Modularity, context-dependence, and insulation in engineered biological circuits

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.

Limiting the Broadcast Range of a Secreting Cell during Intercellular Signaling Using Protease-Mediated Degradation

Synthetic biology is revolutionizing our approaches to biocomputing, diagnostics, and environmental monitoring through the use of designed genetic circuits that perform a function within a single cell. More complex functions can be performed by multiple cells that coordinate as they perform different subtasks. Cell-cell communication using molecular signals is particularly suited for aiding in this communication, but the number of molecules that can be used in different communication channels is limited. Here we investigate how proteases can limit the broadcast range of communicating cells. We find that adding barrierpepsin to Saccharomyces cerevisiae cells in two-dimensional multicellular networks that use α-factor signaling prevents cells beyond a specific radius from responding to α-factor signals. Such limiting of the broadcast range of cells could allow multiple cells to use the same signaling molecules to direct different communication processes and functions, provided that they are far enough from one another. These results suggest a means by which complex synthetic cellular networks using only a few signals for communication could be created by structuring a community of cells to create distinct broadcast environments.

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.

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.

Growth Feedback Confers Cooperativity in Resource-Competing Synthetic Gene Circuits

Modularity is a key concept in designing synthetic gene circuits, as it allows for constructing complex molecular systems using well-characterized building blocks. One of the major challenges in this field is that these modular components often do not function as expected when assembled into larger circuits. One of the major issues is caused by resource competition, where multiple genes in the circuit compete for the same limited cellular resources, such as transcription factors and ribosomes. In addition, the mutual inhibition between synthetic gene circuits and cell growth results in growth feedback that significantly impacts its host-circuit dynamics. However, the complexity of the gene circuit dynamics under intertwined resource competition and growth feedback is not fully understood. This study developed a theoretical framework to examine the dynamics of synthetic gene circuits by considering both growth feedback and resource competition. Our results suggest a cooperative behavior between resource-competing gene circuits under growth feedback. Cooperation or competition is non-monotonically determined by the metabolic burden threshold. These two diverse effects could lead to the activation or deactivation of one circuit by the other. Lastly, the cooperativity mediated by growth feedback can attenuate the winner-takes-all resource competition. These findings show that coupling growth feedback and resource competition plays a crucial role in the dynamics of the host-circuit system, and understanding its effects helps control unexpected gene expression behaviors.

Synthetic bacterial therapies for intestinal diseases based on quorum- sensing circuits

Bacterial therapy has become a key strategy against intestinal infectious diseases in recent years. Moreover, regulating the gut microbiota through traditional fecal microbiota transplantation and supplementation of probiotics faces controllability, efficacy, and safety challenges. The infiltration and emergence of synthetic biology and microbiome provide an operational and safe treatment platform for live bacterial biotherapies. Synthetic bacterial therapy can artificially manipulate bacteria to produce and deliver therapeutic drug molecules. This method has the advantages of solid controllability, low toxicity, strong therapeutic effects, and easy operation. As an essential tool for dynamic regulation in synthetic biology, quorum sensing (QS) has been widely used for designing complex genetic circuits to control the behavior of bacterial populations and achieve predefined goals. Therefore, QS-based synthetic bacterial therapy might become a new direction for the treatment of diseases. The pre-programmed QS genetic circuit can achieve a controllable production of therapeutic drugs on particular ecological niches by sensing specific signals released from the digestive system in pathological conditions, thereby realizing the integration of diagnosis and treatment. Based on this as well as the modular idea of synthetic biology, QS-based synthetic bacterial therapies are divided into an environmental signal sensing module (senses gut disease physiological signals), a therapeutic molecule producing module (plays a therapeutic role against diseases), and a population behavior regulating module (QS system). This review article summarized the structure and function of these three modules and discussed the rational design of QS gene circuits as a novel intervention strategy for intestinal diseases. Moreover, the application prospects of QS-based synthetic bacterial therapy were summarized. Finally, the challenges faced by these methods were analyzed to make the targeted recommendations for developing a successful therapeutic strategy for intestinal diseases.

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.

Accurate characterization of dynamic microbial gene expression and growth rate profiles

Genetic circuits are subject to variability due to cellular and compositional contexts. Cells face changing internal states and environments, the cellular context, to which they sense and respond by changing their gene expression and growth rates. Furthermore, each gene in a genetic circuit operates in a compositional context of genes which may interact with each other and the host cell in complex ways. The context of genetic circuits can, therefore, change gene expression and growth rates, and measuring their dynamics is essential to understanding natural and synthetic regulatory networks that give rise to functional phenotypes. However, reconstruction of microbial gene expression and growth rate profiles from typical noisy measurements of cell populations is difficult due to the effects of noise at low cell densities among other factors. We present here a method for the estimation of dynamic microbial gene expression rates and growth rates from noisy measurement data. Compared to the current state-of-the-art, our method significantly reduced the mean squared error of reconstructions from simulated data of growth and gene expression rates, improving the estimation of timing and magnitude of relevant shapes of profiles. We applied our method to characterize a triple-reporter plasmid library combining multiple transcription units in different compositional and cellular contexts in Escherichia coli. Our analysis reveals cellular and compositional context effects on microbial growth and gene expression rate dynamics and suggests a method for the dynamic ratiometric characterization of constitutive promoters relative to an in vivo reference.

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.

