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bioRxiv2023

Genetic modification strategies for electroporation and CRISPR-Cas-based technologies in the non-competent Gram-negative bacterium Acinetobacter sp. Tol 5

Environmental isolates are promising candidates for new chassis of synthetic biology because of their inherent conversion capabilities and resilience to environmental stresses; however, many remain genetically intractable and unamenable to established genetic tools tailored for model bacteria. Acinetobacter sp. Tol 5 possesses intriguing properties for use in synthetic biology applications. However, genetic manipulation via electroporation is hindered by its low transformation efficiency. This study demonstrated the genetic refinement of the Tol 5 strain, achieving efficient transformation via electroporation. We deleted two genes encoding restriction enzymes. The resulting mutant strain not only exhibited marked efficiency of electrotransformation but also proved receptive to both in vitro and in vivo DNA assembly technologies, thereby facilitating the construction of recombinant DNA. In addition, we successfully adapted a CRISPR-Cas9-based base-editing platform developed for other Acinetobacter species. Our genetic modification strategy allows for the domestication of non-model bacteria, streamlining their utilization in synthetic biology applications.

synthetic biology↗

bioRxiv2024

CHARACTERIZATION OF RECOMBINASE ACTIVITY ACROSS CELLULAR GROWTH PHASES

Recombinases, which are enzymes that catalyze targeted DNA modifications, hold significant potential in synthetic biology. Their capacity to precisely manipulate genetic material enables the construction of complex genetic circuits that can be dynamically reconfigured in response to environmental stimuli. Such capabilities are essential for developing synthetic organisms tailored for specific functions, including biosensing, bioremediation, and pharmaceutical production. Therefore, characterizing the dynamics of recombinases is crucial for the innovative design of cellular devices. A deeper understanding of how recombinases interact with DNA in various conditions can improve the efficiency and control of genetic modifications, thereby enhancing both the functionality and reliability of synthetic biological systems. This study presents a detailed examination of the dynamics and efficiency of the serine recombinase Bxb1, focusing on its behavior under controlled expression in Escherichia coli. It highlights the significant influence of cellular growth phases (exponential and stationary) on the efficiency of recombinase-mediated gene expression. Our findings show that recombinase activity is maintained during stationary phase, which is critical to ensure ongoing recombination without the need for the continuous presence of an inducer. In experiments, we quantified the recombination efficiency of Bxb1 by monitoring expression of green fluorescent protein (GFP) as a reporter. Optimal expression of Bxb1, which maximized the recombination efficiency, occurred during exponential phase. However, once the culture reached stationary phase, accumulated Bxb1 continued to facilitate recombination, although GFP expression levels plateaued due to reduced cellular activity. These insights are relevant for synthetic biology applications where precise control of genetic functions is necessary.

synthetic biology↗

bioRxiv2020

Verification of genetic engineering in yeasts with nanopore whole genome sequencing

Yeast genomes can be assembled from sequencing data, but genome integrations and episomal plasmids often fail to be resolved with accuracy, completeness, and contiguity. Resolution of these features is critical for many synthetic biology applications, including strain quality control and identifying engineering in unknown samples. Here, we report an integrated workflow, named Prymetime, that uses sequencing reads from inexpensive NGS platforms, assembly and error correction software, and a list of synthetic biology parts to achieve accurate whole genome sequences of yeasts with engineering annotated. To build the workflow, we first determined which sequencing methods and software packages returned an accurate, complete, and contiguous genome of an engineered S. cerevisiae strain with two similar plasmids and an integrated pathway. We then developed a sequence feature annotation step that labels synthetic biology parts from a standard list of yeast engineering sequences or from a custom sequence list. We validated the workflow by sequencing a collection of 15 engineered yeasts built from different parent S. cerevisiae and nonconventional yeast strains. We show that each integrated pathway and episomal plasmid can be correctly assembled and annotated, even in strains that have part repeats and multiple similar plasmids. Interestingly, Prymetime was able to identify deletions and unintended integrations that were subsequently confirmed by other methods. Furthermore, the whole genomes are accurate, complete, and contiguous. To illustrate this clearly, we used a publicly available S. cerevisiae CEN.PK113 reference genome and the accompanying reads to show that a Prymetime genome assembly is equivalent to the reference using several standard metrics. Finally, we used Prymetime to resequence the nonconventional yeasts Y. lipolytica Po1f and K. phaffii CBS 7435, producing an improved genome assembly for each strain. Thus, our workflow can achieve accurate, complete, and contiguous whole genome sequences of yeast strains before and after engineering. Therefore, Prymetime enables NGS-based strain quality control through assembly and identification of engineering features.

