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At least 19 recordsLinked to original sources

bioRxiv2025

Intracellularly Coupled Oscillators for Synthetic Biology

Synthetic biology aims to engineer or re-engineer living systems. To achieve increasingly complex functionalities, it is beneficial to use higher-level building blocks. In this study, we focus on oscillators as such building blocks, propose novel oscillator-based circuit designs and model the interactions of intracellularly coupled oscillators. We classify these oscillators on the basis of coupling strength: independent, weakly or strongly, and deeply coupled. We predict a range of fascinating dynamic behaviours to arise in these systems, such as the beat phenomenon, amplitude and frequency modulation, period doubling, higher-period oscillations, chaos, resonance, and synchronization, with the aim of guiding future experimental work in bacterial synthetic biology. Finally, we outline potential applications, including oscillator-based computing that integrates processing and memory functions, offering multistate and nonlinear processing capabilities.

synthetic biology↗

bioRxiv2017

PartsGenie: an integrated tool for optimising and sharing synthetic biology parts

Synthetic biology is typified by developing novel genetic constructs from the assembly of reusable synthetic DNA parts, which contain one or more features such as promoters, ribosome binding sites, coding sequences and terminators. While repositories of such parts exist to promote their reuse, there is still a need to design novel parts from scratch.\n\nPartsGenie, freely available at http://parts.synbiochem.co.uk, is introduced to facilitate the computational design of such synthetic biology parts. PartsGenie has been designed to bridge the gap between optimisation tools for the design of novel parts, the representation of such parts in community-developed data standards such as Synthetic Biology Open Language (SBOL), and their sharing in journal-recommended data repositories.\n\nConsisting of a drag-and-drop web interface, a number of DNA optimisation algorithms, and an interface to the well-used data repository JBEI ICE, PartsGenie facilitates the design, optimisation and dissemination of reusable synthetic biology parts through a single, integrated application. PartsGenie can therefore be used as a single, stand-alone tool, or integrated into larger synthetic biology pipelines that are being developed in the SYNBIOCHEM centre and elsewhere.

synthetic biology↗

bioRxiv2019

TXO: Transcription-Only genetic circuits as a novel cell-free approach for Synthetic Biology

While synthetic biology represents a promising approach to solve real-world problems, the use of genetically modified organisms is a cause of legal and environmental concerns. Cell-free systems have emerged as a possible solution but much work is needed to optimize their functionality and simplify their usage for Synthetic Biology. Here we present TXO, transcription-only genetic circuits, independent of translation or post-translation maturation. RNA aptamers are used as reaction output allowing the generation of fast, reliable and simple-to-design transcriptional units. TXO cell-free reactions and their possible applications are a promising new tool for fast and simple bench-to-market genetic circuit and biosensor applications.

synthetic biology↗

bioRxiv2019

Sequence-to-function deep learning frameworks for synthetic biology

While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of novel circuit components beyond nature-inspired templates can prove itself challenging without well-established design rules. Toehold switches -- programmable nucleic acid sensors -- face an analogous prediction and design bottleneck: our limited understanding of how sequence impacts functionality can require expensive, time-consuming screens for effective switches. Here, we introduce the Sequence-based Toehold Optimization and Redesign Model (STORM), a deep learning architecture that applies gradient ascent to re-engineer poorly-performing toeholds. Based on a dataset of 91,534 toehold switches, we examined convolutional filters and saliency maps of sequences to interpret our sequence-to-function model, identifying hot spots where mutations change toehold effectiveness and features unique to high-performing switches. Our modeling platform provides frameworks for future toehold selection, augmenting our ability to construct potent synthetic circuit components and precision diagnostics, and enabling straightforward translation of this in silico workflow to other circuitries.

synthetic biology↗

bioRxiv2023

libSBOLj3: A graph-based library for design and data exchange in synthetic biology

The Synthetic Biology Open Language version 3 data standard provides a graph-based approach to exchange information about biological designs. The new data model has major updates and offers several features for software tools. Here, we present libSBOLj3 to facilitate data exchange and provide interoperability between computer-aided design and automation tools using this standard. The library adopts a graph-based approach. Tool developers can extend these graphs with application-specific information and use detailed validation reports to identify errors and interoperability issues and apply best practice rules. The libSBOLj3 library is implemented in Java and can be downloaded or used as a Maven dependency. The open-source project, examples and documentation about accessing and using the library are available via GitHub at https://github.com/SynBioDex/libSBOLj3.

synthetic biology↗

bioRxiv2018

Capturing Multicellular System Designs Using the Synthetic Biology Open Language (SBOL)

