Development of Cell-Free Transcription-Translation Systems in Three Soil Pseudomonads.

In vitro transcription-translation (TX-TL) can enable faster engineering of biological systems. This speed-up can be significant, especially in difficult-to-transform chassis. This work shows the successful development of TX-TL systems using three soil-derived wild-type Pseudomonads known to promote plant growth: Pseudomonas synxantha, Pseudomonas chlororaphis, and Pseudomonas aureofaciens. All three species demonstrated multiple sonication, runoff, and salt conditions producing detectable protein synthesis. One of these new TX-TL systems, P. synxantha, demonstrated a maximum protein yield of 2.5 µM at 125 proteins per DNA template, a maximum protein synthesis rate of 20 nM/min, and a range of DNA concentrations with a linear correspondence with the resulting protein synthesis. A set of different constitutive promoters driving mNeonGreen expression were tested in TX-TL and integrated into the genome, showing similar normalized strengths for in vivo and in vitro fluorescence. This correspondence between the TX-TL-derived promoter strength and the in vivo promoter strength indicates that these lysate-based cell-free systems can be used to characterize and engineer biological parts without genomic integration, enabling a faster design-build-test cycle.

Engineering the Soil Bacterium Pseudomonas synxantha 2-79 into a Ratiometric Bioreporter for Phosphorus Limitation.

Microbial bioreporters hold promise for addressing challenges in medical and environmental applications. However, the difficulty in ensuring their stable persistence and function within the target environment remains a challenge. One strategy is to integrate information about the host strain and target environment into the design-build-test cycle of the bioreporter itself. Here, we present a case study for such an environmentally motivated design process by engineering the wheat commensal bacterium Pseudomonas synxantha 2-79 into a ratiometric bioreporter for phosphorus limitation. Comparative analysis showed that an exogenous P-responsive promoter outperformed its native counterparts. This reporter can selectively sense and report phosphorus limitation at plant-relevant concentrations of 25-100 µM without cross-activation from carbon or nitrogen limitation or high cell densities. Its performance is robust over a field-relevant pH range (5.8-8), and it responds only to inorganic phosphorus, even in the presence of common soil organic P. Finally, we used fluorescein-calibrated flow cytometry to assess whether the reporter's performance in shaken liquid culture predicts its performance in soil, finding that although the reporter is still functional at the bulk level, its variability in performance increases when grown in a soil slurry as compared to planktonic culture, with a fraction of the population not expressing the reporter proteins. Together, our environmentally aware design process provides an example of how laboratory bioengineering efforts can generate microbes with a greater promise to function reliably in their applied contexts.

TRILL: ORCHESTRATING MODULAR DEEP-LEARNING WORKFLOWS FOR DEMOCRATIZED, SCALABLE PROTEIN ANALYSIS AND ENGINEERING.

Deep-learning models have been rapidly adopted by many fields, partly due to the deluge of data humanity has amassed. In particular, the petabases of biological sequencing data enable the unsupervised training of protein language models that learn the "language of life." However, due to their prohibitive size and complexity, contemporary deep-learning models are often unwieldy, especially for scientists with limited machine learning backgrounds. TRILL (TRaining and Inference using the Language of Life) is a platform for creative protein design and discovery. Leveraging several state-of-the-art models such as ESM-2, DiffDock, and RFDiffusion, TRILL allows researchers to generate novel proteins, predict 3-D structures, extract high-dimensional representations of proteins, functionally classify proteins and more. What sets TRILL apart is its ability to enable complex pipelines by chaining together models and effectively merging the capabilities of different models to achieve a sum greater than its individual parts. Whether using Google Colab with one GPU or a supercomputer with hundreds, TRILL allows scientists to effectively utilize models with millions to billions of parameters by using optimized training strategies such as ZeRO-Offload and distributed data parallel. Therefore, TRILL not only bridges the gap between complex deep-learning models and their practical application in the field of biology, but also simplifies the orchestration of these models into comprehensive workflows, democratizing access to powerful methods. Documentation: https://trill.readthedocs.io/en/latest/home.html.

Addressable and adaptable intercellular communication via DNA messaging.

