm-building capacities and dynamic ranges in response to green light. Since the phenotypic output of the device depends on a large number of parameters (multiple promoters, RNA stability, translational efficacy, metabolic precursors, protein folding, etc.), we argue that random chromosomal insertions enable sampling the intracellular milieu for an optimal set of resources that deliver a preset phenotypic specification. Results thus support the notion that the context dependency can be exploited as a tool for multiobjective optimization, rather than a foe to be suppressed in Synthetic Biology constructs.

A blueprint for a synthetic genetic feedback optimizer

Biomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.

Shaping bacterial gene expression by physiological and proteome allocation constraints

Networks of molecular regulators are often the primary objects of focus in the study of gene regulation, with the machinery of protein synthesis tacitly relegated to the background. Shifting focus to the constraints imposed by the allocation of protein synthesis flux reveals surprising ways in which the actions of molecular regulators are shaped by physiological demands. Using carbon catabolite repression as a case study, we describe how physiological constraints are sensed through metabolic fluxes and how flux-controlled regulation gives rise to simple empirical relations between protein levels and the rate of cell growth.

Gene-by-environment interactions are pervasive among natural genetic variants

Gene-by-environment (GxE) interactions, in which a genetic variant's phenotypic effect is condition specific, are fundamental for understanding fitness landscapes and evolution but have been difficult to identify at the single-nucleotide level. Although many condition-specific quantitative trait loci (QTLs) have been mapped, these typically contain numerous inconsequential variants in linkage, precluding understanding of the causal GxE variants. Here, we introduce BARcoded Cas9 retron precise parallel editing via homology (CRISPEY-BAR), a high-throughput precision genome editing strategy, and use it to map GxE interactions of naturally occurring genetic polymorphisms impacting yeast growth. We identified hundreds of GxE variants within condition-specific QTLs, revealing unexpected genetic complexity. Moreover, we found that 93.7% of non-neutral natural variants within ergosterol biosynthesis pathway genes showed GxE interactions, including many impacting antifungal drug resistance through diverse molecular mechanisms. In sum, our results suggest an extremely complex, context-dependent fitness landscape characterized by pervasive GxE interactions while also demonstrating massively parallel genome editing as an effective means for investigating this complexity.

Computational capabilities of a multicellular reservoir computing system

The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits. To overcome these limitations, synthetic biologists have begun engineering multicellular systems that combine cells with designed subfunctions. To further advance information processing in synthetic multicellular systems, we introduce the application of reservoir computing. Reservoir computers (RCs) approximate a temporal signal processing task via a fixed-rule dynamic network (the reservoir) with a regression-based readout. Importantly, RCs eliminate the need of network rewiring, as different tasks can be approximated with the same reservoir. Previous work has already demonstrated the capacity of single cells, as well as populations of neurons, to act as reservoirs. In this work, we extend reservoir computing in multicellular populations with the widespread mechanism of diffusion-based cell-to-cell signaling. As a proof-of-concept, we simulated a reservoir made of a 3D community of cells communicating via diffusible molecules and used it to approximate a range of binary signal processing tasks, focusing on two benchmark functions-computing median and parity functions from binary input signals. We demonstrate that a diffusion-based multicellular reservoir is a feasible synthetic framework for performing complex temporal computing tasks that provides a computational advantage over single cell reservoirs. We also identified a number of biological properties that can affect the computational performance of these processing systems.

Chimeric MerR-Family Regulators and Logic Elements for the Design of Metal Sensitive Genetic Circuits in Bacillus subtilis

Whole-cell biosensors are emerging as promising tools for monitoring environmental pollutants such as heavy metals. These sensors constitute a genetic circuit comprising a sensing module and an output module, such that a detectable signal is produced in the presence of the desired analyte. The MerR family of metal-responsive regulators offers great potential for the construction of metal sensing circuits, due to their high sensitivity, tight transcription control, and large diversity in metal-specificity. However, the sensing diversity is broadest in Gram-negative systems, while chassis organisms are often selected from Gram-positive species, particularly sporulating bacilli. This can be problematic, because Gram-negative biological parts, such as promoters, are frequently observed to be nonfunctional in Gram-positive hosts. Herein, we combined construction of synthetic genetic circuits and chimeric MerR regulators, supported by structure-guided design, to generate metal-sensitive biosensor modules that are functional in the biotechnological work-horse species Bacillus subtilis. These chimeras consist of a constant Gram-positive derived DNA-binding domain fused to variable metal binding domains of Gram-negative origins. To improve the specificity of the whole-cell biosensor, we developed a modular "AND gate" logic system based on the B. subtilis two-subunit σ-factor, SigO-RsoA, designed to maximize future use for synthetic biology applications in B. subtilis. This work provides insights into the use of modular regulators, such as the MerR family, in the design of synthetic circuits for the detection of heavy metals, with potentially wider applicability of the approach to other systems and genetic backgrounds.