synthetic biology↗

bioRxiv2022

AssemblyTron: Flexible automation of DNA assembly with Opentrons OT-2 lab robots

As one of the newest fields of engineering, synthetic biology relies upon a trial-and-error Design-Build-Test-Learn approach to simultaneously learn how function is encoded in biology and attempt to engineer it. Many software and hardware platforms have been developed to automate, optimize, and algorithmically perform each step of the Design-Build-Test-Learn cycle. However, there are many fewer options for automating the Build step. Build typically involves DNA assembly, which remains manual, low throughput, and unreliable in most cases, limiting our ability to advance the science and engineering of biology. Here, we present AssemblyTron: an open-source python package to integrate j5 DNA assembly design software outputs with build implementation in Opentrons liquid handling robotics with minimal human intervention. We demonstrate the versatility of AssemblyTron through several scarless, multipart DNA assemblies beginning from fragment amplification. We show that AssemblyTron can perform PCRs across a range of fragment lengths and annealing temperatures by using an optimal annealing temperature gradient calculation algorithm. We then demonstrate that AssemblyTron can perform Golden Gate and homology-dependent in vivo assemblies with comparable fidelity to manual assemblies by simultaneously building four four-fragment assemblies of chromoprotein reporter expression plasmids. Finally, we used AssemblyTron to perform site-directed mutagenesis reactions via homology-dependent in vivo assembly also achieving comparable fidelity to manual assemblies as assessed by sequencing. AssemblyTron can reduce the time, training, costs, and wastes associated with synthetic biology, which along with open-source and affordable automation, will further foster the accessibility of synthetic biology and accelerate biological research and engineering.

synthetic biology↗

bioRxiv2020

Robustness testing and scalability of phosphate regulated promoters useful for two-stage autoinduction in E. coli

A key challenge in synthetic biology is the successful utilization of characterized parts, such as promoters, in different biological contexts. We report the robustness testing of a small library of E. coli PhoB regulated promoters that enable heterologous protein production in two-stage cultures. Expression levels were measured both in a rich Autoinduction Broth as well as a minimal mineral salts media. Media dependent differences were promoter dependent. 4 out of 16 promoters tested were identified to have tightly controlled expression which was also robust to media formulation. Improved promoter robustness led to more predictable scale up and consistent expression in instrumented bioreactors. This subset of PhoB activated promoters, useful for two-stage autoinduction, highlight the impact of the environment on the performance of biological parts, and the importance of robustness testing in synthetic biology. HighlightsO_LICharacterization of the impact of media on promoter activity C_LIO_LIIdentification of promoters robust to environmental variables C_LIO_LIIdentification of promoters whose expression scale from microtiter plates to bioreactors C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=62 SRC="FIGDIR/small/920280v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@94f035org.highwire.dtl.DTLVardef@264edaorg.highwire.dtl.DTLVardef@102ee30org.highwire.dtl.DTLVardef@163d16e_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

synthetic biology↗

bioRxiv2022

An intelligent synthetic bacterium with sound-integrated ability for chronological toxicant detection, degradation, and lethality

Modules, toolboxes, and systems of synthetic biology are being designed to solve environmental problems. However, weak and decentralized functional modules require complicated controls. To address this issue, we investigated an integrated system that can complete detection, degradation, and lethality, in chronological order without exogenous inducers. Biosensors were optimized by regulating expression of receptor and reporter to get higher sensitivity and output signal. Several stationary-phase promoters were selected and compared, while promoter Pfic was chosen to express the degradation enzyme. We created two concepts of lethal circuits by testing various toxic proteins, with a toxin/antitoxin circuit showing a potent lethal effect. Three modules were coupled, step-by-step. Detection, degradation, and lethality were sequentially completed, and the modules had partial attenuation compared to pre-integration, except for degradation. Our study provides a novel concept for integrating and controlling functional modules, which can accelerate the transition of synthetic biology from a concept to practical applications. TeaserWe provide new ideas for integration and chronological control of multiple modules in synthetic biology.

synthetic biology↗

bioRxiv2025

Causes and consequences of experimental variation in Nicotiana benthamiana transient expression