Engineered consortia are a major research focus for synthetic biologists because they can implement sophisticated behaviors inaccessible to single-strain systems. However, this functional capacity is constrained by their constituent strains' ability to engage in complex communication. DNA messaging, by enabling information-rich channel-decoupled communication, is a promising candidate architecture for implementing complex communication. But its major advantage, its messages' dynamic mutability, is still unexplored. We develop a framework for addressable and adaptable DNA messaging that leverages all three of these advantages and implement it using plasmid conjugation in E. coli. Our system can bias the transfer of messages to targeted receiver strains by 100- to 1000-fold, and their recipient lists can be dynamically updated in situ to control the flow of information through the population. This work lays the foundation for future developments that further utilize the unique advantages of DNA messaging to engineer previously-inaccessible levels of complexity into biological systems.

Characterization of Integrase and Excisionase Activity in a Cell-Free Protein Expression System Using a Modeling and Analysis Pipeline.

We present a full-stack modeling, analysis, and parameter identification pipeline to guide the modeling and design of biological systems starting from specifications to circuit implementations and parametrizations. We demonstrate this pipeline by characterizing the integrase and excisionase activity in a cell-free protein expression system. We build on existing Python tools─BioCRNpyler, AutoReduce, and Bioscrape─to create this pipeline. For enzyme-mediated DNA recombination in a cell-free system, we create detailed chemical reaction network models from simple high-level descriptions of the biological circuits and their context using BioCRNpyler. We use Bioscrape to show that the output of the detailed model is sensitive to many parameters. However, parameter identification is infeasible for this high-dimensional model; hence, we use AutoReduce to automatically obtain reduced models that have fewer parameters. This results in a hierarchy of reduced models under different assumptions to finally arrive at a minimal ODE model for each circuit. Then, we run sensitivity analysis-guided Bayesian inference using Bioscrape for each circuit to identify the model parameters. This process allows us to quantify integrase and excisionase activity in cell extracts enabling complex-circuit designs that depend on accurate control over protein expression levels through DNA recombination. The automated pipeline presented in this paper opens up a new approach to complex circuit design, modeling, reduction, and parametrization.

Integrase-mediated differentiation circuits improve evolutionary stability of burdensome and toxic functions in E. coli.

Advances in synthetic biology, bioengineering, and computation allow us to rapidly and reliably program cells with increasingly complex and useful functions. However, because the functions we engineer cells to perform are typically burdensome to cell growth, they can be rapidly lost due to the processes of mutation and natural selection. Here, we show that a strategy of terminal differentiation improves the evolutionary stability of burdensome functions in a general manner by realizing a reproductive and metabolic division of labor. To implement this strategy, we develop a genetic differentiation circuit in Escherichia coli using unidirectional integrase-recombination. With terminal differentiation, differentiated cells uniquely express burdensome functions driven by the orthogonal T7 RNA polymerase, but their capacity to proliferate is limited to prevent the propagation of advantageous loss-of-function mutations that inevitably occur. We demonstrate computationally and experimentally that terminal differentiation increases duration and yield of high-burden expression and that its evolutionary stability can be improved with strategic redundancy. Further, we show this strategy can even be applied to toxic functions. Overall, this study provides an effective, generalizable approach for protecting burdensome engineered functions from evolutionary degradation.

Layered feedback control overcomes performance trade-off in synthetic biomolecular networks.

Layered feedback is an optimization strategy in feedback control designs widely used in engineering. Control theory suggests that layering multiple feedbacks could overcome the robustness-speed performance trade-off limit. In natural biological networks, genes are often regulated in layers to adapt to environmental perturbations. It is hypothesized layering architecture could also overcome the robustness-speed performance trade-off in genetic networks. In this work, we validate this hypothesis with a synthetic biomolecular network in living E. coli cells. We start with system dynamics analysis using models of various complexities to guide the design of a layered control architecture in living cells. Experimentally, we interrogate system dynamics under three groups of perturbations. We consistently observe that the layered control improves system performance in the robustness-speed domain. This work confirms that layered control could be adopted in synthetic biomolecular networks for performance optimization. It also provides insights into understanding genetic feedback control architectures in nature.

BioCRNpyler: Compiling chemical reaction networks from biomolecular parts in diverse contexts.

Biochemical interactions in systems and synthetic biology are often modeled with chemical reaction networks (CRNs). CRNs provide a principled modeling environment capable of expressing a huge range of biochemical processes. In this paper, we present a software toolbox, written in Python, that compiles high-level design specifications represented using a modular library of biochemical parts, mechanisms, and contexts to CRN implementations. This compilation process offers four advantages. First, the building of the actual CRN representation is automatic and outputs Systems Biology Markup Language (SBML) models compatible with numerous simulators. Second, a library of modular biochemical components allows for different architectures and implementations of biochemical circuits to be represented succinctly with design choices propagated throughout the underlying CRN automatically. This prevents the often occurring mismatch between high-level designs and model dynamics. Third, high-level design specification can be embedded into diverse biomolecular environments, such as cell-free extracts and in vivo milieus. Finally, our software toolbox has a parameter database, which allows users to rapidly prototype large models using very few parameters which can be customized later. By using BioCRNpyler, users ranging from expert modelers to novice script-writers can easily build, manage, and explore sophisticated biochemical models using diverse biochemical implementations, environments, and modeling assumptions.