Emergence of dynamic properties in network hypermotifs

Networks are fundamental for our understanding of complex systems. The study of networks has uncovered common principles that underlie the behavior of vastly different fields of study, including physics, biology, sociology, and engineering. One of these common principles is the existence of network motifs-small recurrent patterns that can provide certain features that are important for the specific network. However, it remains unclear how network motifs are joined in real networks to make larger circuits and what properties emerge from interactions between network motifs. Here, we develop a framework to explore the mesoscale-level behavior of complex networks. Considering network motifs as hypernodes, we define the rules for their interaction at the network's next level of organization. We develop a method to infer the favorable arrangements of interactions between network motifs into hypermotifs from real evolved and designed network data. We mathematically explore the emergent properties of these higher-order circuits and their relations to the properties of the individual minimal circuit components they combine. We apply this framework to biological, neuronal, social, linguistic, and electronic networks and find that network motifs are not randomly distributed in real networks but are combined in a way that both maintains autonomy and generates emergent properties. This framework provides a basis for exploring the mesoscale structure and behavior of complex systems where it can be used to reveal intermediate patterns in complex networks and to identify specific nodes and links in the network that are the key drivers of the network's emergent properties.

A microfluidic optimal experimental design platform for forward design of cell-free genetic networks

Cell-free protein synthesis has been widely used as a “breadboard” for design of synthetic genetic networks. However, due to a severe lack of modularity, forward engineering of genetic networks remains challenging. Here, we demonstrate how a combination of optimal experimental design and microfluidics allows us to devise dynamic cell-free gene expression experiments providing maximum information content for subsequent non-linear model identification. Importantly, we reveal that applying this methodology to a library of genetic circuits, that share common elements, further increases the information content of the data resulting in higher accuracy of model parameters. To show modularity of model parameters, we design a pulse decoder and bistable switch, and predict their behaviour both qualitatively and quantitatively. Finally, we update the parameter database and indicate that network topology affects parameter estimation accuracy. Utilizing our methodology provides us with more accurate model parameters, a necessity for forward engineering of complex genetic networks.

Characterization of cell-free genetic networks is inherently difficult. Here the authors use optimal experimental design and microfluidics to improve characterization, demonstrating modularity and predictability of parts in applied test cases.

Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles

Engineered signaling networks can impart cells with new functionalities useful for directing differentiation and actuating cellular therapies. For such applications, the engineered networks must be tunable, precisely regulate target gene expression, and be robust to perturbations within the complex context of mammalian cells. Here, we use bacterial two-component signaling proteins to develop synthetic phosphoregulation devices that exhibit these properties in mammalian cells. First, we engineer a synthetic covalent modification cycle based on kinase and phosphatase proteins derived from the bifunctional histidine kinase EnvZ, enabling analog tuning of gene expression via its response regulator OmpR. By regulating phosphatase expression with endogenous miRNAs, we demonstrate cell-type specific signaling responses and a new strategy for accurate cell type classification. Finally, we implement a tunable negative feedback controller via a small molecule-stabilized phosphatase, reducing output expression variance and mitigating the context-dependent effects of off-target regulation and resource competition. Our work lays the foundation for establishing tunable, precise, and robust control over cell behavior with synthetic signaling networks.

Modular, robust and extendible multicellular circuit design in yeast

Division of labor between cells is ubiquitous in biology but the use of multi-cellular consortia for engineering applications is only beginning to be explored. A significant advantage of multi-cellular circuits is their potential to be modular with respect to composition but this claim has not yet been extensively tested using experiments and quantitative modeling. Here, we construct a library of 24 yeast strains capable of sending, receiving or responding to three molecular signals, characterize them experimentally and build quantitative models of their input-output relationships. We then compose these strains into two- and three-strain cascades as well as a four-strain bistable switch and show that experimentally measured consortia dynamics can be predicted from the models of the constituent parts. To further explore the achievable range of behaviors, we perform a fully automated computational search over all two-, three- and four-strain consortia to identify combinations that realize target behaviors including logic gates, band-pass filters and time pulses. Strain combinations that are predicted to map onto a target behavior are further computationally optimized and then experimentally tested. Experiments closely track computational predictions. The high reliability of these model descriptions further strengthens the feasibility and highlights the potential for distributed computing in synthetic biology.

RNA Compensation: A Positive Feedback Insulation Strategy for RNA-Based Transcription Networks

The lack of signaling modularity of biomolecular systems poses major challenges toward engineering complex networks. Directional signaling between an upstream and a downstream circuit requires the presence of binding events, which result in the consumption of regulatory molecules and can compromise the operation of the upstream circuit. This issue has been previously addressed by introducing insulation strategies that include high-gain negative feedback and activation-deactivation reaction cycles. In this paper, we focus on RNA-based circuits and propose a new positive-feedback strategy to mitigate signal consumption that we propose occurs for each regulatory event due to irreversible binding of the RNA input to the RNA target. To mitigate this, an extra RNA input is added in tandem with transcription output to compensate the RNA consumption, leading to concentration robustness of the input RNA molecule regardless of the amount of downstream modules. We term this strategy RNA compensation, and it can be applied to systems that have a stringent input-output gain, such as Small Transcription Activating RNAs (STARs). Our theoretical analysis shows that RNA compensation not only eliminates the signaling consumption in individual STAR-based regulators, but also improves the composability of STAR cascades and the modularity of RNA bistable systems.

