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

Degradation efficiency for phthalates and cooperative mechanism in synthetic bacterial consortium and its bioaugmentation for soil remediation

Agricultural soil contamination by phthalates (PAEs) necessitates efficient remediation strategies with microbial degradation. However, microbial cooperation of PAE-degrading consortia and their effectiveness in bioaugmentation through re-colonization remain poorly understood. In this study, synthetic PAE-degrading bacterial consortia were constructed using the 20 isolates derived from maize rhizosphere to explore microbial cooperation and bioaugmentation for PAE removal. Following optimization, five key isolates either with strong individual degradation capacity or beneficial metabolic interactions were co-cultured to form a synthetic consortium SC-5. This consortium demonstrated efficient degradation of di-n-butyl phthalate (DBP) and di-(2-ethylhexyl) phthalate (DEHP) (each at 200 mg/L), achieving 98.1 %-100 % removal percentages and reduced half-lives compared to the single strain. Metabolic pathway analysis revealed that consortium SC-5 could completely degrade PAEs and their intermediates including monoester, phthalic acid, and protocatechuate through cooperative metabolism. Within the consortium, Mycobacterium sp. R14 exhibited strong PAE-degrading ability, while genera Rhizobium and Paenarthrobacter predominated in mineral salt media supplemented with PAEs or glucose, as confirmed by high-throughput sequencing. These results underscore their differentiated utilization of parent PAEs, degradation intermediates, and metabolites through microbial cooperation. Furthermore, consortium SC-5 could effectively re-colonize maize rhizosphere with significantly higher relative abundances of the genera affiliating to the members of SC-5 than those in bulk soil, thereby significantly facilitating DEHP removal from rhizosphere. The present study highlights the importance of microbial cooperation within synthetic consortium and demonstrates its potential in bioaugmentation-based bioremediation of PAE-polluted soil.

Engineering coupled consortia-based biosensors for diagnostic

Synthetic multicellular systems have great potential for performing complex tasks, including multi-signal detection and computation through cell-to-cell communication. However, engineering these systems is challenging, requiring precise control over the cell concentrations of distinct members and coordination of their activity. Here, we develop a bacterial consortia-based biosensor for Heme and Lactate, wherein members are coupled through a global shared quorum-sensing signal that simultaneously controls the activity of the diverse biosensing strains. The multicellular system incorporates a gene circuit that computes the minimum between each biosensor's activity and the shared signal. We evaluate three consortia configurations: one where the shared signal is externally supplied, another directly produced via an inducible gene circuit, and a third generated through an incoherent feedforward loop (IFFL) gene circuit. Among these configurations, the IFFL system, which maintains the shared signal at low and stable levels over an extended period, demonstrates improved performance and robustness against perturbations in cell populations. Finally, we examine these coupled consortia to monitor Lactate and Heme in humanized fecal samples for diagnostics.

Multi-Layer Autocatalytic Feedback Enables Integral Control Amidst Resource Competition and Across Scales

Integral feedback control strategies have proven effective in regulating protein expression in unpredictable cellular environments. These strategies, grounded in model-based designs and control theory, have advanced synthetic biology applications. Autocatalytic integral feedback controllers, utilizing positive autoregulation for integral action, are one class of simplest architectures to design integrators. This class of controllers offers unique features, such as robustness against dilution effects and cellular growth, as well as the potential for synthetic realizations across different biological scales, owing to their similarity to self-regenerative behaviors widely observed in nature. Despite this, their potential has not yet been fully exploited. One key reason, we discuss, is that their effectiveness is often hindered by resource competition and context-dependent couplings. This study addresses these challenges using a multilayer feedback strategy. Our designs enabled population-level integral feedback and multicellular integrators, where the control function emerges as a property of coordinated interactions distributed across different cell populations coexisting in a multicellular consortium. We provide a generalized mathematical framework for modeling resource competition in complex genetic networks, supporting the design of intracellular control circuits. The use of our proposed multilayer autocatalytic controllers is examined in two typical control tasks that pose significant relevance to synthetic biology applications: concentration regulation and ratiometric control. We define a ratiometric control task and solve it using a variant of our controller. The effectiveness of our controller motifs is demonstrated through a range of application examples, from precise regulation of gene expression and gene ratios in embedded designs to population growth and coculture composition control in multicellular designs within engineered microbial ecosystems. These findings offer a versatile approach to achieving robust adaptation and homeostasis from subcellular to multicellular scales.

