Synthetic Biology

Tuning protein expression in non-model organisms is often constrained by the lack of validated genetic parts and predictive design tools. Translational tuning through the modulation of upstream untranslated regions (5'-UTRs) offers a potentially organism-agnostic route, but existing methods typically rely on mechanistic assumptions, prior knowledge that may not be available in non-model contexts, or the screening of sequence libraries. Here, we present a simple generative approach for creating synthetic 5'-UTR libraries based solely on the genomic sequence statistics of any desired organism. The method uses a sliding-window n-gram language model applied to native 5'-UTR sequences to produce novel sequences that preserve organism-specific base distributions and motifs without hard-coding specific motifs or mechanistic rules into inflexible statistical templates. We have applied this approach to the model bacterium Escherichia coli and the non-model probiotic Limosilactobacillus reuteri. Libraries of approximately 1,000 sequences were generated for each organism, from which about 100 unique sequences were experimentally tested for translation of a fluorescent reporter protein. In both organisms, the synthetic libraries yielded a broad range of translation levels from this relatively small number of tested variants. Sequences derived from an organisms own genomic statistics generally performed better in that organism than sequences derived from the other species. Correlations of individual sequence performance across the two species were weak, and thermodynamic predictions of ribosome binding strength showed very little predictive power, especially in the non-model L. reuteri. The results demonstrate that simple statistical language model approaches applied to genomic data can generate functional translational regulatory sequence libraries without detailed mechanistic knowledge or explicit reference to consensus motifs. The approach requires minimal computational resources, avoids reproducing native sequences, and can be readily applied to any organism with a sequenced genome. This strategy may lower technical barriers to expression tuning in non-model organisms.

Promoters play a key role in influencing transcriptional regulation for fine-tuning expression of genes. Heterologous promoter engineering has been a widely used concept to control the level of transcription in all model organisms. The strength of a promoter is mainly determined by its nucleotide composition. Many promoter libraries have been curated but few have attempted to develop theoretical methods to predict the strength of promoters from its nucleotide sequence. Such theoretical methods are not only valuable in the design of promoters with specified strength, but are also meaningful to understand the mechanism of promoters in gene transcription. In this study, we present a theoretical model to describe the relationship between promoter strength and nucleotide sequence in Saccharomyces cerevisiae. We infer from our analysis that the -49 to 10 sequence with respect to the Transcription Start Site represents the minimal region that can be used to predict the promoter strength. We present an online tool https://qpromoters.com/ that takes advantage of this fact to quickly quantify the strength of the promoters.

Sequence features, including the binding affinity of binding motifs for their cognate transcription factors, are important contributors to promoter behavior. The ability to predictably recode affinity enables the development of synthetic promoters with varying levels of response to known cellular signals. Here we describe a luminescence-based microplate assay for comparing the interactions of transcription factors with short DNA probes. We then demonstrate how this data can be used to design synthetic plant promoters of varying strengths that respond to the same transcription.

Foundational techniques in molecular biology--such as cloning genes, tagging biomolecules for purification or identification, and overexpressing recombinant proteins--rely on introducing non-native or synthetic DNA sequences into organisms. These sequences may be recognized by the transcription and translation machinery in their new context in unintended ways. The cryptic gene expression that sometimes results has been shown to produce genetic instability and mask experimental signals. Computational tools have been developed to predict individual types of gene expression elements, but it can be difficult for researchers to contextualize their collective output. Here, we introduce CryptKeeper, a software pipeline that visualizes predictions of bacterial gene expression signals and estimates the translational burden possible from a DNA sequence. We investigate several published examples where cryptic gene expression in E. coli interfered with experiments. CryptKeeper accurately postdicts unwanted gene expression from both eukaryotic virus infectious clones and individual proteins that led to genetic instability. It also identifies off-target gene expression elements that resulted in truncations that confounded protein purification. Incorporating negative design using CryptKeeper into reverse genetics and synthetic biology workflows can help to mitigate cloning challenges and avoid unexplained failures and complications that arise from unintentional gene expression.