Design, mutate, screen: Multiplexed creation and arrayed screening of synchronized genetic clocks

A major goal in synthetic biology is coordinating cellular behavior using cell-cell interactions; however, designing and testing complex genetic circuits that function only in large populations remains challenging. Although directed evolution has commonly supplemented rational design methods for synthetic gene circuits, this method relies on the efficient screening of mutant libraries for desired phenotypes. Recently, multiple techniques have been developed for identifying dynamic phenotypes from large, pooled libraries. These technologies have advanced library screening for single-cell, time-varying phenotypes but are currently incompatible with population-level phenotypes dependent on cell-cell communication. Here, we utilize directed mutagenesis and multiplexed microfluidics to develop an arrayed-screening workflow for dynamic, population-level genetic circuits. Specifically, we create a mutant library of an existing oscillator, the synchronized lysis circuit, and discover variants with different period-amplitude characteristics. Lastly, we utilize our screening workflow to construct a transcriptionally regulated synchronized oscillator that functions over long timescales. A record of this paper's transparent peer review process is included in the supplemental information.

Double-edged role of resource competition in gene expression noise and control

Despite extensive investigation demonstrating that resource competition can significantly alter the deterministic behaviors of synthetic gene circuits, it remains unclear how resource competition contributes to the gene expression noise and how this noise can be controlled. Utilizing a two-gene circuit as a prototypical system, we uncover a surprising double-edged role of resource competition in gene expression noise: competition decreases noise through introducing a resource constraint but generates its own type of noise which we name as "resource competitive noise." Utilization of orthogonal resources enables retainment of the noise reduction conferred by resource constraint while removing the added resource competitive noise. The noise reduction effects are studied using three negative feedback types: negatively competitive regulation (NCR), local, and global controllers, each having four placement architectures in the protein biosynthesis pathway (mRNA or protein inhibition on transcription or translation). Our results show that both local and NCR controllers with mRNA-mediated inhibition are efficacious at reducing noise, with NCR controllers demonstrating a superior noise-reduction capability. We also find that combining feedback controllers with orthogonal resources can improve the local controllers. This work provides deep insights into the origin of stochasticity in gene circuits with resource competition and guidance for developing effective noise control strategies.

Predicting Composition of Genetic Circuits with Resource Competition: Demand and Sensitivity

The design of genetic circuits typically relies on characterization of constituent modules in isolation to predict the behavior of modules' composition. However, it has been shown that the behavior of a genetic module changes when other modules are in the cell due to competition for shared resources. In order to engineer multimodule circuits that behave as intended, it is thus necessary to predict changes in the behavior of a genetic module when other modules load cellular resources. Here, we introduce two characteristics of circuit modules: the demand for cellular resources and the sensitivity to resource loading. When both are known for every genetic module in a circuit library, they can be used to predict any module's behavior upon addition of any other module to the cell. We develop an experimental approach to measure both characteristics for any circuit module using a resource sensor module. Using the measured resource demand and sensitivity for each module in a library, the outputs of the modules can be accurately predicted when they are inserted in the cell in arbitrary combinations. These resource competition characteristics may be used to inform the design of genetic circuits that perform as predicted despite resource competition.

Emergent Bistable Switches from the Incoherent Feed-Forward Signaling of a Positive Feedback Loop

Bistability is intrinsically connected to various decision making processes in living systems. The operating principles of a bistable switch, generated from a positive feedback loop, are well understood both in natural and synthetic settings. However, the fate of dynamic modularity of a positive feedback loop is unknown when it is connected to another dynamically modular signaling motif. In order to address this, here we investigate feed-forward signaling of a positive feedback loop to determine the fate of a bistable switch under such signaling. Using the potential energy based high-throughput bifurcation analysis method, we uncover that in addition to the conventional bistability the hybrid motifs generate various emergent bistable switches, namely mushroom and isola switches, which are not produced by the individual motifs. Using random parameter sampling, network perturbation, and phase plane analysis, we establish the design principles of such emergent behaviors. Incoherent feed-forward signaling of a positive feedback loop with distinct regulatory thresholds of the two arms of the feed-forward loop are the key requirements for such emergent behaviors. Our calculations show that the specific types of atypical bistable responses depend on the logic gate configuration of the signals. However, the emergent bistable behaviors of the hybrid networks do not depend on the nature of the positive feedback loop.

A plasmid toolbox for controlled gene expression across the Proteobacteria

Controlled gene expression is fundamental for the study of gene function and our ability to engineer bacteria. However, there is currently no easy-to-use genetics toolbox that enables controlled gene expression in a wide range of diverse species. To facilitate the development of genetics systems in a fast, easy, and standardized manner, we constructed and tested a plasmid assembly toolbox that will enable the identification of well-regulated promoters in many Proteobacteria and potentially beyond. Each plasmid is composed of four categories of genetic parts (i) the origin of replication, (ii) resistance marker, (iii) promoter-regulator and (iv) reporter. The plasmids can be efficiently assembled using ligation-independent cloning, and any gene of interest can be easily inserted in place of the reporter. We tested this toolbox in nine different Proteobacteria and identified regulated promoters with over fifty-fold induction range in eight of these bacteria. We also constructed variant libraries that enabled the identification of promoter-regulators with varied expression levels and increased inducible fold change relative to the original promoter. A selection of over 50 plasmids, which contain all of the toolbox's genetic parts, are available for community use and will enable easy construction and testing of genetics systems in both model and non-model bacteria.