Resource competition-driven bistability and stochastic switching amplify gene expression noise

Although the impact of resource competition on the deterministic behavior of synthetic gene circuits has been studied, its effects on gene expression noise remain obscure. In this work, we systematically analyze the role of resource competition in noise propagation within a genetic inhibition cascade circuit. We found that resource competition amplifies gene expression noise by introducing unexpected bistability and stochastic switching between the two stable states. This emergent bistability, driven by resource competition-mediated double negative feedback, allows one gene to dominate expression while suppressing the other in a "winner-takes-all" behavior. Our findings highlight the critical role of resource competition in shaping the noise dynamics and its propagation, underscoring the importance of considering these effects when designing and controlling synthetic circuits.

Unlocking the full potential of plant cell-based production for valuable proteins: Challenges and innovative strategies

Plant cell-based bioproduction systems offer a promising platform for the sustainable production of valuable proteins as they provide distinctive advantages over mammalian cell culture and whole plant cultivation. However, significant technical challenges remain, including low productivity, altered efficacy of plant-derived proteins, along with issues in culture process development, such as cell clumping, genetic instability, and difficulties with cryopreservation. To date, the full production potential of this platform remains largely untapped. This review addresses these critical challenges and proposes innovative strategies to unlock the full potential of the production platform. Rather than simply revisiting past advancements or summarizing current progress, it proposes forward-thinking solutions with a particular emphasis on cellular engineering. Key strategies include designing novel protein partners to enhance recombinant protein accumulation and functionality, employing precise gene integration techniques in genome to enhance transgene transcription, implementing cutting-edge methods for screening and maintaining elite cell lines to mitigate genetic instability, and leveraging genome editing tools for cellular engineering to develop new plant cell lines optimized for bioproduction. A key focus is on cell wall engineering to develop cellulose- or pectin-deficient cell lines, facilitating modifications to the morphology of existing plant cell lines. By exploring these innovative approaches, this review aims to foster innovative thinking and inspire future research in plant cell-based bioproduction.

Model-guided gene circuit design for engineering genetically stable cell populations in diverse applications

Maintaining engineered cell populations' genetic stability is a key challenge in synthetic biology. Synthetic genetic constructs compete with a host cell's native genes for expression resources, burdening the cell and impairing its growth. This creates a selective pressure favouring mutations which alleviate this growth defect by removing synthetic gene expression. Non-functional mutants thus spread in cell populations, eventually making them lose engineered functions. Past work has attempted to limit mutation spread by coupling synthetic gene expression to survival. However, these approaches are highly context-dependent and must be tailor-made for each particular synthetic gene circuit to be retained. By contrast, we develop and analyse a biomolecular controller which depresses mutant cell growth independently of the mutated synthetic gene's identity. Modelling shows how our design can be deployed alongside various synthetic circuits without any re-engineering of its genetic components, outperforming extant gene-specific mutation spread mitigation strategies. Our controller's performance is evaluated using a novel simulation approach which leverages resource-aware cell modelling to directly link a circuit's design parameters to its population-level behaviour. Our design's adaptability promises to mitigate mutation spread in an expanded range of applications, while our analyses provide a blueprint for using resource-aware cell models in circuit design.