Synthetic mammalian signaling circuits for robust cell population control.

In multicellular organisms, cells actively sense and control their own population density. Synthetic mammalian quorum-sensing circuits could provide insight into principles of population control and extend cell therapies. However, a key challenge is reducing their inherent sensitivity to "cheater" mutations that evade control. Here, we repurposed the plant hormone auxin to enable orthogonal mammalian cell-cell communication and quorum sensing. We designed a paradoxical population control circuit, termed "Paradaux," in which auxin stimulates and inhibits net cell growth at different concentrations. This circuit limited population size over extended timescales of up to 42 days of continuous culture. By contrast, when operating in a non-paradoxical regime, population control became more susceptible to mutational escape. These results establish auxin as a versatile "private" communication system and demonstrate that paradoxical circuit architectures can provide robust population control.

Synthetic logic circuits using RNA aptamer against T7 RNA polymerase.

Recent advances in nucleic acids engineering introduced several RNA-based regulatory components for synthetic gene circuits, expanding the toolsets to engineer organisms. In this work, we designed genetic circuits implementing an RNA aptamer previously described to have the capability of binding to the T7 RNA polymerase and inhibiting its activity in vitro. We first demonstrated the utility of the RNA aptamer in combination with programmable synthetic transcription networks in vitro. As a step to quickly assess the feasibility of aptamer functions in vivo, we tested the aptamer and its sequence variants in the cell-free expression system, verifying the aptamer functionality in the cell-free testbed. The expression of aptamer in E. coli demonstrated control over GFP expression driven by T7 RNA polymerase, indicating its ability to serve as building blocks for logic circuits and transcriptional cascades. This work elucidates the potential of T7 RNA polymerase aptamer as regulators for synthetic biological circuits and metabolic engineering.

Data-driven network models for genetic circuits from time-series data with incomplete measurements.

Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli-based transcriptional event detector.

Rapid Characterization of Genetic Parts with Cell-free Systems.

Characterizing and cataloging genetic parts are critical to the design of useful genetic circuits. Having well-characterized parts allows for the fine-tuning of genetic circuits, such that their function results in predictable outcomes. With the growth of synthetic biology as a field, there has been an explosion of genetic circuits that have been implemented in microbes to execute functions pertaining to sensing, metabolic alteration, and cellular computing. Here, we show a rapid and cost-effective method for characterizing genetic parts. Our method utilizes cell-free lysate, prepared in-house as a medium to evaluate parts via the expression of a reporter protein. Template DNA is prepared by PCR amplification using inexpensive primers to add variant parts to the reporter gene, and the template is added to the reaction as linear DNA without cloning. Parts that can be added in this way include promoters, operators, ribosome binding sites, insulators, and terminators. This approach, combined with the incorporation of an acoustic liquid handler and 384-well plates, allows the user to carry out high-throughput evaluations of genetic parts in a single day. By comparison, cell-based screening approaches require time-consuming cloning and have longer testing times due to overnight culture and culture density normalization steps. Further, working in cell-free lysate allows the user to exact tighter control over the expression conditions through the addition of exogenous components and DNA at precise concentrations. Results obtained from cell-free screening can be used directly in applications of cell-free systems or, in some cases, as a way to predict function in whole cells.

Erratum to: A MATLAB toolbox for modeling genetic circuits in cell-free systems.

[This corrects the article DOI: 10.1093/synbio/ysab007.].

Guiding Ethical Principles in Engineering Biology Research.

Engineering biology is being applied toward solving or mitigating some of the greatest challenges facing society. As with many other rapidly advancing technologies, the development of these powerful tools must be considered in the context of ethical uses for personal, societal, and/or environmental advancement. Researchers have a responsibility to consider the diverse outcomes that may result from the knowledge and innovation they contribute to the field. Together, we developed a Statement of Ethics in Engineering Biology Research to guide researchers as they incorporate the consideration of long-term ethical implications of their work into every phase of the research lifecycle. Herein, we present and contextualize this Statement of Ethics and its six guiding principles. Our goal is to facilitate ongoing reflection and collaboration among technical researchers, social scientists, policy makers, and other stakeholders to support best outcomes in engineering biology innovation and development.

