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bioRxiv2024

Metabolite-responsive Control of Transcription by Phase Separation-based Synthetic Organelles

Living natural materials have remarkable sensing abilities that translate external cues into functional changes of the material. The reconstruction of such sensing materials in bottom-up synthetic biology provides the opportunity to develop synthetic materials with life-like sensing and adaptation ability. Key to such functions are material modules that translate specific input signals into a biomolecular response. Here, we engineer a synthetic organelle based on liquid-liquid phase separation that translates a metabolic signal into the regulation of gene transcription. To this aim, we engineer the pyruvate-dependent repressor PdhR to undergo liquid-liquid phase separation in vitro by fusion to intrinsically disordered regions. We demonstrate that the resulting coacervates bind DNA harbouring PdhR-responsive operator sites in a pyruvate dose-dependent and reversible manner. We observed that the activity of transcription units on the DNA was strongly attenuated following recruitment to the coacervates. However, the addition of pyruvate resulted in a reversible and dose-dependent reconstitution of transcriptional activity. The coacervate-based synthetic organelles linking metabolic cues to transcriptional signals represent a materials approach to confer stimulus-responsiveness to minimal bottom-up synthetic biological systems and open opportunities in materials for sensor applications.

synthetic biology↗

bioRxiv2023

From Context to Code: Rational De Novo DNA Design and Predicting Cross-Species DNA Functionality Using Deep Learning Transformer Models

Synthetic biology currently operates under a framework dominated by trial-and-error approaches, which hinders the effective engineering of organisms and the expansion of large-scale biomanufacturing. Motivated by the success of computational designs in areas like architecture and aeronautics, we aspire to transition to a more efficient and predictive methodology in synthetic biology. In this study, we report a DNA Design Platform that relies on the predictive power of Transformer-based deep learning architectures. The platform transforms the conventional paradigms in synthetic biology by enabling the context-sensitive and host-specific engineering of 5' regulatory elements--promoters and 5' untranslated regions (UTRs) along with an array of codon-optimised coding sequence (CDS) variants. This allows us to generate context-sensitive 5' regulatory sequences and CDSs, achieving an unparalleled level of specificity and adaptability in different target hosts. With context-aware design, we significantly broaden the range of possible gene expression profiles and phenotypic outcomes, substantially reducing the need for laborious high-throughput screening efforts. Our context-aware, AI-driven design strategy marks a significant advancement in synthetic biology, offering a scalable and refined approach for gene expression optimisation across a diverse range of expression hosts. In summary, this study represents a substantial leap forward in the field, utilising deep learning models to transform the conventional design, build, test, learn-cycle into a more efficient and predictive framework.

synthetic biology↗

bioRxiv2025

Metabolomics-informed coarse-grained model enables prediction of cell-free protein expression dynamics

Cell-free expression systems have gained considerable attention for their potential in biomanufac-turing, biosensing, and circuit prototyping. All these endeavors make use of the innate metabolic activity of cell lysates. However, our knowledge of the underlying nature of cell-free metabolism remains lacking. In this work, we use non-targeted mass spectrometry to generate time-course measurements of small molecules in E. coli cell lysates (metabolomics) during cell-free protein synthesis (CFPS) to show that the majority of E. colis metabolism is active in cell lysate. Fur-thermore, these data indicate that protein synthesis is a relatively small metabolic burden on cell lysates compared to the background metabolism, and that the build-up of multiple metabolic waste products is fundamentally responsible for stopping cell-free protein expression as opposed to depletion of fuel sources. We use these insights coupled with high-throughput CFPS experiments to develop a foundational coarse-grained mechanistic model of CFPS which we show can be easily recalibrated using Bayesian parameter inference techniques to provide accurate models across hundreds of novel experimental conditions. This work provides new experimental insights into the effects of cell-free metabolism on CFPS and establishes a novel framework for leveraging existing CFPS models in new experimental contexts, opening the avenue to future cell-free applications aimed at building complex systems, from multilayered biological circuits to synthetic biological cells.