Partitioning of a 2-bit hash function across 66 communicating cells

Powerful distributed computing can be achieved by communicating cells that individually perform simple operations. Here, we report design software to divide a large genetic circuit across cells as well as the genetic parts to implement the subcircuits in their genomes. These tools were demonstrated using a 2-bit version of the MD5 hashing algorithm, which is an early predecessor to the cryptographic functions underlying cryptocurrency. One iteration requires 110 logic gates, which were partitioned across 66 Escherichia coli strains, requiring the introduction of a total of 1.1 Mb of recombinant DNA into their genomes. The strains were individually experimentally verified to integrate their assigned input signals, process this information correctly and propagate the result to the cell in the next layer. This work demonstrates the potential to obtain programable control of multicellular biological processes.

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.

Modeling ncRNA-Mediated Circuits in Cell Fate Decision: From Systems Biology to Synthetic Biology

Noncoding RNAs (ncRNAs) play critical roles in essential cell fate decisions. However, the exact molecular mechanisms underlying ncRNA-mediated bistable switches remain elusive and controversial. In recent years, systematic mathematical and quantitative experimental analyses have made significant contributions to elucidating the molecular mechanisms of controlling ncRNA-mediated cell fate decision processes. In this chapter, we review and summarize the general framework of mathematical modeling of ncRNA in a pedagogical way and the application of this general framework to real biological processes. We discuss the emerging properties resulting from the reciprocal regulation between mRNA, miRNA, and competing endogenous mRNA (ceRNA). We also explore the efforts within the synthetic biology approach to understand the fundamental design principles underlying cell fate decisions. Both the positive feedback loops between ncRNAs and transcription factors and the emerging properties from the miRNA-mRNA reciprocal regulation enable bistable switches to direct cell fate decisions.

Distributed opinion competition scheme with gradient-based neural network in social networks

In the context of social networks becoming primary platforms for information dissemination and public discourse, understanding how opinions compete and reach consensus has become increasingly vital. This paper introduces a novel distributed competition model designed to elucidate the dynamics of opinion competitive behavior in social networks. The proposed model captures the development mechanism of various opinions, their appeal to individuals, and the impact of the social environment on their evolution. The model reveals that a subset of opinions ultimately prevails and is adopted. Key elements of social networks are quantified as parameters, with parameter variations representing the dynamics of opinions. Furthermore, a modified gradient-based neural network is designed as the evolutional law of the opinion, whose stability and convergence are confirmed by theoretical analysis. Additionally, experiments simulate real-world competitive scenarios, demonstrating practical applications for the model. This model can be widely applied to various filed in social networks, offering a new perspective for understanding and predicting competition phenomenon in complex social systems. Overall, this work provides a structured and systematic approach to understanding opinion dynamics, which greatly enhances our ability to analyze competitive behaviors and anticipate the outcomes of diverse viewpoints in social networks.

mFusion: a multiscale fusion method bridging neuroimages to genes through neurotransmissions in mental health disorders

Mental health disorders emerge from complex interactions among neurobiological processes across multiple scales, which poses challenges in uncovering pathological pathways from molecular dysfunction to neuroimaging changes. Here, we proposed a multiscale fusion (mFusion) method to evaluate the relevance of each gene to the neuroimaging traits of mental health disorders. We combined gene-neuroimaging associations with gene-positron emission tomography (PET) and PET-neuroimaging associations using protein-protein interaction networks, where various genes traced by PET maps are involved in neurotransmission. Compared with previous methods, the proposed algorithm identified more disease genes on both simulated and empirical data sets. Applying mFusion to eight mental health disorders, we found that these disorders formed three clusters with distinct associated genes. In summary, mFusion is a promising tool of prioritizing genes for mental health disorders by establishing gene-PET-neuroimaging pathways.

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.

Bringing neural networks to life

A synthetic protein-based winner-take-all neural network controls cell fate decisions.