Histone proteins are decorated with a combinatorially and numerically diverse set of biochemical modifications. Here we describe a versatile and scalable platform termed Rapid interrogation of Epigenome Modifications using Yeast surface display (REMY), which enables efficient characterization of histone modifications without the need for recombinant protein production. As proof-of-concept, we first used REMY to rapidly profile the histone H3 and H4 residue writing specificities of the human histone acetyltransferase, p300. Subsequently, we used REMY to screen a large panel of commercially available anti-acetylation antibodies for their specificities, identifying many suitable and unsuitable reagents. Further, use of REMY enabled efficient mapping of the large binary crosstalk space between acetylated residues on histones H3 and H4, and uncovered previously unreported residue interdependencies affecting p300 activity. Our results show that REMY is a useful tool that can advance our understanding of chromatin biology by enabling efficient interrogation of the complexity of epigenome modifications.

Plant synthetic biology is a fast-evolving field that employs engineering principles to empower research and bioproduction in plant systems. Nevertheless, in the whole synthetic biology landscape, plant systems lag compared to microbial and mammalian systems. When it comes to multigene delivery to plants, the predictability of the outcome is decreased since it depends on three different chassis: E. coli, Agrobacterium, and the plant species. Here we aimed to develop standardised and streamlined tools for genetic engineering in plant synthetic biology. We have devised Mobius Assembly for Plant Systems (MAPS), a user-friendly Golden Gate Assembly system for fast and easy generation of complex DNA constructs. MAPS is based on a new group of small plant binary vectors (pMAPs) that contains an origin of replication from a cryptic plasmid of Paracoccus pantotrophus. The functionality of the pMAP vectors was confirmed by transforming the MM1 cell culture, demonstrating for the first time that plant transformation is dependent on the Agrobacterium strains and plasmids; plasmid stability was highly dependent on the plasmid and bacterial strain. We made a library of new short promoters and terminators and characterised them using a high-throughput protoplast expression assay. Our results underscored the strong influence of terminators in gene expression, and they altered the strength of promoters in some combinations and indicated the presence of synergistic interactions between promoters and terminators. Overall this work will further facilitate plant synthetic biology and contribute to improving its predictability, which is challenged by combinatorial interactions among the genetic parts, vectors, and chassis.

Mathematical models can aid the design of genetic circuits, but may yield inaccurate results if individual parts are not modeled at the appropriate resolution. To illustrate the importance of this concept, we study transcriptional cascades consisting of two inducible synthetic transcription factors connected in series. Despite the simplicity of this design, we find that accurate prediction of circuit behavior requires mapping the dose responses of each circuit component along the dimensions of both its expression level and its inducer concentration. With such multidimensional characterizations, we were able to computationally explore the behavior of 16 different circuit designs. We experimentally verified a subset of these predictions and found substantial agreement. This method of biological part characterization enables the use of models to identify (un)desired circuit behaviors prior to experimental implementation, thus shortening the design-build-test cycle for more complex circuits.Competing Interest StatementThe authors have declared no competing interest.AbbreviationsiSynTFinducible synthetic transcription factorYFPyellow fluorescent proteinGEMGal4 DNA binding domain, estradiol ligand binding domain, Msn2 activating domainZ3PMZ3 DNA binding domain, progesterone ligand binding domain, Msn2 activating domainZ4EMZ4 DNA binding domain, estradiol ligand binding domain, Msn2 activating domainView Full Text

Golden Gate assembly is a requisite method in synthetic biology that facilitates critical conventions such as genetic part abstraction and rapid prototyping. However, compared to robotic implementation, manual Golden Gate implementation is cumbersome, error-prone, and inconsistent for complex assembly designs. AssemblyTron is an open-source python package that provides an affordable automation solution using open-source Opentrons OT-2 lab robots. Automating Golden Gate assembly with AssemblyTron can reduce failure-rate, resource consumption, and training requirements for building complex DNA constructs, as well as indexed and combinatorial libraries. Here, we dissect a panel of upgrades to AssemblyTrons Golden Gate assembly capabilities, which include Golden Gate assembly into modular cloning part vectors, error-prone PCR combinatorial mutant library assembly, and modular cloning indexed plasmid library assembly. These upgrades enable a broad pool of users with varying levels of experience to readily implement advanced Golden Gate applications using low-cost, open-source lab robotics.

Reporter cell assays, such as those used to detect estrogenic chemicals, can detect target chemicals at low concentrations and can be used to analyze chemical mixtures without a priori knowledge of the mixture components. However, the outputs of these assays are affected by biological variability, which complicates their interpretation. Here, we describe and demonstrate a workflow that is useful for determining potential sources of biological variability and optimizing the performance of cell-based assays. The workflow involves developing an appropriate mathematical model for a transcriptional activation assay, calibrating it with experimental data, and conducting sensitivity analysis to characterize individual components of the genetic circuit based on their effect on the reporter signal output. This workflow was tested using an estrogen receptor transcriptional activation assay. For this circuit, our analysis predicts that controlling estrogen response element number, promoter strength, and reporter signal degradation rates minimizes reporter output variability. We show that careful model development, calibration, and analysis can offer biologically relevant insights to minimize the variability of cell-based assays and improve genetic circuits for increased sensitivity and dynamic range.