synthetic biology↗

bioRxiv2024

Dynamics of genetic circuits in Pseudomonas protegens

The engineering of genetic circuits to perform predefined computations is central to synthetic biology, enabling living cells with new functionalities applicable across various domains. However, these circuits are often specifically tailored to particular cellular hosts, with Escherichia coli being the most popular. Consequently, their intended functions may not translate well to other organisms, limiting their scope. Understanding circuit dynamics in less familiar organisms is crucial, especially for niche-specific applications requiring cellular chassis different from model organisms generally used in synthetic biology. Here, we develop a combined experimental and theoretical pipeline to evaluate the performance of NOT logic circuits, also called inverters, in the soil bacterium Pseudomonas protegens Pf-5--a host renowned for its unique environmental functions and a newcomer to genetic circuitry. Inverters were experimentally tested to characterize input-output functionality, and mathematical modelling was used to infer the dynamic principles of circuit modules. The model quantified the individual impacts of key parameters--such as translation efficiency, repressor performance, and promoter activity--on output levels, enabling predictions about inter-host circuit portability. This parameter calibration revealed unique properties of the chassis, including faster transitions between on and off circuit states compared to the synthetic biology workhorse Pseudomonas putida. These characteristics may reflect adaptations to the fluctuating conditions of the plant rhizosphere, where this bacteria thrives. As a result, our work provides DNA parts, circuits and mathematical characterizations to establish P. protegens Pf-5 as a viable chassis for environmental synthetic biology.

synthetic biology↗

bioRxiv2026

Genetically Programmed Shape-morphing of Engineered Living Materials

Engineered living materials (ELMs) promise genetically programmable functions by coupling biological regulation to synthetic material responses. Here, we introduce genetically encoded, reversible shape-morphing in a peptide-crosslinked polyethylene glycol (PEG) hydrogel whose network density is modulated by opposing enzymatic pairs that induce crosslinking or hydrolysis. This molecular programmability alternates the hydrogel between deswelling and swelling/disintegration and produces 2 - 5-fold changes in mechanical properties. By fabricating a bilayer hydrogel with an inert layer, these molecular modulations are translated into a reversible and directional motion with angular bending motions exceeding 80{degrees}. Further, by embedding genetically engineered bacteria or interfacing mammalian cells, producing the relevant enzymatic cues, the reversible shape-morphing of these ELMs is programmed at the genetic level. We further demonstrate genetically programmed, autonomous reversible bending in a bilayer hydrogel controlled by out-of-equilibrium counteracting biochemical reactions with dynamically changing respective reaction rates. This work establishes a concept where coordinated polymer/peptide material engineering and synthetic biology yield autonomous shape-morphing ELMs, opening avenues toward biohybrid soft robotics, adaptive microfluidic systems, and dynamic biomedical interfaces.

synthetic biology↗

bioRxiv2023

High-throughput, microscopy-based screening, and quantification of genetic elements

Synthetic biology relies on the screening and quantification of genetic components to assemble sophisticated gene circuits with specific functions. Microscopy is powerful tool for characterizing complex cellular phenotypes with increasing spatial and temporal resolution to library screening of genetic elements. Microscopy-based assays are powerful tools for characterizing cellular phenotypes with spatial and temporal resolution, and can be applied to large-scale samples for library screening of genetic elements. However, strategies for high-throughput microscopy experiments remain limited. Here, we present a high-throughput, microscopy-based platform that can simultaneously complete the preparation of an 8x12-well agarose pads plate, allowing for the screening of 96 independent strains or experimental conditions in a single experiment. Using this platform, we screened a library of natural intrinsic promoters from Pseudomonas aeruginosa and identified a small subset of robust promoters that drives stable levels of gene expression under varying growth conditions. Additionally, the platform allowed for single-cell measurement of genetic elements over time, enabling the identification of complex and dynamic phenotypes to map genotype in high-throughput. We expected that the platform could be employed to accelerate the identification and characterization of genetic elements in various biological systems, as well as to understand the relationship between cellular phenotypes and internal states, including genotypes and gene expression programs. Impact statementThe high-throughput microscopy-based platform, presented in this study, enables efficient screening of 96 independent strains or experimental conditions in a single experiment, facilitating the rapid identification of genetic elements with desirable features, thereby advancing synthetic biology. The robust promoters identified through this platform, which provide predictable and consistent control over gene expression under varying growth conditions, can be utilized as reliable tools to regulate gene expression in various biological applications, including synthetic biology, metabolic engineering, and gene therapy, where consistent system performance is required.