Ovarian tumor cells gain competitive advantage by actively reducing the cellular fitness of microenvironment cells

Cell competition and fitness comparison between cancer and tumor microenvironment (TME) cells determine oncogenic fate. Our previous study established a role for human Flower isoforms as fitness fingerprints, where the expression of Flower Win isoforms in tumor cells leads to growth advantage over TME cells expressing Lose isoforms. Here we demonstrate that the expression of Flower Lose and reduced microenvironment fitness is not a pre-existing condition but, rather, a cancer-induced phenomenon. Cancer cells actively reduce TME fitness by the exosome-mediated release of a cancer-specific long non-coding RNA, Tu-Stroma, which controls the splicing of the Flower gene in the TME cells and expression of Flower Lose isoform, which leads to reduced fitness status. This mechanism controls cancer growth, metastasis and host survival in ovarian cancer. Targeting Flower protein with humanized monoclonal antibody (mAb) in mice significantly reduces cancer growth and metastasis and improves survival. Pre-treatment with Flower mAb protects intraperitoneal organs from developing lesions despite the presence of aggressive tumor cells.

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.

Phase Separation to Resolve Growth-Related Circuit Failures

Fluctuations in host cell growth poses a significant challenge to synthetic gene circuits, often disrupting circuit function. Existing solutions typically rely on circuit redesign with alternative topologies or additional control elements, yet a broadly applicable approach remains elusive. Here, we introduce a new strategy based on liquid-liquid phase separation (LLPS) to stabilize circuit performance. By engineering a self-activating circuit with transcription factors (TF) fused to an intrinsically disordered region (IDR), we enable the formation of TF condensates at the promoter region, maintaining local TF concentration despite growth-mediated dilution. This condensate formation preserves bistable memory in the self-activating circuit, demonstrating that phase separation can robustly counteract growth fluctuations, offering a novel design principle for resilient synthetic circuits.

Effects of growth feedback on adaptive gene circuits: A dynamical understanding

The successful integration of engineered gene circuits into host cells remains a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback, where the circuit influences cell growth and vice versa. Understanding the dynamics of circuit failures and identifying topologies resilient to growth feedback are crucial for both fundamental and applied research. Utilizing transcriptional regulation circuits with adaptation as a paradigm, we systematically study more than four hundred topological structures and uncover various categories of failures. Three dynamical mechanisms of circuit failures are identified: continuous deformation of the response curve, strengthened or induced oscillations, and sudden switching to coexisting attractors. Our extensive computations also uncover a scaling law between a circuit robustness measure and the strength of growth feedback. Despite the negative effects of growth feedback on the majority of circuit topologies, we identify several circuits that maintain optimal performance as designed, a feature important for applications.

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

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.

Programming Dynamic Division of Labor Using Horizontal Gene Transfer

The metabolic engineering of microbes has broad applications, including biomanufacturing, bioprocessing, and environmental remediation. The introduction of a complex, multistep pathway often imposes a substantial metabolic burden on the host cell, restraining the accumulation of productive biomass and limiting pathway efficiency. One strategy to alleviate metabolic burden is the division of labor (DOL) in which different subpopulations carry out different parts of the pathway and work together to convert a substrate into a final product. However, the maintenance of different engineered subpopulations is challenging due to competition and convoluted interstrain population dynamics. Through modeling, we show that dynamic division of labor (DDOL), which we define as the DOL between indiscrete populations capable of dynamic and reversible interchange, can overcome these limitations and enable the robust maintenance of burdensome, multistep pathways. We propose that DDOL can be mediated by horizontal gene transfer (HGT) and use plasmid genomics to uncover evidence that DDOL is a strategy utilized by natural microbial communities. Our work suggests that bioengineers can harness HGT to stabilize synthetic metabolic pathways in microbial communities, enabling the development of robust engineered systems for deployment in a variety of contexts.