PurposeTargeted regulation of transfected extra-chromosomal plasmid DNA typically requires the integration of 9 - 20 bp docking sites into the plasmid. Here, we report an elegant approach, The Dpn Adaptor Linked Effector (DAL-E) system, to target fusion proteins to 6-methyladenosine in GATC, which appears frequently in popular eukaryotic expression vectors and is absent from endogenous genomic DNA. Methods: The DNA-binding region from the DpnI endonuclease binds 6-methyladenosine within the GATC motif. We used a Dpn-transcriptional activator (DPN7-TA) fusion to induce gene expression from transiently transfected pDNAs. ResultsWe validated methylation-dependent activity of DPN7-TA with a panel of target pDNAs. We observed stronger transactivation when GATC targets were located upstream of the transcriptional start site in the target pDNA. Conclusion: DAL-E, consisting of a 108 aa, 12 kD DNA-binding adaptor and a 4 bp recognition site, offers a genetically-tractable, tunable system that can potentially be redesigned to recruit a variety of regulators (e.g. activators, silencers, epigenome editors) to transfected plasmid DNA. LAY SUMMARYTransfection of plasmid DNA (pDNA) is a commonly used method for introducing exogenous genetic material into mammalian cells. Once introduced into cells not all pDNAs express this genetic material at sufficient levels. Current techniques to improve transgene expression are limited and are not always feasible for all plasmids. This report presents a new method to improve gene expression from pDNA. The Dpn Adaptor Linked Effector (DAL-E) binds to methylated adenines in the pDNA resulting in increased expression. This technique has exciting implications for improved genetic engineering of mammalian cells. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=124 HEIGHT=200 SRC="FIGDIR/small/466616v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1116db0org.highwire.dtl.DTLVardef@1386f99org.highwire.dtl.DTLVardef@26c63corg.highwire.dtl.DTLVardef@1a09673_HPS_FORMAT_FIGEXP M_FIG C_FIG

High-quality plasmid DNA purification at high throughput remains a significant bottleneck in molecular biology and bioengineering. Current methods frequently fail to deliver sufficient yields of pure, transfection-grade DNA required for genetic engineering applications in mammalian cells. Here, we present a Biofoundry-based automated pipeline using the CyBio FeliX robotic liquid handling platform to rapidly purify plasmid DNA with minimal manual intervention. The protocol leverages Solid Phase Reversible Immobilisation (SPRI)-based magnetic bead technology to ensure consistency, scalability, and DNA purity suitable for downstream viral particle production and mammalian cell transfection. The pipeline supports flexible processing of between 8 and 96 samples per run, making it adaptable across a wide range of experimental scales. The protocol is openly available via Earlham Institute GitHub repository, enabling broad adoption across the bioscientific community and contributing to the growing toolkit of reproducible, scalable engineering biology workflows. In this work, we employed an integrated robotic pipeline to process 528 pooled DNA plasmids and built a Lentiviral DNA plasmid library for lineage tracing, validated the library by sequencing, and demonstrated efficacy in downstream mammalian cell transfection experiments.

Large datasets of biomolecular activities are crucial for protein engineering, yet their scarcity due to limited experimental throughput hampers progress. We introduce Direct High-throughput Activity Recording and Measurement Assay (DHARMA), an innovative method enabling ultra-high-throughput measurement of biomolecular activities. DHARMA employs molecular recording techniques to link activity directly to editing rates of DNA segments contiguous with the coding sequence of biomolecule of interest. Leveraging a Bayesian inference-based denoising model, we mapped the fitness landscape of TEV protease across 160,000 variants. Using these datasets, we benchmarked popular protein models and showed the impact of data size on model performance. We also developed circuit self-optimization strategies and demonstrated DHARMAs capability to measure a wide range of biomolecular activities. DHARMA represents a leap forward, offering the machine learning community unparalleled datasets for accurate protein fitness prediction and enhancing our understanding of sequence-to-function relationships. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/483646v4_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@47425borg.highwire.dtl.DTLVardef@557a0corg.highwire.dtl.DTLVardef@1bfc091org.highwire.dtl.DTLVardef@1fb7fdf_HPS_FORMAT_FIGEXP M_FIG C_FIG