Rational search of genetic design space for a heterologous terpene metabolic pathway in Streptomyces

Modern tools in DNA synthesis and assembly give genetic engineers control over the nucleotide-level design of complex, multi-gene systems. Systematic approaches to explore genetic design space and optimize the performance of genetic constructs are lacking. Here we explore the application of a five-level Plackett-Burman fractional factorial design to improve the titer of a heterologous terpene biosynthetic pathway in Streptomyces. A library of 125 engineered gene clusters encoding the production of diterpenoid ent-atiserenoic acid (eAA) via the methylerythritol phosphate pathway was constructed and introduced into Streptomyces albidoflavus J1047 for heterologous expression. The eAA production titer varied within the library by over two orders of magnitude and host strains showed unexpected and reproducible colony morphology phenotypes. Analysis of Plackett-Burman design identified expression of dxs, the gene encoding the first and the flux-controlling enzyme, having the strongest impact on eAA titer, but with a counter-intuitive negative correlation between dxs expression and eAA production. Finally, simulation modeling was performed to determine how several plausible sources of experimental error/noise and non-linearity impact the utility of Plackett-Burman analyses.

Efficient gene activation in plants by the MoonTag programmable transcriptional activator

CRISPR/Cas-based transcriptional activators have been developed to induce gene expression in eukaryotic and prokaryotic organisms. The main advantages of CRISPR-Cas based systems is that they can achieve high levels of transcriptional activation and are very easy to program via pairing between the guide RNA and the DNA target strand. SunTag is a second-generation system that activates transcription by recruiting multiple copies of an activation domain (AD) to its target promoters. SunTag is a strong activator; however, in some species it is difficult to stably express. To overcome this problem, we designed MoonTag, a new activator that worked on the same basic principle as SunTag, but whose components are better tolerated when stably expressed in transgenic plants. We demonstrate that MoonTag is capable of inducing high levels of transcription in all plants tested. In Setaria, MoonTag is capable of inducing high levels of transcription of reporter genes as well as of endogenous genes. More important, MoonTag components are expressed in transgenic plants to high levels without any deleterious effects. MoonTag is also able to efficiently activate genes in eudicotyledonous species such as Arabidopsis and tomato. Finally, we show that MoonTag activation is functional across a range of temperatures, which is promising for potential field applications.

Context-Aware Technology Mapping in Genetic Design Automation

Genetic design automation (GDA) tools hold promise to speed-up circuit design in synthetic biology. Their widespread adoption is hampered by their limited predictive power, resulting in frequent deviations between the in silico and in vivo performance of a genetic circuit. Context effects, i.e., the change in overall circuit functioning, due to the intracellular environment of the host and due to cross-talk among circuits components are believed to be a major source for the aforementioned deviations. Incorporating these effects in computational models of GDA tools is challenging but is expected to boost their predictive power and hence their deployment. Using fine-grained thermodynamic models of promoter activity, we show in this work how to account for two major components of cellular context effects: (i) crosstalk due to limited specificity of used regulators and (ii) titration of circuit regulators to off-target binding sites on the host genome. We show how we can compensate the incurred increase in computational complexity through dedicated branch-and-bound techniques during the technology mapping process. Using the synthesis of several combinational logic circuits based on Cello's device library as a case study, we analyze the effect of different intensities and distributions of crosstalk on circuit performance and on the usability of a given device library.

The two principles that shape scientific research

This paper argues that all scientific research is framed by one of two organizing principles that underpin and shape almost every aspect of scientific research as well as nonscientific inquiry. The most commonly employed principle within mainstream science is content determines content. This is a closed, circular principle that is usually unstated within hypotheses but plays a major role in developing methodologies and arriving at conclusions. The second more open principle is context determines content. This principle represents the implied background embedded within hypotheses. The difference between these two principles revolves around the issue of context, with the first principle closing off contexts by ignoring, erasing, or devaluing them, while the second more holistic principle explicitly takes them into account. Each of these research principles has a focus on the explicit detailed nature of 'content' while differing in relation to the source and cause of such content. We argue that the more open and holistic principle of context determines that content is superior in producing reliable evidence, results and conclusions.