Context-aware synthetic biology by controller design: Engineering the mammalian cell

The rise of systems biology has ushered a new paradigm: the view of the cell as a system that processes environmental inputs to drive phenotypic outputs. Synthetic biology provides a complementary approach, allowing us to program cell behavior through the addition of synthetic genetic devices into the cellular processor. These devices, and the complex genetic circuits they compose, are engineered using a design-prototype-test cycle, allowing for predictable device performance to be achieved in a context-dependent manner. Within mammalian cells, context effects impact device performance at multiple scales, including the genetic, cellular, and extracellular levels. In order for synthetic genetic devices to achieve predictable behaviors, approaches to overcome context dependence are necessary. Here, we describe control systems approaches for achieving context-aware devices that are robust to context effects. We then consider cell fate programing as a case study to explore the potential impact of context-aware devices for regenerative medicine applications.

CRISPR-Based Synthetic Transcription Factors In Vivo: The Future of Therapeutic Cellular Programming

Pinpoint control over endogenous gene expression in vivo has long been a fevered dream for clinicians and researchers alike. With the recent repurposing of programmable, RNA-guided DNA endonucleases from the CRISPR bacterial immune system, this dream is becoming a powerful reality. Engineered CRISPR/Cas9-based transcriptional regulators and epigenome editors have enabled researchers to perturb endogenous gene expression in vivo, allowing for the therapeutic reprogramming of cell and tissue behavior. For this technology to be of maximal use, a variety of technological hurdles still need to be addressed. Better understanding of the design principle controlling gene expression together with technologies that enable spatiotemporal control of transcriptional engineering are fundamental for rational design, improved efficacy, and ultimately safe translation to humans. In this review, we will discuss recent advances and integrative strategies that can help pave the path toward a new class of transcriptional therapeutics.

Emergent Damped Oscillation Induced by Nutrient-Modulating Growth Feedback

Growth feedback, the inherent coupling between the synthetic gene circuit and the host cell growth, could significantly change the circuit behaviors. Previously, a diverse array of emergent behaviors, such as growth bistability, enhanced ultrasensitivity, and topology-dependent memory loss, were reported to be induced by growth feedback. However, the influence of the growth feedback on the circuit functions remains underexplored. Here, we reported an unexpected damped oscillatory behavior of a self-activation gene circuit induced by nutrient-modulating growth feedback. Specifically, after dilution of the activated self-activation switch into the fresh medium with moderate nutrients, its gene expression first decreases as the cell grows and then shows a significant overshoot before it reaches the steady state, leading to damped oscillation dynamics. Fitting the data with a coarse-grained model suggests a nonmonotonic growth-rate regulation on gene production rate. The underlying mechanism of the oscillation was demonstrated by a molecular mathematical model, which includes the ribosome allocation toward gene production, cell growth, and cell maintenance. Interestingly, the model predicted a counterintuitive dependence of oscillation amplitude on the nutrition level, where the highest peak was found in the medium with moderate nutrients, but was not observed in rich nutrients. We experimentally verified this prediction by tuning the nutrient level in the culture medium. We did not observe significant oscillatory behavior for the toggle switch, suggesting that the emergence of damped oscillatory behavior depends on circuit network topology. Our results demonstrated a new nonlinear emergent behavior mediated by growth feedback, which depends on the ribosome allocation between gene circuit and cell growth.

Joint Universal Modular Plasmids: A Flexible Platform for Golden Gate Assembly in Any Microbial Host

Modular cloning standards based on Golden Gate DNA assembly allow for construction of complex DNA constructs over several rounds of assembly. Despite being reliable and automation-friendly, each standard uses a specific set of vectors, requiring researchers to generate new tool kits for novel hosts and cloning applications. JUMP vectors (Valenzuela-Ortega and French, bioRxiv 799585, 2019) combine the robustness of modular cloning standards with the Standard European Vector Architecture and a flexible design that allows researchers to easily modify the vector backbone via secondary cloning sites. This flexibility allows for JUMP vectors to be used in a wide variety of applications and hosts.

Future trends in synthetic biology in Asia

Synthetic biology research and technology translation has garnered increasing interest from the governments and private investors in Asia, where the technology has great potential in driving a sustainable bio-based economy. This Perspective reviews the latest developments in the key enabling technologies of synthetic biology and its application in bio-manufacturing, medicine, food and agriculture in Asia. Asia-centric strengths in synthetic biology to grow the bio-based economy, such as advances in genome editing and the presence of biofoundries combined with the availability of natural resources and vast markets, are also highlighted. The potential barriers to the sustainable development of the field, including inadequate infrastructure and policies, with suggestions to overcome these by building public-private partnerships, more effective multi-lateral collaborations and well-developed governance framework, are presented. Finally, the roles of technology, education and regulation in mitigating potential biosecurity risks are examined. Through these discussions, stakeholders from different groups, including academia, industry and government, are expectantly better positioned to contribute towards the establishment of innovation and bio-economy hubs in Asia.

Winner-takes-all resource competition redirects cascading cell fate transitions

Failure of modularity remains a significant challenge for assembling synthetic gene circuits with tested modules as they often do not function as expected. Competition over shared limited gene expression resources is a crucial underlying reason. It was reported that resource competition makes two seemingly separate genes connect in a graded linear manner. Here we unveil nonlinear resource competition within synthetic gene circuits. We first build a synthetic cascading bistable switches (Syn-CBS) circuit in a single strain with two coupled self-activation modules to achieve two successive cell fate transitions. Interestingly, we find that the in vivo transition path was redirected as the activation of one switch always prevails against the other, contrary to the theoretically expected coactivation. This qualitatively different type of resource competition between the two modules follows a ‘winner-takes-all’ rule, where the winner is determined by the relative connection strength between the modules. To decouple the resource competition, we construct a two-strain circuit, which achieves successive activation and stable coactivation of the two switches. These results illustrate that a highly nonlinear hidden interaction between the circuit modules due to resource competition may cause counterintuitive consequences on circuit functions, which can be controlled with a division of labor strategy.