Infiltration of Agrobacterium tumefaciens into Nicotiana benthamiana has become a foundational technique in plant biology, enabling efficient delivery of transgenes in planta with technical ease, robust signal, and relatively high throughput. Despite transient expressions prevalence in disciplines such as synthetic biology, little work has been done to describe and address the variability inherent in this system, a concern for experiments that rely on highly quantitative readouts. In a comprehensive analysis of N. benthamiana agroinfiltration experiments, we model sources of variability that affect transient expression. Our findings emphasize the need to validate normalization methods under the specific conditions of each study, as distinct normalization schemes do not always reduce variation either within or between experiments. Using a dataset of 1,915 plants collected over three years, we develop a model of variation in N. benthamiana transient expression, using power analysis to determine the number of individual plants required for a given effect size. Drawing on our longitudinal data, these findings inform practical guidelines for minimizing variability through strategic experimental design and power analysis, providing a foundation for more robust and reproducible use of N. benthamiana in quantitative plant biology and synthetic biology applications.

plant biology↗

bioRxiv2021

Komagataeibacter tool kit (KTK): a modular cloning system for multigene constructs and programmed protein secretion from cellulose producing bacteria

Bacteria proficient at producing cellulose are an attractive synthetic biology host for the emerging field of Engineered Living Materials (ELMs). Species from the Komagataeibacter genus produce high yields of pure cellulose materials in a short time with minimal resources, and pioneering work has shown that genetic engineering in these strains is possible and can be used to modify the material and its production. To accelerate synthetic biology progress in these bacteria, we introduce here the Komagataeibacter tool kit (KTK), a standardised modular cloning system based on Golden Gate DNA assembly that allows DNA parts to be combined to build complex multigene constructs expressed in bacteria from plasmids. Working in Komagataeibacter rhaeticus, we describe basic parts for this system, including promoters, fusion tags and reporter proteins, before showcasing how the assembly system enables more complex designs. Specifically, we use KTK cloning to reformat the Escherichia coli curli amyloid fibre system for functional expression in K. rhaeticus, and go on to modify it as a system for programming protein secretion from the cellulose producing bacteria. With this toolkit, we aim to accelerate modular synthetic biology in these bacteria, and enable more rapid progress in the emerging ELMs community.

synthetic biology↗

bioRxiv2023

Machine learning for optimization of multiscale biological circuits

Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signalling pathways and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian Optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space towards an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and hence provides a feasible approach to solve a highly non-convex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.

synthetic biology↗

bioRxiv2021

Development of a Freeze-Dried CRISPR-Cas12 Sensor for Detecting Wolbachia in the Secondary Science Classroom

Training the future synthetic biology workforce requires opportunity and exposure to biotechnology concepts and activities in secondary education. Detecting Wolbachia bacteria in arthropods using PCR has become a common way for secondary students to investigate and apply DNA technology in the science classroom. Despite this framework, cutting-edge biotechnologies like CRISPR-based diagnostics have yet to be widely implemented in the classroom. To address this gap, we present a freeze-dried CRISPR-Cas12 sensing reaction to complement traditional DNA technology education and teach synthetic biology concepts. The reactions accurately detect Wolbachia from arthropod-derived PCR samples in under 2 hours and can be stored at room temperature for over a month without appreciable degradation. The reactions are easy-to-use and cost less than $40 to implement for a classroom of 22 students including the cost of reusable equipment. We see this technology as an accessible way to incorporate synthetic biology education into existing biology curriculum, which will expand biology educational opportunities in science, technology, engineering, and mathematics (STEM) education.

synthetic biology↗

bioRxiv2024

Engineered Receptors for Soluble Cell-to-Cell Communication

Despite recent advances in mammalian synthetic biology, there remains a lack of modular synthetic receptors that can robustly respond to soluble ligands and in turn activate cellular functions. Such receptors would have extensive clinical potential to regulate the activity of engineered therapeutic cells, but to date only receptors against cell surface targets have approached clinical translation. To address this gap, we developed a receptor architecture based on synNotch, called SyNthetic Intramembrane Proteolysis Receptor (SNIPR), that has the added ability to be activated by soluble ligands, both natural and synthetic, with remarkably low baseline activity and high fold activation. SNIPRs are able to access an endocytic, pH-dependent cleavage mechanism to achieve soluble ligand sensing, in addition to employing a canonical-like pathway for detecting surface-bound ligands. We demonstrate the therapeutic capabilities of the receptor platform by localizing the activity of CAR T-cells to solid tumors where soluble disease-associated factors are expressed, bypassing the major hurdle of on-target off-tumor toxicity in bystander organs. We further applied the SNIPR platform to engineer fully synthetic signaling networks between cells orthogonal to natural signaling pathways, expanding the scope of synthetic biology. Our design framework enables cellular communication and environmental interactions, extending the capabilities of synthetic cellular networking in clinical and research contexts.