molecular biology↗

bioRxiv2023

A scalable framework for high-throughput identification of functional origins of replication in non-model bacteria

Microbial genetic manipulation requires access to engineerable plasmids that can be programmed to perturb genes, pathways and genomes. The extensive repertoire of plasmids available for model microbes, such as Escherichia coli, has facilitated fundamental biology studies and synthetic biology applications. However, the scarcity of plasmids for non-model microbes hinders efforts to broaden our biological knowledge and constrains the development of biotechnological solutions. In this study, we introduce a molecular toolkit and multiplexed screen to evaluate functional plasmids in non-model microbes. We constructed a collection of genetic parts consisting of 22 origins of replication (ORIs), 20 antibiotic selectable markers, and 30 molecular barcodes, which can be assembled combinatorially to create a library of plasmids trackable by next-generation DNA sequencing. We demonstrate our approach by delivering a pooled library of 22 ORIs to 12 bacterial species including extremophiles, electroactive bacteria and bioproduction strains. We report, for the first time, DNA delivery by conjugation and functional ORIs for Halomonas alkaliphila, Halomonas neptunia, and Shewanella electrodiphila. Furthermore, we expand the list of functional ORIs for Duganella zoogloeoides, Pseudomonas alcaliphila, Shewanella oneidensis and Shewanella putrefaciens. This screen provides a scalable high-throughput system to rapidly build and identify functional plasmids to establish genetic tractability in non-model microbes.

synthetic biology↗

bioRxiv2019

A quantitative method for proteome reallocation using minimal regulatory interventions

Engineering resource allocation in biological systems for synthetic biology applications is an ongoing challenge. Wild type organisms allocate abundant cellular resources for ensuring survival in changing environments, reducing the productivity of engineered functions. Here we present a novel approach for engineering the resource allocation of Escherichia coli by rationally modifying the transcriptional regulatory network of the bacterium. Our method (ReProMin) identifies the minimal set of genetic interventions that maximise the savings in cell resources that would normally be used to express non-essential genes. To this end we categorize Transcription Factors (TFs) according to the essentiality of the genes they regulate and we use available proteomic data to rank them based on its proteomic balance, defined as the net proteomic charge they release. Using a combinatorial approach, we design the removal of TFs that maximise the release of the proteomic charge and we validate the model predictions experimentally. Expression profiling of the resulting strain shows that our designed regulatory interventions are highly specific. We show that our resulting engineered strain containing only three mutations, theoretically releasing 0.5% of their proteome, has higher proteome budget and show increased production yield of a molecule of interest obtained from a recombinant metabolic pathway. This approach shows that combining whole-cell proteomic and regulatory data is an effective way of optimizing strains in a predictable way using conventional molecular methods.\n\nImportanceBiological regulatory mechanisms are complex and occur in hierarchical layers such as transcription, translation and post-translational mechanisms. We foresee the use of regulatory mechanism as a control layer that will aid in the design of cellular phenotypes. Our ability to engineer biological systems will be dependent on the understanding of how cells sense and respond to their environment at a system level. Few studies have tackled this issue and none of them in a rational way. By developing a workflow of engineering resource allocation based on our current knowledge of E. colis regulatory network, we pursue the objective of minimizing cell proteome using a minimal genetic intervention principle. We developed a method to rationally design a set of genetic interventions that reduce the hedging proteome allocation. Using available datasets of a model bacterium we were able to reallocate parts of the unused proteome in laboratory conditions to the production of an engineered task. We show that we are able to reduce the unused proteome (theoretically 0.5%) with only three regulatory mutations designed in a rational way, which results in strains with increased capabilities for recombinant expression of pathways of interest.\n\nHighlightsO_LIProteome reduction with minimal genetic intervention as design principle\nC_LIO_LIRegulatory and proteomic data integration to identify transcription factor activated proteome\nC_LIO_LIDeletion of the TF combination that reduces the greater proteomic load\nC_LIO_LIRegulatory interventions are highly specific\nC_LIO_LIDesigned strains show less burden, improved protein and violacein production\nC_LI