Synthetic gene circuits may not function as expected due to the resource competition between modules. Here the authors build cascading bistable switches to achieve two successive cell fate transitions but found a ‘winner-takes-all’ behaviour, which is overcome by a division of labour strategy.

Joint universal modular plasmids (JUMP): a flexible vector platform for synthetic biology

Generation of new DNA constructs is an essential process in modern life science and biotechnology. Modular cloning systems based on Golden Gate cloning, using Type IIS restriction endonucleases, allow assembly of complex multipart constructs from reusable basic DNA parts in a rapid, reliable and automation-friendly way. Many such toolkits are available, with varying degrees of compatibility, most of which are aimed at specific host organisms. Here, we present a vector design which allows simple vector modification by using modular cloning to assemble and add new functions in secondary sites flanking the main insertion site (used for conventional modular cloning). Assembly in all sites is compatible with the PhytoBricks standard, and vectors are compatible with the Standard European Vector Architecture (SEVA) as well as BioBricks. We demonstrate that this facilitates the construction of vectors with tailored functions and simplifies the workflow for generating libraries of constructs with common elements. We have made available a collection of vectors with 10 different microbial replication origins, varying in copy number and host range, and allowing chromosomal integration, as well as a selection of commonly used basic parts. This design expands the range of hosts which can be easily modified by modular cloning and acts as a toolkit which can be used to facilitate the generation of new toolkits with specific functions required for targeting further hosts.

Using a Design of Experiments Approach to Inform the Design of Hybrid Synthetic Yeast Promoters

Hybrid promoter engineering takes advantage of the modular nature of eukaryotic promoters by combining discrete promoter motifs to confer novel regulatory function. By combinatorially screening sequence libraries for trans-acting transcriptional operators, activators, repressors and core promoter sequences, it is possible to derive constitutive or inducible promoter collections covering a broad range of expression strengths. However, combinatorial approaches to promoter design can result in highly complex, multidimensional design spaces, which can be experimentally costly to thoroughly explore in vivo. Here, we describe an in silico pipeline for the design of hybrid promoter libraries that employs a Design of Experiments (DoE) approach to reduce experimental burden and efficiently explore the promoter fitness landscape. We also describe a software pipeline to ensure that the designed promoter sequences are compatible with the YTK assembly standard.

An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells

Synthetic biology has the potential to bring forth advanced genetic devices for applications in healthcare and biotechnology. However, accurately predicting the behavior of engineered genetic devices remains difficult due to lack of modularity, wherein a device's output does not depend only on its intended inputs but also on its context. One contributor to lack of modularity is loading of transcriptional and translational resources, which can induce coupling among otherwise independently-regulated genes. Here, we quantify the effects of resource loading in engineered mammalian genetic systems and develop an endoribonuclease-based feedforward controller that can adapt the expression level of a gene of interest to significant resource loading in mammalian cells. Near-perfect adaptation to resource loads is facilitated by high production and catalytic rates of the endoribonuclease. Our design is portable across cell lines and enables predictable tuning of controller function. Ultimately, our controller is a general-purpose device for predictable, robust, and context-independent control of gene expression.

Synthesizing AND gate minigene circuits based on CRISPReader for identification of bladder cancer cells

The logical AND gate gene circuit based on the CRISPR-Cas9 system can distinguish bladder cancer cells from normal bladder epithelial cells. However, the layered artificial gene circuits have the problems of high complexity, difficulty in accurately predicting the behavior, and excessive redundancy, which cannot be applied to clinical translation. Here, we construct minigene circuits based on the CRISPReader, a technology used to control promoter-less gene expression in a robust manner. The minigene circuits significantly induce robust gene expression output in bladder cancer cells, but have nearly undetectable gene expression in normal bladder epithelial cells. The minigene circuits show a higher capability for cancer identification and intervention when compared with traditional gene circuits, and could be used for in vivo cancer gene therapy using the all-in-one AAV vector. This approach expands the design ideas and concepts of gene circuits in medical synthetic biology.

Guidelines for designing the antithetic feedback motif

Integral feedback control is commonly used in mechanical and electrical systems to achieve zero steady-state error following an external disturbance. Equivalently, in biological systems, a property known as robust perfect adaptation guarantees robustness to environmental perturbations and return to the pre-disturbance state. Previously, Briat et al proposed a biomolecular design for integral feedback control (robust perfect adaptation) called the antithetic feedback motif. The antithetic feedback controller uses the sequestration binding reaction of two biochemical species to record the integral of the error between the current and the desired output of the network it controls. The antithetic feedback motif has been successfully built using synthetic components in vivo in Escherichia coli and Saccharomyces cerevisiae cells. However, these previous synthetic implementations of antithetic feedback have not produced perfect integral feedback control due to the degradation and dilution of the two controller species. Furthermore, previous theoretical results have cautioned that integral control can only be achieved under stability conditions that not all antithetic feedback motifs necessarily fulfill. In this paper, we study how to design antithetic feedback motifs that simultaneously achieve good stability and small steady-state error properties, even as the controller species are degraded and diluted. We provide simple tuning guidelines to achieve flexible and practical synthetic biological implementations of antithetic feedback control. We use several tools and metrics from control theory to design antithetic feedback networks, paving the path for the systematic design of synthetic biological controllers.

