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Plastic pollution is rapidly increasing worldwide, causing adverse impacts on the environment, wildlife and human health. One tempting solution to this crisis is upcycling plastics into products with engineered microorganisms; however, this remains challenging due to complexity in conversion. Here we present a synthetic microbial consortium that efficiently degrades polyethylene terephthalate hydrolysate and subsequently produces desired chemicals through division of labor. The consortium involves two Pseudomonas putida strains, specializing in terephthalic acid and ethylene glycol utilization respectively, to achieve complete substrate assimilation. Compared with its monoculture counterpart, the consortium exhibits reduced catabolic crosstalk and faster deconstruction, particularly when substrate concentrations are high or crude hydrolysate is used. It also outperforms monoculture when polyhydroxyalkanoates serves as a target product and confers flexible tuning through population modulation for cis-cis muconate synthesis. This work demonstrates engineered consortia as a promising, effective platform that may facilitate polymer upcycling and environmental sustainability. Plastic pollution is rapidly increasing worldwide, causing adverse impacts on the environment, wildlife and human health. Here the authors present a synthetic microbial consortium that efficiently degrades polyethylene terephthalate hydrolysate and upcycles it to desired chemicals through cellular division of labor.

Abstract Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO 2 -fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 10 25 conditions with only 1,000 experiments to yield the most efficient CO 2 -fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

Homeostasis is a recurring theme in biology that ensures that regulated variables robustly—and in some systems, completely—adapt to environmental perturbations. This robust perfect adaptation feature is achieved in natural circuits by using integral control, a negative feedback strategy that performs mathematical integration to achieve structurally robust regulation 1 , 2 . Despite its benefits, the synthetic realization of integral feedback in living cells has remained elusive owing to the complexity of the required biological computations. Here we prove mathematically that there is a single fundamental biomolecular controller topology 3 that realizes integral feedback and achieves robust perfect adaptation in arbitrary intracellular networks with noisy dynamics. This adaptation property is guaranteed both for the population-average and for the time-average of single cells. On the basis of this concept, we genetically engineer a synthetic integral feedback controller in living cells 4 and demonstrate its tunability and adaptation properties. A growth-rate control application in Escherichia coli shows the intrinsic capacity of our integral controller to deliver robustness and highlights its potential use as a versatile controller for regulation of biological variables in uncertain networks. Our results provide conceptual and practical tools in the area of cybergenetics 3 , 5 , for engineering synthetic controllers that steer the dynamics of living systems 3 – 9 . A synthetic gene circuit implementing an integral feedback topology is shown to achieve robust perfect adaptation in living cells--mathematical analysis proves this topology is necessary for adaptation in networks with noisy dynamics.

As synthetic biology permeates society, the signal processing circuits in engineered living systems must be customized to meet practical demands. Towards this mission, novel regulatory mechanisms and genetic circuits with unprecedented complexity have been implemented over the past decade. These regulatory mechanisms, such as transcription and translation control, could be integrated into hybrid circuits termed “multi-level circuits”. The multi-level circuit design will tremendously benefit the current genetic circuit design paradigm, from modifying basic circuit dynamics to facilitating real-world applications, unleashing our capabilities to customize cellular signal processing and address global challenges through synthetic biology. As synthetic biology permeates society, the signal processing circuits in engineered living systems must be customized to meet practical demands. In this review, the authors outline design strategies for the DNA, RNA, and protein-level circuits and the hybrid “multi-level” circuits.

Despite recent advances in circuit engineering, the design of genetic networks in mammalian cells is still painstakingly slow and fraught with inexplicable failures. Here, we demonstrate that transiently expressed genes in mammalian cells compete for limited transcriptional and translational resources. This competition results in the coupling of otherwise independent exogenous and endogenous genes, creating a divergence between intended and actual function. Guided by a resource-aware mathematical model, we identify and engineer natural and synthetic miRNA-based incoherent feedforward loop (iFFL) circuits that mitigate gene expression burden. The implementation of these circuits features the use of endogenous miRNAs as elementary components of the engineered iFFL device, a versatile hybrid design that allows burden mitigation to be achieved across different cell-lines with minimal resource requirements. This study establishes the foundations for context-aware prediction and improvement of in vivo synthetic circuit performance, paving the way towards more rational synthetic construct design in mammalian cells. The design of genetic networks in mammalian cells is still slow and often fails. Here the authors show that miRNA-based incoherent feedforward loop circuits can be used to alleviate cellular burden.

Synthetic biology is the discipline of engineering application-driven biological functionalities that were not evolved by nature. Early breakthroughs of cell engineering, which were based on ectopic (over)expression of single sets of transgenes, have already had a revolutionary impact on the biotechnology industry, regenerative medicine and blood transfusion therapies. Now, we require larger-scale, rationally assembled genetic circuits engineered to programme and control various human cell functions with high spatiotemporal precision in order to solve more complex problems in applied life sciences, biomedicine and environmental sciences. This will open new possibilities for employing synthetic biology to advance personalized medicine by converting cells into living therapeutics to combat hitherto intractable diseases.

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. Accurately predicting the behaviour of a genetic circuit remains difficult due to the lack of modularity. Here the authors quantify the effects of resource loading in mammalian systems and develop an endoribonuclease-based feedfoward controller to adapt gene expression to the effects of resource loading.

Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern. Complex biological systems have individual cells acting collectively to solve complex tasks. Here the authors implement neural network-like computing in a bacterial consortia to recognise patterns.

Multicellularity enables the growth of complex life forms as it allows for the specialization of cell types, differentiation and large-scale spatial organization. In a similar way, modular construction of synthetic multicellular systems will lead to dynamic biomimetic materials that can respond to their environment in complex ways. To achieve this goal, artificial cellular communication and developmental programs still have to be established. Here, we create geometrically controlled spatial arrangements of emulsion-based artificial cellular compartments containing synthetic in vitro gene circuitry, separated by lipid bilayer membranes. We quantitatively determine the membrane pore-dependent response of the circuits to artificial morphogen gradients, which are established via diffusion from dedicated organizer cells. Utilizing different types of feedforward and feedback in vitro gene circuits, we then implement artificial signalling and differentiation processes, demonstrating the potential for the realization of complex spatiotemporal dynamics in artificial multicellular systems. Synthetic gene circuits encapsulated in lipid membrane compartments are often employed as artificial cell mimics, but these lack the complex behaviour of biological tissues. Now, spatial information based on chemical gradients has been used to engineer non-trivial dynamics such as signal propagation and differentiation in an artificial multicellular system.

Genetic circuits that are reliable, robust, and scalable are built without the need for optimization using a recombinase-based system. Engineered genetic circuits for mammalian cells often require extensive fine-tuning to perform as intended. We present a robust, general, scalable system, called 'Boolean logic and arithmetic through DNA excision' (BLADE), to engineer genetic circuits with multiple inputs and outputs in mammalian cells with minimal optimization. The reliability of BLADE arises from its reliance on recombinases under the control of a single promoter, which integrates circuit signals on a single transcriptional layer. We used BLADE to build 113 circuits in human embryonic kidney and Jurkat T cells and devised a quantitative, vector-proximity metric to evaluate their performance. Of 113 circuits analyzed, 109 functioned (96.5%) as intended without optimization. The circuits, which are available through Addgene, include a 3-input, two-output full adder; a 6-input, one-output Boolean logic look-up table; circuits with small-molecule-inducible control; and circuits that incorporate CRISPR–Cas9 to regulate endogenous genes. BLADE enables execution of sophisticated cellular computation in mammalian cells, with applications in cell and tissue engineering.