bioengineering↗

bioRxiv2025

When good guides go bad: empirical evaluation of all unique Cas9 targets in E. coli reveal widespread functionality and rules for gRNA biological design

The Cas9 nuclease has become central to modern methods and technologies in synthetic biology, largely due to the ease in which it can be targeted to specific DNA loci via guide RNAs (gRNAs). Reports vary widely on the actual specificity of this targeting, with some studies observing 60% of gRNAs possessing no activity against the genome, yet an assumption that inactive gRNAs are rare persists in the E. coli community. To resolve these contradictions, we evaluated the activity of nearly 500,000 unique gRNAs in the E. coli K12 MG1655 genome. We show that the overwhelming majority (at least 93%) of unique gRNAs are functional while only 0.3% are nonfunctional.These nonfunctional gRNAs exhibit strong spacer self-interaction, which can be either excluded using a simple design rule or "repaired" during library design. Finally, this work provides the greater microbial synthetic biology community both a set of nearly half a million E. coli gRNAs that have been empirically evaluated in vivo as well as a thoroughly evaluated experimental procedure, complete with appropriate controls for Cas9 activity, for conducting Cas9 assays in E. coli specifically and bacteria more generally. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/651106v3_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1038ddcorg.highwire.dtl.DTLVardef@566e37org.highwire.dtl.DTLVardef@5df6b2org.highwire.dtl.DTLVardef@ef453c_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

SynBioGPT: A Retrieval-Augmented Large Language Model Platform for AI-Guided Microbial Strain Development

Synthetic biology seeks to engineer microbial cell factories for sustainable bioproduction, yet the optimization of these systems is impeded by the complexity of metabolic engineering and the protracted timelines of iterative design-build-test-learn (DBTL) cycles. Traditional computational approaches, such as constraint-based modeling, provide valuable insights but demand extensive manual curation. Large language models (LLMs) hold promises for automating knowledge extraction and strain design, yet conventional models like GPT-4 suffer from outdated corpora and hallucination errors in domain-specific tasks. SynBioGPT v1.0, a Retrieval-Augmented Generation (RAG)-enhanced LLM, enhanced knowledge retrieval using vector search but often retrieved semantically similar yet contextually irrelevant documents. Here, we introduce SynBioGPT v2.0 (https://synbiogpt.biodesign.ac.cn), which mitigates these limitations by decomposing queries into sub-questions and employing keyword-based searches. Tested on a 100-question synthetic biology benchmark, SynBioGPT v2.0 achieved 98% accuracy with the Claude-3.7-sonnet backend, a 10% improvement over v1.0s 88% with Llama3-8B-Instruct. This advance highlight the efficacy of query decomposition and precise retrieval in enhancing LLM utility for synthetic biology.

synthetic biology↗

bioRxiv2016

A new improved and extended version of the multicell bacterial simulator gro

gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use and provide new functionalities. The new version of gro is between one and two orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 20 minutes. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.

synthetic biology↗

bioRxiv2019

Engineered phenotype patterns in microbial populations

Rapid advances in cellular engineering1,2 have positioned synthetic biology to address therapeutic3,4 and industrial5 problems, but a significant obstacle is the myriad of unanticipated cellular responses in heterogeneous environments such as the gut6,7, solid tumors8,9, bioreactors10 or soil11. Complex interactions between the environment and cells often arise through non-uniform nutrient availability, which can generate bidirectional coupling as cells both adjust to and modify their local environment through different growth phenotypes across a colony.12,13 While spatial sensing14 and gene expression patterns15-17 have been explored under homogeneous conditions, the mutual interaction between gene circuits, growth phenotype, and the environment remains a challenge for synthetic biology. Here, we design gene circuits which sense and control spatiotemporal phenotype patterns in a model system of heterogeneous microcolonies containing both growing and dormant bacteria. We implement pattern control by coupling different downstream modules to a tunable sensor module that leverages E. colis stress response and is activated upon growth arrest. One is an actuator module that slows growth and thereby creates an environmental negative feedback via nutrient diffusion. We build a computational model of this system to understand the interplay between gene regulation, population dynamics, and chemical transport, which predicts oscillations in both growth and gene expression. Experimentally, this circuit indeed generates robust cycling between growth and dormancy in the interior of the colony. We also use the stress sensor to drive an inducible gating module that enables selective gene expression in non-dividing cells. The stress-gated lysis circuit derived from this module radically alters the growth pattern through elimination of the dormant phenotype upon a chemical cue. Our results establish a strategy to leverage and control the presence of distinct microbial growth phenotypes for synthetic biology applications in complex environments.