A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits

Within a cell, synthetic and native genes compete for expression machinery, influencing cellular process dynamics through resource couplings. Models that simplify competitive resource binding kinetics can guide the design of strategies for countering these couplings. However, in bacteria resource availability and cell growth rate are interlinked, which complicates resource-aware biocircuit design. Capturing this interdependence requires coarse-grained bacterial cell models that balance accurate representation of metabolic regulation against simplicity and interpretability. We propose a coarse-grained E. coli cell model that combines the ease of simplified resource coupling analysis with appreciation of bacterial growth regulation mechanisms and the processes relevant for biocircuit design. Reliably capturing known growth phenomena, it provides a unifying explanation to disparate empirical relations between growth and synthetic gene expression. Considering a biomolecular controller that makes cell-wide ribosome availability robust to perturbations, we showcase our model's usefulness in numerically prototyping biocircuits and deriving analytical relations for design guidance.

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.

Programming dynamic division of labor using horizontal gene transfer

The metabolic engineering of microbes has broad applications, including in biomanufacturing, bioprocessing, and environmental remediation. The introduction of a complex, multi-step pathway often imposes a substantial metabolic burden on the host cell, restraining the accumulation of productive biomass and limiting pathway efficiency. One strategy to alleviate metabolic burden is division of labor (DOL), in which different subpopulations carry out different parts of the pathway and work together to convert a substrate into a final product. However, the maintenance of different engineered subpopulations is challenging due to competition and convoluted inter-strain population dynamics. Through modeling, we show that dynamic division of labor (DDOL) mediated by horizontal gene transfer (HGT) can overcome these limitations and enable the robust maintenance of burdensome, multi-step pathways. We also use plasmid genomics to uncover evidence that DDOL is a strategy utilized by natural microbial communities. Our work suggests that bioengineers can harness HGT to stabilize synthetic metabolic pathways in microbial communities, enabling the development of robust engineered systems for deployment in a variety of contexts.

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.

Automated design of gene circuits with optimal mushroom-bifurcation behavior

Recent advances in synthetic biology are enabling exciting technologies, including the next generation of biosensors, the rational design of cell memory, modulated synthetic cell differentiation, and generic multifunctional biocircuits. These novel applications require the design of gene circuits leading to sophisticated behaviors and functionalities. At the same time, designs need to be kept minimal to avoid compromising cell viability. Bifurcation theory addresses such challenges by associating circuit dynamical properties with molecular details of its design. Nevertheless, incorporating bifurcation analysis into automated design processes has not been accomplished yet. This work presents an optimization-based method for the automated design of synthetic gene circuits with specified bifurcation diagrams that employ minimal network topologies. Using this approach, we designed circuits exhibiting the mushroom bifurcation, distilled the most robust topologies, and explored its multifunctional behavior. We then outline potential applications in biosensors, memory devices, and synthetic cell differentiation.

Microbiome-based enrichment pattern mining has enabled a deeper understanding of the biome-species-function relationship

Microbes live in diverse habitats (i.e. biomes), yet their species and genes were biome-specific, forming enrichment patterns. These enrichment patterns have mirrored the biome-species-function relationship, which is shaped by ecological and evolutionary principles. However, a grand picture of these enrichment patterns, as well as the roles of external and internal factors in driving these enrichment patterns, remain largely unexamined. In this work, we have examined the enrichment patterns based on 1705 microbiome samples from four representative biomes (Engineered, Gut, Freshwater, and Soil). Moreover, an "enrichment sphere" model was constructed to elucidate the regulatory principles behind these patterns. The driving factors for this model were revealed based on two case studies: (1) The copper-resistance genes were enriched in Soil biomes, owing to the copper contamination and horizontal gene transfer. (2) The flagellum-related genes were enriched in the Freshwater biome, due to high fluidity and vertical gene accumulation. Furthermore, this enrichment sphere model has valuable applications, such as in biome identification for metagenome samples, and in guiding 3D structure modeling of proteins. In summary, the enrichment sphere model aims towards creating a bluebook of the biome-species-function relationships and be applied in many fields.

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.