Negatively Competitive Incoherent Feedforward Loops Mitigate Winner-Take-All Resource Competition

The effects of host resource limitations on the function of synthetic gene circuits have gained significant attention over the past years. Hosts, having evolved resource capacities optimal for their own genome, have been repeatedly demonstrated to suffer from the added burden of synthetic genetic programs, which may in return pose deleterious effects on the circuit's function. Three resource controller archetypes have been proposed previously to mitigate resource distribution problems in dynamic circuits: the local controller, the global controller, and a "negatively competitive" regulatory (NCR) controller that utilizes synthetic competition to combat resource competition. The dynamics of negative feedback forms of these controllers have been previously investigated, and here we extend the analysis of these resource allocation strategies to the incoherent feedforward loop (iFFL) topology. We demonstrate that the three iFFL controllers can attenuate Winner-Take-All resource competition between two bistable switches. We uncover that the parameters associated with the synthetic competition in the NCR iFFL controller are paramount to its increased efficacy over the local controller type, while the global controllers demonstrate to be relatively ineffectual. Interestingly, unlike the negative feedback counterpart topologies, iFFL controllers exhibit a unique coupling of switch activation thresholds which we term the "coactivation threshold shift" effect. Finally, we demonstrate that a nearly fully orthogonal set of bistable switches could be achieved by pairing an NCR controller with an appropriate level of controller resource consumption.

Universal structural requirements for maximal robust perfect adaptation in biomolecular networks

Adaptation is a running theme in biology. It allows a living system to survive and thrive in the face of unpredictable environments by maintaining key physiological variables at their desired levels through tight regulation. When one such variable is maintained at a certain value at the steady state despite perturbations to a single input, this property is called robust perfect adaptation (RPA). Here we address and solve the fundamental problem of maximal RPA (maxRPA), whereby, for a designated output variable, RPA is achieved with respect to perturbations in virtually all network parameters. In particular, we show that the maxRPA property imposes certain structural constraints on the network. We then prove that these constraints are fully characterized by simple linear algebraic stoichiometric conditions which differ between deterministic and stochastic descriptions of the dynamics. We use our results to derive a new internal model principle (IMP) for biomolecular maxRPA networks, akin to the celebrated IMP in control theory. We exemplify our results through several known biological examples of robustly adapting networks and construct examples of such networks with the aid of our linear algebraic characterization. Our results reveal the universal requirements for maxRPA in all biological systems, and establish a foundation for studying adaptation in general biomolecular networks, with important implications for both systems and synthetic biology.

Synthetic neuromorphic computing in living cells

Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their revolutionary impact on computing, neuro-inspired models can transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and readily reconfigurable for different tasks. To this end, we introduce the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in Escherichia coli cells. We successfully modify perceptgene parameters to create devices that encode a minimum, maximum, and average of analog inputs. With these devices, we create multi-layer perceptgene circuits that compute a soft majority function, perform an analog-to-digital conversion, and implement a ternary switch. We also create a programmable perceptgene circuit whose computation can be modified from OR to AND logic using small molecule induction. Finally, we show that our approach enables circuit optimization via artificial intelligence algorithms.

A Framework for the Systematic Selection of Biosensor Chassis for Environmental Synthetic Biology

Microbial biosensors sense and report exposures to stimuli, thereby facilitating our understanding of environmental processes. Successful design and deployment of biosensors hinge on the persistence of the microbial host of the genetic circuit, termed the chassis. However, model chassis organisms may persist poorly in environmental conditions. In contrast, non-model organisms persist better in environmental conditions but are limited by other challenges, such as genetic intractability and part unavailability. Here we identify ecological, metabolic, and genetic constraints for chassis development and propose a conceptual framework for the systematic selection of environmental biosensor chassis. We identify key challenges with using current model chassis and delineate major points of conflict in choosing the most suitable organisms as chassis for environmental biosensing. This framework provides a way forward in the selection of biosensor chassis for environmental synthetic biology.