synthetic biology↗

bioRxiv2024

GRN modeler: An Intuitive Tool for Constructing and Evaluating Gene Regulatory Networks and its Applications to Oscillators and a Light Biosensor

Modeling and simulating gene regulatory networks (GRNs) is crucial for understanding biological processes, predicting system behavior, guiding the design of synthetic biological systems, and interpreting experimental data. In synthetic biology, GRNs play a pivotal role in enabling the design and control of complex systems for a wide range of applications. However, GRN simulations can be time-consuming and often require specialized expertise. To make this process more accessible, we developed a user-friendly application with a graphical user interface (GUI), allowing users to create simple phenomenological models without requiring prior programming experience. We demonstrate the versatility of our tool through several examples, including the design of novel oscillator families capable of robust oscillation with an even number of nodes. These complement the well-known repressilator family, which only oscillates with odd-numbered nodes. Furthermore, we showcase how GRN modeler allowed us to develop a light-detecting biosensor in Escherichia coli that can track light intensity over several days, leaving a record in the form of ring patterns in bacterial colonies. In summary, our work empowers biologists to model their systems of interest even without programming expertise.

synthetic biology↗

bioRxiv2022

Investigating And Modeling the Factors that Effects the Performance of Genetic Circuits

Over the past two decades, synthetic biology has yielded ever more complex genetic circuits able to perform sophisticated functions in response to specific signals. Yet, genetic circuits are not immediately transferable to an outside-the-lab setting where their performance is highly compromised. We propose introducing a scale step to the design-build-test workflow to include factors that might contribute to unexpected genetic circuit performance. As a proof-of-concept, we designed and tested a genetic circuit under different temperatures, mediums, inducer concentrations, and bacterial growth phases. We determined that the circuits performance is dramatically altered when these factors differ from the optimal lab conditions. Based on these results, a scaling effort, coupled with a learning process, proceeded to generate model predictions for the genetic circuits performance under untested conditions, which is currently lacking in synthetic biology application design. As the synthetic biology discipline transitions from proof-of-concept genetic programs to appropriate and safe application implementations, more emphasis on a scale step is needed to ensure correct and robust performances.

synthetic biology↗

bioRxiv2020

Codon optimisation for maximising gene expression in multiple species and microbial consortia

MotivationCodon optimisation, the process of adapting the codon composition of a coding sequence, is often used in synthetic biology to increase expression of a heterologous protein. Recently, a number of synthetic biology approaches that allow synthetic constructs to be deployed in multiple organisms have been published. However, so far, design tools for codon optimisation have not been updated to reflect these new approaches. ApproachWe designed an evolutionary algorithm (EA) to design coding sequences (CDSs) that encode a target protein for one or more target organisms, based on the Chimera average repetitive substring (ARS) metric -- a correlate of gene expression. A parameter scan was then used to find optimal parameter sets. Using the optimal parameter sets, three heterologous proteins were repeatedly optimised Bacillus subtilis 168 and Escherichia coli MG1655. The ARS scores of the resulting sequences were compared to the ARS scores of coding sequences that had been optimised for each organism individually (using Chimera Map). ResultsWe demonstrate that an EA is a valid approach to optimising a coding sequence for multiple organisms at once; both crossover and mutation operators were shown to be necessary for the best performance. In some scenarios, the EA generated CDSs that had higher ARS scores than CDSs optimised for the individual organisms, suggesting that the EA exploits the CDS design space in a way that Chimera Map does not. Availability and implementationThe implementation of the EA, with instructions, is available on GitHub: https://github.com/intbio-ncl/chimera_evolve.