Media Robustness and Scalability of Phosphate Regulated Promoters Useful for Two-Stage Autoinduction in E. coli

A key challenge in synthetic biology is the successful utilization of characterized parts, such as promoters, in different biological contexts. We report the evaluation of the media robustness of a small library of E. coli PhoB regulated promoters that enable heterologous protein production in two-stage cultures. Expression levels were measured both in a rich Autoinduction Broth as well as a minimal mineral salts media. Expression was both media and promoter dependent. Of the 16 promoters tested, 4 were identified to have tightly controlled expression, which was also robust to media formulation. Improved promoter robustness led to more predictable scale up and consistent expression in instrumented bioreactors. This subset of PhoB activated promoters, useful for two-stage autoinduction, highlights the impact of the environment on the performance of biological parts and the importance of robustness testing in synthetic biology.

Microfluidic platforms for the dynamic characterisation of synthetic circuitry

Generating novel functionality from well characterised synthetic parts and modules lies at the heart of synthetic biology. Ideally, circuitry is rationally designed in silico with quantitatively predictive models to predetermined design specifications. Synthetic circuits are intrinsically stochastic, often dynamically modulated and set in a dynamic fluctuating environment within a living cell. To build more complex circuits and to gain insight into context effects, intrinsic noise and transient performance, characterisation techniques that resolve both heterogeneity and dynamics are required. Here we review recent advances in both in vitro and in vivo microfluidic technologies that are suitable for the characterisation of synthetic circuitry, modules and parts.

Heterogeneity coordinates bacterial multi-gene expression in single cells

For a genetically identical microbial population, multi-gene expression in various environments requires effective allocation of limited resources and precise control of heterogeneity among individual cells. However, it is unclear how resource allocation and cell-to-cell variation jointly shape the overall performance. Here we demonstrate a Simpson’s paradox during overexpression of multiple genes: two competing proteins in single cells correlated positively for every induction condition, but the overall correlation was negative. Yet this phenomenon was not observed between two competing mRNAs in single cells. Our analytical framework shows that the phenomenon arises from competition for translational resource, with the correlation modulated by both mRNA and ribosome variability. Thus, heterogeneity plays a key role in single-cell multi-gene expression and provides the population with an evolutionary advantage, as demonstrated in this study.

Microbes perform multitasking for a wide range of purposes, including survival, adaptation, colonization, and evolution. Both modelling and experimental results at the ensemble level reveal trade-offs between different tasks due to resource competition, but it is unclear how single cells allocate limited intracellular resources to perform multitasking, and how does a population coordinate single cell performances during multitasking to maximize population efficiencies. In this study, we address this question by using bacterial multi-gene overexpression as the basic form of multitasking. We discovered and analyzed a statistical phenomenon called Simpson’s paradox, where competing proteins in single cells correlate positively at each constant condition, although the proteins correlate negatively when all conditions are combined. We demonstrate that the phenomenon arises from competition for translational resources, with the correlation modulated by heterogeneity of both mRNA and ribosomes. We further show that heterogeneity coordinates multiple functional modules, conferring an evolutionary advantage on the population. Our work discloses that heterogeneity in the form of Simpson’s paradox is an important phenomenon in coordinating multi-gene expression.

Design, Fabrication, and Device Chemistry of a 3-Input-3-Output Synthetic Genetic Combinatorial Logic Circuit with a 3-Input AND Gate in a Single Bacterial Cell

Advancement of in-cell molecular computation requires multi-input-multi-output genetic logic devices. However, increased physical size, a higher number of molecular interactions, cross-talk, and complex systems level device chemistry limited the realization of such multi-input-multi-output devices in a single bacterial cell. Here, by adapting a circuit minimization and conjugated promoter engineering approach, we created the first 3-input-3-output logic function in a single bacterial cell. The circuit integrated three extracellular chemical signals as inputs and produced three different fluorescent proteins as outputs following the truth table of the circuit. First, we created a noncascaded 1-gate-3-input synthetic genetic AND gate in bacteria. We showed that the 3-input AND gate was digital in nature and mathematically predictable, two important characteristics, which were not reported for previous 3-input AND gates in bacteria. Our design consists of a 128 bp DNA scaffold, which conjugated various protein-binding sites in a single piece of DNA and worked as a hybrid promoter. The scaffold was a few times smaller than the similar 3-input synthetic genetic AND gate promoter reported. Integrating this AND gate with a new 2-input-2-output integrated circuit, which was also digital-like and predictive, we created a 3-input-3-output combinatorial logic circuit. This work demonstrated the integration of a 3-input AND gate in a larger circuit and a 3-input-3-output synthetic genetic circuit, both for the first time. The work has significance in molecular computation, biorobotics, DNA nanotechnology, and synthetic biology.

Rapid generation of sequence-diverse terminator libraries and their parameterization using quantitative Term-Seq

Synthetic biology and the rational design and construction of biological devices require vast numbers of characterized biological parts, as well as reliable design tools to build increasingly complex, multigene architectures. Design principles for intrinsic terminators have been established; however, additional sequence-structure studies are needed to refine parameters for termination-based genetic devices. We report a rapid single-pot method to generate libraries of thousands of randomized bidirectional intrinsic terminators and a modified quantitative Term-Seq (qTerm-Seq) method to simultaneously identify terminator sequences and measure their termination efficiencies (TEs). Using qTerm-Seq, we characterize hundreds of additional strong terminators (TE > 90%) with some terminators reducing transcription read-through by up to 1000-fold in Escherichia coli. Our terminator library and qTerm-Seq pipeline constitute a flexible platform enabling identification of terminator parts that can achieve transcription termination not only over a desired range but also to investigate their sequence-structure features, including for specific genetic and application contexts beyond the common in vivo systems such as E. coli.