A MATLAB toolbox for modeling genetic circuits in cell-free systems.

We introduce a MATLAB-based simulation toolbox, called txtlsim, for an Escherichia coli-based Transcription-Translation (TX-TL) system. This toolbox accounts for several cell-free-related phenomena, such as resource loading, consumption and degradation, and in doing so, models the dynamics of TX-TL reactions for the entire duration of solution phase batch-mode experiments. We use a Bayesian parameter inference approach to characterize the reaction rate parameters associated with the core transcription, translation and mRNA degradation mechanics of the toolbox, allowing it to reproduce constitutive mRNA and protein-expression trajectories. We demonstrate the use of this characterized toolbox in a circuit behavior prediction case study for an incoherent feed-forward loop.

Assessment of Robustness to Temperature in a Negative Feedback Loop and a Feedforward Loop.

Robustness to temperature variation is an important specification in biomolecular circuit design. While the cancellation of parametric temperature dependencies has been shown to improve the temperature robustness of the period in a synthetic oscillator design, the performance of other biomolecular circuit designs in different temperature conditions is relatively unclear. Using a combination of experimental measurements and mathematical models, we assessed the temperature robustness of two biomolecular circuit motifs-a negative feedback loop and a feedforward loop. We found that the measured responses of both the circuits changed with temperature, both in the amplitude and in the transient response. We also found that, in addition to the cancellation of parametric temperature dependencies, certain parameter regimes could facilitate the temperature robustness of the negative feedback loop, although at a performance cost. We discuss these parameter regimes in the context of the measured data for the negative feedback loop. These results should help develop a framework for assessing and designing temperature robustness in biomolecular circuits.

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.

A method for cost-effective and rapid characterization of engineered T7-based transcription factors by cell-free protein synthesis reveals insights into the regulation of T7 RNA polymerase-driven expression.

The T7 bacteriophage RNA polymerase (T7 RNAP) serves as a model for understanding RNA synthesis, as a tool for protein expression, and as an actuator for synthetic gene circuit design in bacterial cells and cell-free extract. T7 RNAP is an attractive tool for orthogonal protein expression in bacteria owing to its compact single subunit structure and orthogonal promoter specificity. Understanding the mechanisms underlying T7 RNAP regulation is important to the design of engineered T7-based transcription factors, which can be used in gene circuit design. To explore regulatory mechanisms for T7 RNAP-driven expression, we developed a rapid and cost-effective method to characterize engineered T7-based transcription factors using cell-free protein synthesis and an acoustic liquid handler. Using this method, we investigated the effects of the tetracycline operator's proximity to the T7 promoter on the regulation of T7 RNAP-driven expression. Our results reveal a mechanism for regulation that functions by interfering with the transition of T7 RNAP from initiation to elongation and validates the use of the method described here to engineer future T7-based transcription factors.

Hard Limits and Performance Tradeoffs in a Class of Antithetic Integral Feedback Networks.

Feedback regulation is pervasive in biology at both the organismal and cellular level. In this article, we explore the properties of a particular biomolecular feedback mechanism called antithetic integral feedback, which can be implemented using the binding of two molecules. Our work develops an analytic framework for understanding the hard limits, performance tradeoffs, and architectural properties of this simple model of biological feedback control. Using tools from control theory, we show that there are simple parametric relationships that determine both the stability and the performance of these systems in terms of speed, robustness, steady-state error, and leakiness. These findings yield a holistic understanding of the behavior of antithetic integral feedback and contribute to a more general theory of biological control systems.

Construction of Incoherent Feedforward Loop Circuits in a Cell-Free System and in Cells.

Cells utilize transcriptional regulation networks to respond to environmental signals. Network motifs, such as feedforward loops, play essential roles in these regulatory networks. In this work, we construct two different functional and modular incoherent type 1 feedforward loop circuits in a cell-free transcription-translation system and in cells. With the help of mathematical modeling and the cell-free system, we can streamline the design-build-test cycles of the circuits, in which we characterize and optimize these circuits in vitro to confirm that they function as expected before implementing them in vivo. We show that the performance of these circuits from in vitro studies closely recapitulates those from in vivo experiments. We demonstrate that these feedforward loops show dynamic response and pulse-like behavior both in vitro and in vivo. These novel feedforward loop network motifs can be incorporated in more complicated biological circuits as detectors or responders.