A challenge in the design and construction of synthetic genetic circuits is that they will operate within biological systems that have noisy and changing parameter regimes that are largely unmeasurable. The outcome is that these circuits do not operate within design specifications or have a narrow operational envelope in which they can function. This behavior is often observed as a lack of reproducibility in function from day to day or lab to lab. Moreover, this narrow range of operating conditions does not promote reproducible circuit function in deployments where environmental conditions for the chassis are changing, as environmental changes can affect the parameter space in which the circuit is operating. Here we describe a computational method for assessing the robustness of circuit function across broad parameter regions. Previously designed circuits are assessed by this computational method and then circuit performance is measured across multiple growth conditions in budding yeast. The computational predictions are correlated with experimental findings, suggesting that the approach has predictive value for assessing the robustness of a circuit design.

1Modern scientific enterprises are often highly complex and multidisciplinary, particularly in areas like synthetic biology where the subject at hand is itself inherently complex and multidisciplinary. Collaboration across many organizations is necessary to efficiently tackle such problems [6, 15], but remains difficult. The challenge is further amplified by automation that increases the pace at which new information can be produced, and particularly so for matters of fundamental research, where concepts and definitions are inherently fluid and may rapidly change as an investigation evolves [7]. The DARPA program Synergistic Discovery and Design (SD2) aimed to address these challenges by organizing the development of data-driven methods to accelerate discovery and improve design robustness, with one of the key domains under study being synthetic biology. The program was specifically organized such that teams provided complementary types of expertise and resources, and without any team being in a dominant organizational position, such that subject-matter investigations would necessarily require peer-level collaboration across multiple team boundaries. With more than 100 researchers across more than 20 organizations, several of which ran experimental facilities with high-throughput automation, participants were forced to confront challenges around effective data sharing. The default architecture for scientific collaboration is essentially one of anarchy, with ad-hoc bilateral relations between pairs of collaborators or experimental phases (Figure 1(a)). This was by necessity the case during early phases of the SD2 program as well, in which incorporating new tools into pipelines was ad-hoc and time-consuming, and data was generally disconnected from genetic designs and experimental plans. The other typical approach for collaboration is one of "command and control", in which a dominant organization determines the data sharing content and format for all participants (Figure 1(b)). This can be efficient, but tends to be limited in flexibility and extensibility, rendering it unsuitable for research collaboration, as indeed was found when we attempted this approach during the first year of the SD2 program. We addressed these problems with the application of distributed standards to create a "flexible rendezvous" model of collaboration (Figure 1(c)), enabling information flow to track evolving collaborative relationships, improving the sharing and utility of information across the community and supporting accelerated rates of experimentation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/460644v1_fig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@fc7371org.highwire.dtl.DTLVardef@1ff2733org.highwire.dtl.DTLVardef@66af53org.highwire.dtl.DTLVardef@1809640_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Architectures for data sharing: bilateral relations (a), command and control (b), and flexible rendezvous (c). C_FIG

Despite the fact that microbes in natural environments spend most of their time in growth arrest, we understand little about how this physiological state affects the performance of engineered genetic circuits. Here, we measure repression curves from a library of genetic NOT gates at single-cell resolution in Escherichia coli under both active growth and growth arrest to systematically investigate how growth arrest affects circuit behavior. We find that the impact of growth arrest on circuit performance is almost entirely dominated by a single effect: a >100-fold reduction in unrepressed expression levels. Growth arrest caused gene expression noise to increase moderately and had only minimal impacts on the sensitivity and sharpness of the repression curves. Our work shows both that conventional genetic circuit design paradigms are currently insufficient to develop circuits that can function properly under growth arrest, but also that addressing the reduction in just a single performance parameter would be sufficient to resolve this problem. This work expands our understanding of bacterial gene regulation under growth arrest and lays the groundwork for new design paradigms that will be essential in ensuring the safe and reliable performance of synthetic biology systems in real-world environments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/703179v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@3df103org.highwire.dtl.DTLVardef@9a2f5forg.highwire.dtl.DTLVardef@9c15aborg.highwire.dtl.DTLVardef@1529c39_HPS_FORMAT_FIGEXP M_FIG C_FIG