Synthetic biology aims to improve the development of biological systems and in-crease their reproducibility through the use of engineering principles, such as stan-dardisation and modularisation. It is important that these systems can be represented and shared in a standard way to ensure they are easily understood, reproduced, and utilised by other researchers. The Synthetic Biology Open Language (SBOL) is a data standard for sharing biological designs and information about their implementation and characterisation. Thus far, this standard has been used to represent designs in homogeneous systems, where the same design is implemented in every cell. In recent years there has been increasing interest in multicellular systems, where biological designs are split across multiple cells to optimise the system behaviour and function. Here we show how the SBOL standard can be used to represent such multicellular systems, and hence how researchers can better share designs with the community.

synthetic biology↗

bioRxiv2025

Chemically Synthesized Ultra-long DNA as Building Blocks to Accelerate Complex Gene Construction in Synthetic Biology

Certain applications of synthetic biology rely on the construction of large and complex DNA sequences. Current DNA synthesis technologies are limited in their capacity to generate ultra-long oligonucleotide for complex gene construction with extensive repetitive motifs and uneven base distribution efficiently. Here, we report a novel platform named UCOS (short for Ultralong Complex Oligonucleotides Synthesis) that enables the efficient synthesis of long, complex, and challenging DNA fragments. This platform employs nonporous silica microspheres as the solid support instead of traditional CPG (Controlled Pore Glass) solid support, full-length enrichment based on 5 flank sequence hybridization and an error-removing enzyme for correct sequence selection, substantially enhancing the fidelity of intricate, ultralong oligonucleotides. Using this approach, we successfully synthesized challenging sequences up to 600nt in length, encompassing tandem repeats and uneven base distributions. Overall, this novel platform demonstrates exceptional efficiency and reliability in handling ultralong DNA fragments with highly repetitive and complex features. It provides a strong foundation for advancing synthetic biology and shows great potential as a powerful tool for constructing challenging genes and enabling the customized synthesis of functional genetic elements.

synthetic biology↗

bioRxiv2022

From Specification to Implementation: Assume-Guarantee Contracts for Synthetic Biology

We provide a new perspective on using formal methods to model specifications and synthesize implementations for the design of biological circuits. In synthetic biology, design objectives are rarely described formally. We present an assume-guarantee contract framework to describe biological circuit design objectives as formal specifications. In our approach, these formal specifications are implemented by circuits modeled by ordinary differential equations, yielding a design framework that can be used to design complex synthetic biological circuits at scale. We describe our approach using the design of a biological AND gate as a motivating, running example.

synthetic biology↗

bioRxiv2026

Bacteriophage P22 virus-like particles as nanoscale protein scaffolds for plant synthetic biology

Advancing the utility of plant synthetic biology requires the continued development of protein engineering tools. Self-assembling protein compartments, such as virus-like particles (VLPs), provide versatile scaffolds for synthetic biology. However, few plant-expressed VLPs have demonstrated broad amenability to protein engineering, restricting their applications to specific contexts. Here, the Salmonella typhimurium bacteriophage P22 VLP is explored as a novel protein scaffold for plant synthetic biology, demonstrating its application in a eukaryote for the first time. Through transient expression in the biofactory plant Nicotiana benthamiana, the capacity for P22 VLPs to correctly assemble and selectively encapsulate recombinant protein cargo is demonstrated. The durability of this protein scaffold is explored, through co-encapsulation of multiple cargo protein species and by encapsulation through direct fusion to the P22 coat protein. Finally, the ability to simultaneously program cargo encapsulation and external protein display on P22 VLPs in vivo is demonstrated through SpyTag/SpyCatcher-mediated protein conjugation. This work demonstrates the broad utility of P22 VLPs as nanoscale protein scaffolds for plant synthetic biology.

synthetic biology↗

bioRxiv2018

Synthetic Biology Research and Innovation Profile 2018: Publications and Patents

A profile of synthetic biology research and innovation is presented using data on publications and patents worldwide and for the UK and selected benchmark countries. The search approach used to identify synthetic biology publications identifies a core set of synthetic biology papers, extracts and refines keywords from these core records, searches for additional papers using those keywords, and supplements with articles published in dedicated synthetic biology journals and curated synthetic biology special collections. For the period from 2000 through to mid-July 2018, 11,369 synthetic biology publication records are identified worldwide. For patents, the search approach uses the same keywords as for publications then identifies further patents using a citation-tree search algorithm. The search covered patents by priority year from 2003 to early August 2018. Following geographical matching, 8,460 synthetic biology basic patent records were identified worldwide. Using this data, analyses of publications are presented which look at the growth of synthetic biology outputs, top countries and leading organizations, international co-authoring, leading subject categories, citations, synthetic biology on the map of science, and funding sponsorship. For patents, the analysis examines growth in patenting, national variations in publications compared with patenting, leading patent assignees, and the positioning of synthetic biology on a visualized map of patents.