Toward predictive engineering of gene circuits

Many synthetic biology applications rely on programming living cells using gene circuits - the assembly and wiring of genetic elements to control cellular behaviors. Extensive progress has been made in constructing gene circuits with diverse functions and applications. For many circuit functions, however, it remains challenging to ensure that the circuits operate in a predictable manner. Although the notion of predictability may appear intuitive, close inspection suggests that it is not always clear what constitutes predictability. We dissect this concept and how it can be confounded by the complexity of a circuit, the complexity of the context, and the interplay between the two. We discuss circuit engineering strategies, in both computation and experiment, that have been used to improve the predictability of gene circuits.

Cybergenetic control of microbial community composition

The use of bacterial communities in bioproduction instead of monocultures has potential advantages including increased productivity through division of labour, ability to utilise cheaper substrates, and robustness against perturbations. A key challenge in the application of engineered bacterial communities is the ability to reliably control the composition of the community in terms of its constituent species. This is crucial to prevent faster growing species from outcompeting others with a lower relative fitness, and to ensure that all species are present at an optimal ratio during different steps in a biotechnological process. In contrast to purely biological approaches such as synthetic quorum sensing circuits or paired auxotrophies, cybergenetic control techniques - those in which computers interface with living cells-are emerging as an alternative approach with many advantages. The community composition is measured through methods such as fluorescence intensity or flow cytometry, with measured data fed real-time into a computer. A control action is computed using a variety of possible control algorithms and then applied to the system, with actuation taking the form of chemical (e.g., inducers, nutrients) or physical (e.g., optogenetic, mechanical) inputs. Subsequent changes in composition are then measured and the cycle repeated, maintaining or driving the system to a desired state. This review discusses recent and future developments in methods for implementing cybergenetic control systems, contrasts their capabilities with those of traditional biological methods of population control, and discusses future directions and outstanding challenges for the field.

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.

Frequency dependent growth of bacteria in living materials

The fusion of living bacteria and man-made materials represents a new frontier in medical and biosynthetic technology. However, the principles of bacterial signal processing inside synthetic materials with three-dimensional and fluctuating environments remain elusive. Here, we study bacterial growth in a three-dimensional hydrogel. We find that bacteria expressing an antibiotic resistance module can take advantage of ambient kinetic disturbances to improve growth while encapsulated. We show that these changes in bacterial growth are specific to disturbance frequency and hydrogel density. This remarkable specificity demonstrates that periodic disturbance frequency is a new input that engineers may leverage to control bacterial growth in synthetic materials. This research provides a systematic framework for understanding and controlling bacterial information processing in three-dimensional living materials.

Model-Based Investigation of the Relationship between Regulation Level and Pulse Property of I1-FFL Gene Circuits

Mathematical models are powerful tools in guiding the construction of synthetic biological circuits, given their capability of accurately capturing and predicting circuit dynamics. Recent innovations in RNA technology have enabled the development of a variety of new tools for regulating gene expression at both the transcription and translation levels. However, the effects of different regulation levels on the circuit dynamics remain largely unexplored. In this study, we focus on the type 1 incoherent feed-forward loop (I1-FFL) gene circuit with four different variations (TX, TL, HY-1, HY-2), to investigate how regulation at the transcription and translation levels affect the circuit dynamics. We develop a mechanistic model for each of the four circuits and deploy sensitivity analysis to investigate the circuits' dynamics in terms of pulse generation. Based on the analysis, we observe that the repression regulation mechanism dominates the characteristics of the pulse as compared to the activation regulation mechanism and find that the I1-FFL with transcription repression has a higher chance of generating a pulse meeting the desired criteria. The experimental results in Escherichia coli also confirm our findings from the computational analysis. We expect our findings to facilitate future experimental construction of gene circuits with insights on the selection of appropriate transcription and translation regulation tools.

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.

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.