Legitimacy screening, the process of verifying the identity and purpose of customers ordering synthetic nucleic acids, is a primary safeguard against the misuse of synthetic biology. However, the associated costs discourage the adoption of screening practices. To evaluate whether AI tools can facilitate this process, we tested five large language models on five verification tasks using customer profiles of life sciences researchers from around the world. We compared AI performance against an expert human baseline on flag accuracy, source quality, source fidelity, and cost. The best-performing model, Gemini 2.5 Pro aided by four bibliographic and sanctions APIs, achieved comparable flag accuracy to the human baseline (90% and 89%, respectively). Gemini 2.5 Pro outperformed the human baseline on source quality and fidelity, at roughly one-tenth of the cost ($1.18 vs. $14.04 per customer). For information-gathering tasks, which excluded the human review step, costs averaged $0.23 per customer, around 50 times cheaper than human screening. These results support piloting AI-assisted legitimacy screening at providers of synthetic nucleic acids and other dual-use biotechnology products, with AI systems handling information gathering and human reviewers retaining authority over order fulfillment decisions.

Saccharomyces cerevisiae is an invaluable model organism for both fundamental biological research and biotechnological applications including recombinant protein production as well as protein and metabolic engineering. We previously developed a modular cloning (MoClo) based toolkit for S. cerevisiae that facilitates rapid optimization of signal peptides and anchor proteins for efficient secretion and/or surface display of heterologous proteins of interest. Here we describe further improvements and applications of this yeast secretion and display (YSD) toolkit. New parts encoding anchor proteins based on N-terminal fusion to a truncated Aga1 and C-terminal fusion to Aga2, each with three possible epitope tag options, are described. We also added parts that facilitate high throughput detection of secreted proteins of interest through GFP fluorescence complementation and parts encoding "secretion boosting" yeast proteins, whose overexpression has previously been reported to enhance secretion of heterologous proteins. In addition, two surface display applications of the toolkit are showcased. We demonstrate that yeast surface display of an anti-GFP nanobody allows cost-effective evaluation of the interactions of GFP-tagged proteins of interest, either by flow cytometry or yeast-based co-immunoprecipitation. In addition, using yeast cells as whole-cell catalysts, we show that co-display of the poly(ethylene terephthalate) (PET) degrading enzyme leaf-branch compost cutinase with hydrophobin1 enhances the breakdown of PET plastic, while triple co-display of these proteins with MHETase causes complete conversion of the intermediary monohydroxyLethyl-terephthalate (MHET) to terephthalic acid. The diverse applications described herein demonstrate the broad applications of the updated MoClo YSD toolkit 2.0 in both synthetic biology and other research fields.

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 cost-effective and rapid 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, or by titrating DNA concentrations rather than relying on limited plasmid copy numbers. Because this method retains a cell-like environment, the function of the genetic part will typically mimic its function in whole cells. SUMMARYWell-characterized genetic parts are necessary for the design of novel genetic circuits. Here we describe a cost-effective, high-throughput method for rapidly characterizing genetic parts. Our method reduces cost and time by combining cell-free lysates, linear DNA to avoid cloning, and acoustic liquid handling to increase throughput and reduce reaction volumes.

Coordinating multiple liquid handling robots is a complex logistical task when designing biological experiments. Protocol designers must consider the capabilities and constraints of each robot to distribute work optimally across multiple instruments. We developed an optimization framework that finds optimal liquid handling solutions that leverage an arbitrary number of robots. Our algorithm, called Pourfecto, abstracts the capabilities of each robot and their labware, allowing us to plan and schedule a wide range of biological experiments using commercial instruments and custom-built hardware. Pourfecto can optimize multiple objectives (minimum transfers, fewest reagents, fewest labware swaps) and scales to experiments with hundreds of thousands of liquid transfers.

Saccharomyces cerevisiae is a widely used biotechnological workhorse in both academic and industrial settings. One reason for its continued popularity is the extensive legacy of genetic tools, developed over its long history of use, that enable precise manipulation of the S. cerevisiae genome. These tools have enabled extensive genetic characterisation and dramatic re-programming efforts for applications ranging from fundamental research to industrial chemical production. Here we present a digital toolkit called PYEAST (Python Enabled Automated Strain Transformation) that encodes some of the most widely used methods for working with S. cerevisiae and modernizes them to leverage advances in DNA synthesis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/655004v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@4584feorg.highwire.dtl.DTLVardef@1e65caorg.highwire.dtl.DTLVardef@1acbe2borg.highwire.dtl.DTLVardef@1f91abc_HPS_FORMAT_FIGEXP M_FIG C_FIG