Characterization of Gene Circuit Parts Based on Multiobjective Optimization by Using Standard Calibrated Measurements

Standardization and characterization of biological parts is necessary for the further development of bottom-up synthetic biology. Herein, an easy-to-use methodology that embodies both a calibration procedure and a multiobjective optimization approach is proposed to characterize biological parts. The calibration procedure generates values for specific fluorescence per cell expressed as standard units of molecules of equivalent fluorescein per particle. The use of absolute standard units enhances the characterization of model parameters for biological parts by bringing measurements and estimations results from different sources into a common domain, so they can be integrated and compared faithfully. The multiobjective optimization procedure exploits these concepts by estimating the values of the model parameters, which represent biological parts of interest, while considering a varied range of experimental and circuit contexts. Thus, multiobjective optimization provides a robust characterization of them. The proposed calibration and characterization methodology can be used as a guide for good practices in dry and wet laboratories; thus allowing not only portability between models, but is also useful for generating libraries of tested and well-characterized biological parts.

Engineering orthogonal synthetic timer circuits based on extracytoplasmic function σ factors

The rational design of synthetic regulatory circuits critically hinges on the availability of orthogonal and well-characterized building blocks. Here, we focus on extracytoplasmic function (ECF) σ factors, which are the largest group of alternative σ factors and hold extensive potential as synthetic orthogonal regulators. By assembling multiple ECF σ factors into regulatory cascades of varying length, we benchmark the scalability of the approach, showing that these ‘autonomous timer circuits’ feature a tuneable time delay between inducer addition and target gene activation. The implementation of similar timers in Escherichia coli and Bacillus subtilis shows strikingly convergent circuit behavior, which can be rationalized by a computational model. These findings not only reveal ECF σ factors as powerful building blocks for a rational, multi-layered circuit design, but also suggest that ECF σ factors are universally applicable as orthogonal regulators in a variety of bacterial species.

Synthetic negative feedback circuits using engineered small RNAs

Negative feedback is known to enable biological and man-made systems to perform reliably in the face of uncertainties and disturbances. To date, synthetic biological feedback circuits have primarily relied upon protein-based, transcriptional regulation to control circuit output. Small RNAs (sRNAs) are non-coding RNA molecules that can inhibit translation of target messenger RNAs (mRNAs). In this work, we modelled, built and validated two synthetic negative feedback circuits that use rationally-designed sRNAs for the first time. The first circuit builds upon the well characterised tet-based autorepressor, incorporating an externally-inducible sRNA to tune the effective feedback strength. This allows more precise fine-tuning of the circuit output in contrast to the sigmoidal, steep input–output response of the autorepressor alone. In the second circuit, the output is a transcription factor that induces expression of an sRNA, which inhibits translation of the mRNA encoding the output, creating direct, closed-loop, negative feedback. Analysis of the noise profiles of both circuits showed that the use of sRNAs did not result in large increases in noise. Stochastic and deterministic modelling of both circuits agreed well with experimental data. Finally, simulations using fitted parameters allowed dynamic attributes of each circuit such as response time and disturbance rejection to be investigated.

A quasi-integral controller for adaptation of genetic modules to variable ribosome demand

The behavior of genetic circuits is often poorly predictable. A gene’s expression level is not only determined by the intended regulators, but also affected by changes in ribosome availability imparted by expression of other genes. Here we design a quasi-integral biomolecular feedback controller that enables the expression level of any gene of interest (GOI) to adapt to changes in available ribosomes. The feedback is implemented through a synthetic small RNA (sRNA) that silences the GOI’s mRNA, and uses orthogonal extracytoplasmic function (ECF) sigma factor to sense the GOI’s translation and to actuate sRNA transcription. Without the controller, the expression level of the GOI is reduced by 50% when a resource competitor is activated. With the controller, by contrast, gene expression level is practically unaffected by the competitor. This feedback controller allows adaptation of genetic modules to variable ribosome demand and thus aids modular construction of complicated circuits.

Competition for shared cellular resources often renders genetic circuits poorly predictable. Here the authors design a biomolecular quasi-integral controller that allows gene expression to adapt to variable demand in translation resources.

Quantitative and synthetic biology approaches to combat bacterial pathogens

Antibiotic resistance is one of the biggest threats to public health. The rapid emergence of resistant bacterial pathogens endangers the efficacy of current antibiotics and has led to increasing mortality and economic burden. This crisis calls for more rapid and accurate diagnosis to detect and identify pathogens, as well as to characterize their response to antibiotics. Building on this foundation, treatment options also need to be improved to use current antibiotics more effectively and develop alternative strategies that complement the use of antibiotics. We here review recent developments in diagnosis and treatment of bacterial pathogens with a focus on quantitative biology and synthetic biology approaches.

Engineering Diagnostic and Therapeutic Gut Bacteria

Genetically engineered bacteria have the potential to diagnose and treat a wide range of diseases linked to the gastrointestinal tract, or gut. Such engineered microbes will be less expensive and invasive than current diagnostics and more effective and safe than current therapeutics. Recent advances in synthetic biology have dramatically improved the reliability with which bacteria can be engineered with the sensors, genetic circuits, and output (actuator) genes necessary for diagnostic and therapeutic functions. However, to deploy such bacteria in vivo, researchers must identify appropriate gut-adapted strains and consider performance metrics such as sensor detection thresholds, circuit computation speed, growth rate effects, and the evolutionary stability of engineered genetic systems. Other recent reviews have focused on engineering bacteria to target cancer or genetically modifying the endogenous gut microbiota in situ. Here, we develop a standard approach for engineering "smart probiotics," which both diagnose and treat disease, as well as "diagnostic gut bacteria" and "drug factory probiotics," which perform only the former and latter function, respectively. We focus on the use of cutting-edge synthetic biology tools, gut-specific design considerations, and current and future engineering challenges.