synthetic biology↗

bioRxiv2022

SynBio in 3D: the first synthetic genetic circuit as a 3D-printed STEM educational resource

Synthetic biology is a new area of science that operates at the intersection of engineering and biology and aims to design and synthesize living organisms and systems to perform new or improved functions. This novel biological area is considered of extreme relevance for the development of solutions to global problems. However, its teaching is often inaccessible to students, since many educational resources and methodological procedures are not available for understanding of complex molecular processes. On the other hand, digital fabrication tools, which allow the creation of 3D objects, are increasingly used for educational purposes, and several computational structures of molecular components commonly used in synthetic biology processes are deposited in open databases. Therefore, we hypothesize that the creation of biomolecular structures models by handling 3D physical objects using computer-assisted design (CAD) and additive fabrication based on 3D printing could help professors in synthetic biology teaching. In this sense, the present work describes the design and 3D print of the molecular models of the first synthetic genetic circuit, the toggle switch, which can be freely downloaded and used by teachers to facilitate the training of STEM students in synthetic biology.

scientific communication and education↗

bioRxiv2023

Thermodynamically-consistent, reduced models of gene regulatory networks

Synthetic biology aims to engineer novel functionalities into biological systems. While the approach has to date been predominantly applied to single cells, a richer set of biological phenomena can be engineered by applying synthetic biology to cell populations. To rationally design cell populations, we require mathematical models that link between intracellular biochemistry and intercellular interactions. In this study, we develop a kinetic model of gene expression that is suitable for incorporation into agent-based models of cell populations. To be scalable to large cell populations, models of gene expression should be both computationally efficient and compliant with the laws of physics. We satisfy the first requirement by applying a model reduction scheme to translation, and the second requirement by formulating models using bond graphs. Our reduced model is significantly faster to simulate than the full model, and faithfully reproduces important behaviours of the full model. We couple separate models of gene expression to build models of the toggle switch and repressilator. With these models, we explore the effects of resource availability and cell-to-cell heterogeneity on circuit behaviour. The modelling approaches developed in this study are a bridge towards engineering collective cell behaviours such as synchronisation and division of labour.

synthetic biology↗

bioRxiv2021

iGEM comes of age: trends in its research output.

The international Genetically Engineered Machine (iGEM) is an educational benchmark in synthetic biology. Eighteen years after its inception, it has also catalysed the infusion of synthetic biology with interdisciplinary fundamental and translational research as well as with inspired young scientists. Here, we communicate a quantitative analysis of compiled published work associated to iGEM projects, highlighting trends in their dissemination and versatility. As iGEM comes of age, we anticipate it will continue to revolutionise, alongside SynBio, several disciplines of science and industries through the development of synthetic biological systems towards a sustainable future.

scientific communication and education↗

bioRxiv2025

PIONEER: A Periplasmic Display Platform for Synthetic Biology-Based Screening of Genetically Encoded Protein Regulators

Periplasmic display in yeast is a promising but underdeveloped method for screening and studying genetically encoded biomolecules. We present PIONEER, a modular platform that localizes peptides, proteins, and nanobodies to the membrane-proximal periplasmic space of S. cerevisiae, enabling direct interrogation of membrane protein function. By optimizing secretion signals and display scaffolds, PIONEER ensures stable ligand retention and robust autocrine signaling. The ability to assess peptide and protein ligand activity through signaling, rather than binding alone, marks a key advance toward function-first screening. Applied to human G protein-coupled receptors (GPCRs), the system enables detection of surface expression, ligand activity profiling, and classification of nanobody regulators, including antagonists and conformational stabilizers. It also distinguishes intrabodies based on their functional effects and chaperone activity. Although demonstrated with GPCRs, PIONEER is adaptable to a wide range of membrane and soluble protein targets. As AI-driven design expands the space of candidate ligands and binders, this platform offers a scalable method for linking predicted sequences and their structures to biological function.