scientific communication and education↗

bioRxiv2018

Synthetic Biology: Mapping the Patent Landscape

This article presents the global patent landscape for synthetic biology as a new and emerging area of science and technology. The aim of the article is to provide an overview of the emergence of synthetic biology in the patent system and to contribute to future research by providing a high quality tagged core dataset with 7,424 first filings and 71,887 family members. This dataset is intended to assist with evidence based exploration of synthetic biology in the patent system and with advancing methods for the analysis of new and emerging areas of science and technology.\n\nThe starting point for the research is recognition that traditional methods of patent landscape analysis based on key word searches face limitations when addressing new and emerging areas of science and technology. Synthetic biology can be broadly described as involving the design, synthesis and assembly of biological parts, circuits, pathways, cells and genomes. As such synthetic biology can be understood as emerging from a combination of overlaps and convergences between existing fields and disciplines, such as biotechnology, genetic engineering, protein engineering and systems biology. More precisely, synthetic biology can be understood as the synthetic phase of molecular biology and genetic engineering. This presents the problem that key word strategies may radically overestimate activity because they involve terms that are widely used in underlying fields that are contributing to synthetic biology.\n\nIn response to this problem we combined anthropology, scientometrics and data science to map authors from scientific publications on synthetic biology into the international patent system. We mapped 10,816 authors into the international patent system and identified 2,450 authors of articles on synthetic biology who are also inventors in the period to December 2017. By combining this data with citation information and a baseline keyword strategy we are able to describe the global patent landscape for synthetic biology and the wider patent universe in which synthetic biology is situated.\n\nThis article describes the main features of the global landscape and provides the tagged dataset as a contribution to evidence based debate on intellectual property and synthetic biology and methodological development. We anticipate that the data will prove useful in informing international policy debates on synthetic biology under the United Nations Convention on Biological Diversity.

synthetic biology↗

bioRxiv2024

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

BackgroundThe Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. ResultsBiSDLs effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. ConclusionsBiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

synthetic biology↗

bioRxiv2025

BOTany Methods: Accessible Automation for Plant Synthetic Biology

Most members of the synthetic biology community, particularly plant scientists, lack access to liquid handling robots to scale up experiments, enhance reproducibility, and accelerate the Design, Build, Test, Learn cycle. Biofoundries enable high throughput data acquisition to train AI models and to develop new bioproducts, but they are capital-intensive to set up and not widely distributed. Entry-level, 3D-printed robots offer more affordable alternatives, but suffer from a shortage of validated protocols that can be modified without prior coding experience. To enhance access to biological automation, we developed a collection of modular BOTany Methods using Opentrons OT-2 robots to streamline the most common methods for molecular biology research and education. Our comprehensive workflow offers automation for a variety of procedures, ranging from simple but repetitive tasks (such as primer dilution and PCR setup) to more complex operations, including Plant Modular Cloning (MoClo), bacterial transformation, and plasmid extraction. Our BOTany Methods enable undergraduate students and other early career researchers to run designer experiments using table-based inputs, without editing the custom Python scripts. This pipeline enables end-to-end molecular cloning with minimal user intervention, enhancing throughput and traceability for synthetic biology applications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/671538v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1b98282org.highwire.dtl.DTLVardef@2727bdorg.highwire.dtl.DTLVardef@a2157borg.highwire.dtl.DTLVardef@1305107_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2020

Challenges in Undergraduate Synthetic Biology Training: Insights from a Canadian iGEM Student Perspective

The last two decades have seen vigorous activity in synthetic biology research and ever-increasing applications of synthetic biology technologies. However, pedagogical research on synthetic biology is scarce, especially when compared to some scientific and engineering disciplines. Within Canada, there are only three universities that formally teach synthetic biology programs; two of which are at the undergraduate level. Many Canadian undergraduate students are instead introduced to synthetic biology through participation in the annual International Genetically Engineered Machine (iGEM) competition where they work in design teams to conceive of and execute a synthetic biology project that they present at an international jamboree. We surveyed the Canadian landscape of synthetic biology education through the experience of students from the Canadian iGEM teams of 2019. Using a thematic codebook analysis, we gathered insights to generate recommendations that could empower future iGEM team operations and inform educators about best practices in teaching undergraduate synthetic biology.