Impact of negative feedback in metabolic noise propagation

Synthetic biology combines different branches of biology and engineering aimed at designing synthetic biological circuits able to replicate emergent properties useful for the biotechnology industry, human health and environment. The role of negative feedback in noise propagation for a basic enzymatic reaction scheme is investigated. Two feedback control schemes on enzyme expression are considered: one from the final product of the pathway activity, the other from the enzyme accumulation. Both schemes are designed to provide the same steady-state average values of the involved players, in order to evaluate the feedback performances according to the same working mode. Computations are carried out numerically and analytically, the latter allowing to infer information on which model parameter setting leads to a more efficient noise attenuation, according to the chosen scheme. In addition to highlighting the role of the feedback in providing a substantial noise reduction, our investigation concludes that the effect of feedback is enhanced by increasing the promoter sensitivity for both schemes. A further interesting biological insight is that an increase in the promoter sensitivity provides more benefits to the feedback from the product with respect to the feedback from the enzyme, in terms of enlarging the parameter design space.

Programming gene and engineered-cell therapies with synthetic biology

Gene and engineered-cell therapies promise to treat diseases by genetically modifying cells to carry out therapeutic tasks. Although the field has had some success in treating monogenic disorders and hematological malignancies, current approaches are limited to overexpression of one or a few transgenes, constraining the diseases that can be treated with this approach and leading to potential concerns over safety and efficacy. Synthetic gene networks can regulate the dosage, timing, and localization of gene expression and therapeutic activity in response to small molecules and disease biomarkers. Such "programmable" gene and engineered-cell therapies will provide new interventions for incurable or difficult-to-treat diseases.

Cell-free prediction of protein expression costs for growing cells

Translating heterologous proteins places significant burden on host cells, consuming expression resources leading to slower cell growth and productivity. Yet predicting the cost of protein production for any given gene is a major challenge, as multiple processes and factors combine to determine translation efficiency. To enable prediction of the cost of gene expression in bacteria, we describe here a standard cell-free lysate assay that provides a relative measure of resource consumption when a protein coding sequence is expressed. These lysate measurements can then be used with a computational model of translation to predict the in vivo burden placed on growing E. coli cells for a variety of proteins of different functions and lengths. Using this approach, we can predict the burden of expressing multigene operons of different designs and differentiate between the fraction of burden related to gene expression compared to action of a metabolic pathway.

Rational engineering of synthetic microbial systems: from single cells to consortia

One promise of synthetic biology is to provide solutions for biomedical and industrial problems by rational design of added functionality in living systems. Microbes are at the forefront of this biological engineering endeavor due to their general ease of handling and their relevance in many potential applications from fermentation to therapeutics. In recent years, the field has witnessed an explosion of novel regulatory tools, from synthetic orthogonal transcription factors to posttranslational mechanisms for increased control over the behavior of synthetic circuits. Tool development has been paralleled by the discovery of principles that enable increased modularity and the management of host-circuit interactions. Engineered cell-to-cell communication bridges the scales from intracellular to population-level coordination. These developments facilitate the translation of more than a decade of circuit design into applications.

Decoupling Resource-Coupled Gene Expression in Living Cells

Synthetic biology aspires to develop frameworks that enable the construction of complex and reliable gene networks with predictable functionalities. A key limitation is that increasing network complexity increases the demand for cellular resources, potentially causing resource-associated interference among noninteracting circuits. Although recent studies have shown the effects of resource competition on circuit behaviors, mechanisms that decouple such interference remain unclear. Here, we constructed three systems in Escherichia coli, each consisting of two independent circuit modules where the complexity of one module (Circuit 2) was systematically increased while the other (Circuit 1) remained identical. By varying the expression level of Circuit 1 and measuring its effect on the expression level of Circuit 2, we demonstrated computationally and experimentally that indirect coupling between these seemingly unconnected genetic circuits can occur in three different regulatory topologies. More importantly, we experimentally verified the computational prediction that negative feedback can significantly reduce resource-coupled interference in regulatory circuits. Our results reveal a design principle that enables cells to reliably multitask while tightly controlling cellular resources.

Resource Competition Shapes the Response of Genetic Circuits

A common approach to design genetic circuits is to compose gene expression cassettes together. While appealing, this modular approach is challenged by the fact that expression of each gene depends on the availability of transcriptional/translational resources, which is in turn determined by the presence of other genes in the circuit. This raises the question of how competition for resources by different genes affects a circuit's behavior. Here, we create a library of genetic activation cascades in E. coli bacteria, where we explicitly tune the resource demand by each gene. We develop a general Hill-function-based model that incorporates resource competition effects through resource demand coefficients. These coefficients lead to nonregulatory interactions among genes that reshape the circuit's behavior. For the activation cascade, such interactions result in surprising biphasic or monotonically decreasing responses. Finally, we use resource demand coefficients to guide the choice of ribosome binding site and DNA copy number to restore the cascade's intended monotonically increasing response. Our results demonstrate how unintended circuit's behavior arises from resource competition and provide a model-guided methodology to minimize the resulting effects.