Synthetic robust perfect adaptation achieved by negative feedback coupling with linear weak positive feedback

Unlike their natural counterparts, synthetic genetic circuits are usually fragile in the face of environmental perturbations and genetic mutations. Several theoretical robust genetic circuits have been designed, but their performance under real-world conditions has not yet been carefully evaluated. Here, we designed and synthesized a new robust perfect adaptation circuit composed of two-node negative feedback coupling with linear positive feedback on the buffer node. As a key feature, the linear positive feedback was fine-tuned to evaluate its necessity. We found that the desired function was robustly achieved when genetic parameters were varied by systematically perturbing all interacting parts within the topology, and the necessity of the completeness of the topological structures was evaluated by destroying key circuit features. Furthermore, different environmental perturbances were imposed onto the circuit by changing growth rates, carbon metabolic strategies and even chassis cells, and the designed perfect adaptation function was still achieved under all conditions. The successful design of a robust perfect adaptation circuit indicated that the top-down design strategy is capable of predictably guiding bottom-up engineering for robust genetic circuits. This robust adaptation circuit could be integrated as a motif into more complex circuits to robustly implement more sophisticated and critical biological functions.

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.

Engineered microbial consortia: strategies and applications

Many applications of microbial synthetic biology, such as metabolic engineering and biocomputing, are increasing in design complexity. Implementing complex tasks in single populations can be a challenge because large genetic circuits can be burdensome and difficult to optimize. To overcome these limitations, microbial consortia can be engineered to distribute complex tasks among multiple populations. Recent studies have made substantial progress in programming microbial consortia for both basic understanding and potential applications. Microbial consortia have been designed through diverse strategies, including programming mutualistic interactions, using programmed population control to prevent overgrowth of individual populations, and spatial segregation to reduce competition. Here, we highlight the role of microbial consortia in the advances of metabolic engineering, biofilm production for engineered living materials, biocomputing, and biosensing. Additionally, we discuss the challenges for future research in microbial consortia.

Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters

Multistable switches are ubiquitous building blocks in both systems and synthetic biology. Given their central role, it is thus imperative to understand how their fundamental properties depend not only on the tunable biophysical properties of the switches themselves, but also on their genetic context. To this end, we reveal in this article how these factors shape the essential characteristics of toggle switches implemented using leaky promoters such as their stability and robustness to noise, both at single-cell and population levels. In particular, our results expose the roles that competition for scarce transcriptional and translational resources, promoter leakiness, and cell-to-cell heterogeneity collectively play. For instance, the interplay between protein expression from leaky promoters and the associated cost of relying on shared cellular resources can give rise to tristable dynamics even in the absence of positive feedback. Similarly, we demonstrate that while promoter leakiness always acts against multistability, resource competition can be leveraged to counteract this undesirable phenomenon. Underpinned by a mechanistic model, our results thus enable the context-aware rational design of multistable genetic switches that are directly translatable to experimental considerations, and can be further leveraged during the synthesis of large-scale genetic systems using computer-aided biodesign automation platforms.

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.

Stability and Robustness of Unbalanced Genetic Toggle Switches in the Presence of Scarce Resources

While the vision of synthetic biology is to create complex genetic systems in a rational fashion, system-level behaviors are often perplexing due to the context-dependent dynamics of modules. One major source of context-dependence emerges due to the limited availability of shared resources, coupling the behavior of disconnected components. Motivated by the ubiquitous role of toggle switches in genetic circuits ranging from controlling cell fate differentiation to optimizing cellular performance, here we reveal how their fundamental dynamic properties are affected by competition for scarce resources. Combining a mechanistic model with nullcline-based stability analysis and potential landscape-based robustness analysis, we uncover not only the detrimental impacts of resource competition, but also how the unbalancedness of the switch further exacerbates them. While in general both of these factors undermine the performance of the switch (by pushing the dynamics toward monostability and increased sensitivity to noise), we also demonstrate that some of the unwanted effects can be alleviated by strategically optimized resource competition. Our results provide explicit guidelines for the context-aware rational design of toggle switches to mitigate our reliance on lengthy and expensive trial-and-error processes, and can be seamlessly integrated into the computer-aided synthesis of complex genetic systems.