synthetic biology↗

bioRxiv2017

Population regulation in microbial consortia using dual feedback control

An ongoing area of study in synthetic biology has been the design and construction of synthetic circuits that maintain homeostasis at the population level. Here, we are interested in designing a synthetic control circuit that regulates the total cell population and the relative ratio between cell strains in a culture containing two different cell strains. We have developed a dual feedback control strategy that uses two separate control loops to achieve the two functions respectively. By combining both of these control loops, we have created a population regulation circuit where both the total population size and relative cell type ratio can be set by reference signals. The dynamics of the regulation circuit show robustness and adaptation to perturbations in cell growth rate and changes in cell numbers. The control architecture is general and could apply to any organism for which synthetic biology tools for quorum sensing, comparison between outputs, and growth control are available.

synthetic biology↗

bioRxiv2021

The effects of a synthetic and biological surfactant on the community composition and metabolic activity of a freshwater biofilm

Surfactants are used to control microbial biofilms in industrial and medical settings. Their known toxicity on aquatic biota and their longevity in the environment has encouraged research on biodegradable alternatives such as rhamnolipids. While previous research has investigated the effects of biological surfactants on single species biofilms, there remains a lack of information regarding the effects of synthetic and biological surfactants in freshwater ecosystems. We conducted a mesocosm experiment to test how the surfactant sodium dodecyl sulphate (SDS) and the biological surfactant rhamnolipid altered community composition and metabolic activity of freshwater biofilms. Biofilms were cultured in the flumes using lake water from Lake Lunz in Austria, under high (300 ppm) and low (150 ppm) concentrations of either surfactant over a four-week period. Our results show that both surfactants significantly affected microbial diversity. Up to 36% of microbial operational taxonomic units were lost after surfactant exposure. Rhamnolipid exposure also increased the production of the extracellular enzymes, leucine aminopeptidase and glucosidase, while SDS exposure reduced leucine aminopeptidase and glucosidase. This study demonstrates that exposure of freshwater biofilms to chemical and biological surfactants caused a reduction of microbial diversity and changes in biofilm metabolism, exemplified by shifts in extracellular enzyme activities. Key PointsO_LIMicrobial biofilm diversity decreased significantly after surfactant exposure. C_LIO_LIExposure to either surfactant altered extracellular enzyme activity. C_LIO_LIOverall metabolic activity was not altered, suggesting functional redundancy. C_LI

microbiology↗

bioRxiv2024

Dynamic regulation of engineered T7 RNA polymerases by endogenous metabolites

For many enzymes, activity is regulated post-translationally by endogenous metabolites. Designing liganded control of essential activities like transcription would advance predictive control of biological processes, a fundamental goal of synthetic biology. Here we demonstrate that full-length, single subunit T7-derived RNA polymerases (T7 RNAP) can be controlled by physiologically relevant concentrations of indoles. We used rational design and directed evolution to identify T7 RNAP variants with minimal transcriptional activity in the absence of indole, and a 29-fold increase in activity with an EC50 of 344 {micro}M. Indoles control T7-dependent gene expression exogenously, endogenously, and inter-cellularly. We also demonstrate indole-dependent bacteriophage viability and propagation in trans. Specificity of different indoles, T7 promoter specificities, and portability to different bacteria are shown. Our ligand activated RNA polymerases (LARPs) represent a new chemically inducible platform immediately deployable for novel synthetic biology applications, including for modulation of synthetic co-cultures.

bioengineering↗