Emergence of qualitative states in synthetic circuits driven by ultrasensitive growth feedback

The mutual interactions between the synthetic gene circuits and the host growth could cause unexpected outcomes in the dynamical behaviors of the circuits. However, how the steady states and the stabilities of the gene circuits are affected by host cell growth is not fully understood. Here, we developed a mathematical model for nonlinear growth feedback based on published experimental data. The model analysis predicts that growth feedback could significantly change the qualitative states of the system. Bistability could emerge in a circuit without positive feedback, and high-order multistability (three or more steady states) arises in the self-activation and toggle switch circuits. Our results provide insight into the potential effects of ultrasensitive growth feedback on the emergence of qualitative states in synthetic circuits and the corresponding underlying mechanism.

The mutual inhibitory effect between synthetic gene circuits and cell growth produces growth feedback in the host-circuit system. Previous studies have demonstrated that the growth feedback could significantly impact the dynamics of the host-circuit system. However, the complexity of the growth feedback impact is not fully understood. Here, our data analysis displays ultrasensitive growth feedback between the cells and synthetic gene circuits under different growth conditions. To study the effect of ultrasensitive growth feedback on the host-circuit system, we develop a mathematical modeling framework. Our results reveal the emergence of qualitative states on the host-circuit system induced by ultrasensitive growth feedback. We found an emergence of bistability in a simple synthetic gene circuit with a constitutive promoter. Also, tristability could be seen in self-activation and toggle switch circuits. Our research uncovered the effect of ultrasensitive growth feedback in synthetic gene circuits and host interactions. Understanding the effects of ultrasensitive growth feedback could help scientists and engineers identify unexpected outcomes in gene circuits and formulate control strategies.

Genetic Circuit Design in Rhizobacteria

Genetically engineered plants hold enormous promise for tackling global food security and agricultural sustainability challenges. However, construction of plant-based genetic circuitry is constrained by a lack of well-characterized genetic parts and circuit design rules. In contrast, advances in bacterial synthetic biology have yielded a wealth of sensors, actuators, and other tools that can be used to build bacterial circuitry. As root-colonizing bacteria (rhizobacteria) exert substantial influence over plant health and growth, genetic circuit design in these microorganisms can be used to indirectly engineer plants and accelerate the design-build-test-learn cycle. Here, we outline genetic parts and best practices for designing rhizobacterial circuits, with an emphasis on sensors, actuators, and chassis species that can be used to monitor/control rhizosphere and plant processes.

The context matrix: Navigating biological complexity for advanced biodesign

Synthetic biology offers many solutions in healthcare, production, sensing and agriculture. However, the ability to rationally engineer synthetic biosystems with predictable and robust functionality remains a challenge. A major reason is the complex interplay between the synthetic genetic construct, its host, and the environment. Each of these contexts contains a number of input factors which together can create unpredictable behaviours in the engineered biosystem. It has become apparent that for the accurate assessment of these contextual effects a more holistic approach to design and characterisation is required. In this perspective article, we present the context matrix, a conceptual framework to categorise and explore these contexts and their net effect on the designed synthetic biosystem. We propose the use and community-development of the context matrix as an aid for experimental design that simplifies navigation through the complex design space in synthetic biology.

The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors

Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.

Determination of Nucleotide Sequences within Promoter Regions Affecting Promoter Compatibility between Zymomonas mobilis and Escherichia coli

A promoter plays a crucial role in controlling the expression of the target gene in cells, thus being one of the key biological parts for synthetic biology practices. Although significant efforts have been made to identify and characterize promoters with different strengths in various microorganisms, the compatibility of promoters within different hosts still lacks investigation. In this study, we chose the native Pgap promoter of Zymomonas mobilis to investigate nucleotide sequences within promoter regions affecting promoter compatibility between Escherichia coli and Z. mobilis. Pgap is one of the strongest promotors in Z. mobilis that has many excellent characteristics to be developed as microbial cell factories. Using EGFP as a reporter, a Z. mobilis-derived Pgap mutant library was constructed and sorted in E. coli, with candidate promoters exhibiting high fluorescence intensity collected. A total of 53 variants were finally selected and sequenced by Sanger sequencing. The sequencing results grouped these variants into 12 different Pgap variant types, among which seven types presented higher promoter strength than native Pgap in E. coli. The next-generation sequencing technique was then employed to identify key mutations within the Pgap promoter region that affect the promoter compatibility. Finally, six important sites were identified and confirmed to help increase Pgap strength in E. coli while keeping similar strength of native Pgap in Z. mobilis. Compared to native Pgap, synthetic promoters combining these sites had enhanced strength; especially, Pgap-6M combining all six sites exhibited 20-fold greater strength than native Pgap in E. coli. This study thus not only determined six important sites affecting promoter compatibility but also confirmed a series of Pgap promoter variants with strong promoter activity in both E. coli and Z. mobilis. In addition, a strategy was established in this study to investigate and determine nucleotide sequences in promoter regions affecting promoter compatibility, which can be applied in other microorganisms to help reveal universal factors affecting promoter compatibility and design promoters with desired strengths among different microbial cell factories.