scientific communication and education↗

bioRxiv2017

Quantitative Modeling of Integrase Dynamics Using a Novel Python Toolbox for Parameter Inference in Synthetic Biology

In systems and synthetic biology, it is common to build chemical reaction network (CRN) models of biochemical circuits and networks. Although automation and other high-throughput techniques have led to an abundance of data enabling data-driven quantitative modeling and parameter estimation, the intense amount of simulation needed for these methods still frequently results in a computational bottleneck. Here we present bioscrape (Bio-circuit Stochastic Single-cell Reaction Analysis and Parameter Estimation) - a Python package for fast and flexible modeling and simulation of highly customizable chemical reaction networks. Specifically, bioscrape supports deterministic and stochastic simulations, which can incorporate delay, cell growth, and cell division. All functionalities - reaction models, simulation algorithms, cell growth models, partioning models, and Bayesian inference - are implemented as interfaces in an easily extensible and modular object-oriented framework. Models can be constructed via Systems Biology Markup Language (SBML) or specified programmatically via a Python API. Simulation run times obtained with the package are comparable to those obtained using C code - this is particularly advantageous for computationally expensive applications such as Bayesian inference or simulation of cell lineages. We first show the packages simulation capabilities on a variety of example simulations of stochastic gene expression. We then further demonstrate the package by using it to do parameter inference on a model of integrase enzyme-mediated DNA recombination dynamics with experimental data. The bioscrape package is publicly available online (https://github.com/biocircuits/bioscrape) along with more detailed documentation and examples.

synthetic biology↗

bioRxiv2024

Empowering Student Authorship in Synthetic Biology

Women and racial minorities are underrepresented in the synthetic biology community. Developing a scholarly identity by engaging in a scientific community through writing and communication is an important component for STEM retention, particularly for underrepresented individuals. Several excellent pedagogical tools have been developed to teach scientific literacy and to measure competency in reading and interpreting scientific literature. However, fewer tools exist to measure learning gains with respect to writing, or that teach the more abstract processes of peer review and scientific publishing, which are essential for developing scholarly identity and publication currency. Here we describe our approach to teaching scientific writing and publishing to undergraduate students within a synthetic biology course. Using gold standard practices in project-based learning, we created a writing project in which students became experts in a specific application area of synthetic biology with relevance to an important global problem or challenge. To measure learning gains associated with our learning outcomes, we adapted and expanded the Student Attitudes, Abilities, and Beliefs (SAAB) concept inventory to include additional questions about the process of scientific writing, authorship, and peer review. Our results suggest the project-based approach was effective in achieving the learning objectives with respect to writing and peer reviewed publication, and resulted in high student satisfaction and student self-reported learning gains. We propose that these educational practices will contribute directly to the development of scientific identity of undergraduate students as synthetic biologists, and will be useful in creating a more diverse synthetic biology research enterprise.

scientific communication and education↗

bioRxiv2015

Landauer in the age of synthetic biology: energy consumption and information processing in biochemical networks

A central goal of synthetic biology is to design sophisticated synthetic cellular circuits that can perform complex computations and information processing tasks in response to specific inputs. The tremendous advances in our ability to understand and manipulate cellular information processing networks raises several fundamental physics questions: How do the molecular components of cellular circuits exploit energy consumption to improve information processing? Can one utilize ideas from thermodynamics to improve the design of synthetic cellular circuits and modules? Here, we summarize recent theoretical work addressing these questions. Energy consumption in cellular circuits serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory. We demonstrate these ideas using several simple examples and discuss the implications of these theoretical ideas for the emerging field of synthetic biology. We conclude by discussing how it may be possible to overcome these limitations using \"post-translational\" synthetic biology that exploits reversible protein modification.

Synthetic Biology↗

bioRxiv2016

The Software Crisis of Synthetic Biology

In fifteen years, Synthetic Biology (SB) has moved from proof-of-concept designs to several flagship achievements. Standardisation efforts are still under way, basic engineering concepts such as modularity and orthogonality are still controversial in biology, and making predictions from computer models is still unreliable. A deep characterization in the pattern of re-use of biological blocks in SB has not been attempted to date. We have compared the topological organisation of two different technological networks, one associated to a standard, large-scale software repository and the second provided by the Registry of Standard Biological Parts (RSBP). Our results strongly suggest that software engineering, and not industrial engineering, is the closest complex system to SB. In both cases, combining standard or quasi-standard components assembly with tinkering may not be at odds with success.

Systems Biology↗