synthetic biology↗

bioRxiv2017

Rapid and scalable preparation of bacterial lysates for cell-free gene expression

Cell-free gene expression systems are emerging as an important platform for a diverse range of synthetic biology and biotechnology applications, including production of robust field-ready biosensors. Here, we combine programmed cellular autolysis with a freeze-thaw or freeze-dry cycle to create a practical, reproducible, and a labor- and cost-effective approach for rapid production of bacterial lysates for cell-free gene expression. Using this method, ro-bust and highly active bacterial cell lysates can be produced without specialized equipment at a wide range of scales, making cell-free gene expression easily and broadly accessible. More-over, live autolysis strain can be freeze-dried directly and subsequently lysed upon rehydration to produce active lysate. We demonstrate the utility of autolysates for synthetic biology by reg-ulating protein production and degradation, implementing quorum sensing, and showing quan-titative protection of linear DNA templates by GamS protein. To allow versatile and sensitive {beta}-galactosidase (LacZ) based readout we produce autolysates with no detectable background LacZ activity and use them to produce sensitive mercury(II) biosensors with LacZ-mediated colorimetric and fluorescent outputs. The autolysis approach can facilitate wider adoption of cell-free technology for cell-free gene expression as well as other synthetic biology and biotechnology applications, such as metabolic engineering, natural product biosynthesis, or proteomics.

synthetic biology↗

bioRxiv2024

DNA-Diffusion: Leveraging Generative Models for Controlling Chromatin Accessibility and Gene Expression via Synthetic Regulatory Elements

The challenge of systematically modifying and optimizing regulatory elements for precise gene expression control is central to modern genomics and synthetic biology. Advancements in generative AI have paved the way for designing synthetic sequences with the aim of safely and accurately modulating gene expression. We leverage diffusion models to design context-specific DNA regulatory sequences, which hold significant potential toward enabling novel therapeutic applications requiring precise modulation of gene expression. Our framework uses a cell type-specific diffusion model to generate synthetic 200 bp regulatory elements based on chromatin accessibility across different cell types. We evaluate the generated sequences based on key metrics to ensure they retain properties of endogenous sequences: transcription factor binding site composition, potential for cell type-specific chromatin accessibility, and capacity for sequences generated by DNA diffusion to activate gene expression in different cell contexts using state-of-the-art prediction models. Our results demonstrate the ability to robustly generate DNA sequences with cell type-specific regulatory potential. DNA-Diffusion paves the way for revolutionizing a regulatory modulation approach to mammalian synthetic biology and precision gene therapy.

synthetic biology↗

bioRxiv2020

One step forward, two steps back: transcriptional advancements and fermentation phenomena in Actinobacillus succinogenes 130Z

Within the field of bioproduction, non-model organisms offer promise as bio-platform candidates. Non-model organisms can possess natural abilities to consume complex feedstocks, produce industrially useful chemicals, and withstand extreme environments that can be ideal for product extraction. However, non-model organisms also come with unique challenges due to lack of characterization. As a consequence, developing synthetic biology tools, predicting growth behavior, and building computational models can be difficult. There have been many advancements that have improved work with non-model organisms to address broad limitations, however each organism can come with unique surprises. Here we share our work in the non-model bacterium Actinobacillus succinognes 130Z, which includes both advancements in synthetic biology toolkit development and pitfalls in unpredictable fermentation behaviors. To develop a synthetic biology "tool kit" for A. succinogenes, information gleaned from a growth study and antibiotic screening was used to characterize 22 promoters which demonstrated a 260-fold range of fluorescence protein expression. The strongest of the promoters was incorporated into an inducible system for tunable gene control in A. succinogenes using the promoter for the lac operon as a template. This system flaunted a 481-fold range of expression and no significant basal expression. These findings were accompanied by unexpected changes in fermentation products characterized by a loss of succinic acid and increase in lactic acid after approximately 10 months in the lab. Contamination and mutation were ruled out as causes and further testing is needed to elucidate the driving factors. The significance of this work is to share tools developed in A. succinogenes while simultaneously serving as a cautionary tale. In sharing our findings, we seek to provide necessary information for further development of A. succinogenes as a platform for bioproduction of succinic acid. Additionally, we hope to illustrate the importance of diligent and long-term observation when working with non-model bacteria.

bioengineering↗