Mitigating Host Burden of Genetic Circuits by Engineering Autonegatively Regulated Parts and Improving Functional Prediction

Mitigating unintended interferences between circuits and host cells is key to realize applications of synthetic regulatory systems both for bacteria and mammalian cells. Here, we demonstrated that growth burden and circuit dysregulation occurred in a concentration-dependent manner for specific transcription factors (CymR*/CymR) in E.coli, and direct negative feedback modules were able to control the concentration of CymR*/CymR, mitigate growth burden, and restore circuit functions. A quantitative design scheme was developed for circuits embedded with autorepression modules. Four key parameters were theoretically identified to determine the performance of autoregulated switches and were experimentally modified by fine-tuning promoter architectures and cooperativity. Using this strategy, we synthesized a number of switches and demonstrated its improvement of product titers and host growth controlling the complex deoxyviolacein biosynthesis pathway. Furthermore, we restored functions of a dysregulated multilayer NOR gate by integrating autorepression modules. Our work provides a blueprint for engineering host-adaptable synthetic systems.

Engineering Halomonas bluephagenesis via small regulatory RNAs

Halomonas bluephagenesis, a robust and contamination-resistant microorganism has been developed as a chassis for "Next Generation Industrial Biotechnology". The non-model H. bluephagenesis requires efficient tools to fine-tune its metabolic fluxes for enhanced production phenotypes. Here we report a highly efficient gene expression regulation system (PrrF1-2-HfqPa) in H. bluephagenesis, small regulatory RNA (sRNA) PrrF1 scaffold from Pseudomonas aeruginosa and a target-binding sequence that downregulate gene expression, and its cognate P. aeruginosa Hfq (HfqPa), recruited by the scaffold to facilitate the hybridization of sRNA and the target mRNA. The PrrF1-2-HfqPa system targeting prpC in H. bluephagenesis helps increase 3-hydroxyvalerate fraction in poly(3-hydroxybutyrate-co-3-hydroxyvalerate) to 21 mol% compared to 3.1 mol% of the control. This sRNA system repressed phaP1 and minD simultaneously, resulting in large polyhydroxybutyrate granules. Further, an sRNA library targeting 30 genes was employed for large-scale target identification to increase mevalonate production. This work expands the study on using an sRNA system not based on Escherichia coli MicC/SgrS-Hfq to repress gene expression, providing a framework to exploit new powerful genome engineering tools based on other sRNAs.

Resource-aware whole-cell model of division of labour in a microbial consortium for complex-substrate degradation

BACKGROUND

Low-cost sustainable feedstocks are essential for commercially viable biotechnologies. These feedstocks, often derived from plant or food waste, contain a multitude of different complex biomolecules which require multiple enzymes to hydrolyse and metabolise. Current standard biotechnology uses monocultures in which a single host expresses all the proteins required for the consolidated bioprocess. However, these hosts have limited capacity for expressing proteins before growth is impacted. This limitation may be overcome by utilising division of labour (DOL) in a consortium, where each member expresses a single protein of a longer degradation pathway.

RESULTS

Here, we model a two-strain consortium, with one strain expressing an endohydrolase and a second strain expressing an exohydrolase, for cooperative degradation of a complex substrate. Our results suggest that there is a balance between increasing expression to enhance degradation versus the burden that higher expression causes. Once a threshold of burden is reached, the consortium will consistently perform better than an equivalent single-cell monoculture.

CONCLUSIONS

We demonstrate that resource-aware whole-cell models can be used to predict the benefits and limitations of using consortia systems to overcome burden. Our model predicts the region of expression where DOL would be beneficial for growth on starch, which will assist in making informed design choices for this, and other, complex-substrate degradation pathways.