DNA-BOT: a low-cost, automated DNA assembly platform for synthetic biology

Pangenomic landscapes shape performances of a synthetic genetic circuit across Stutzerimonas species

Engineering identical genetic circuits into different species typically results in large differences in performance due to the unique cellular environmental context of each host, a phenomenon known as the "chassis-effect" or "context-dependency". A better understanding of how genomic and physiological contexts underpin the chassis-effect will improve biodesign strategies across diverse microorganisms. Here, we combined a pangenomic-based gene expression analysis with quantitative measurements of performance from an engineered genetic inverter device to uncover how genome structure and function relate to the observed chassis-effect across six closely related Stutzerimonas hosts. Our results reveal that genome architecture underpins divergent responses between our chosen non-model bacterial hosts to the engineered device. Specifically, differential expression of the core genome, gene clusters shared between all hosts, was found to be the main source of significant concordance to the observed differential genetic device performance, whereas specialty genes from respective accessory genomes were not significant. A data-driven investigation revealed that genes involved in denitrification and components of trans-membrane transporter proteins were among the most differentially expressed gene clusters between hosts in response to the genetic device. Our results show that the chassis-effect can be traced along differences among the most conserved genome-encoded functions and that these differences create a unique biodesign space among closely related species.IMPORTANCEContemporary synthetic biology endeavors often default to a handful of model organisms to host their engineered systems. Model organisms such as Escherichia coli serve as attractive hosts due to their tractability but do not necessarily provide the ideal environment to optimize performance. As more novel microbes are domesticated for use as biotechnology platforms, synthetic biologists are urged to explore the chassis-design space to optimize their systems and deliver on the promises of synthetic biology. The consequences of the chassis-effect will therefore only become more relevant as the field of biodesign grows. In our work, we demonstrate that the performance of a genetic device is highly dependent on the host environment it operates within, promoting the notion that the chassis can be considered a design variable to tune circuit function. Importantly, our results unveil that the chassis-effect can be traced along similarities in genome architecture, specifically the shared core genome. Our study advocates for the exploration of the chassis-design space and is a step forward to empowering synthetic biologists with knowledge for more efficient exploration of the chassis-design space to enable the next generation of broad-host-range synthetic biology.

Deep integration of low-cost liquid handling robots in an industrial pharmaceutical development environment

The pharmaceutical industry is increasingly embracing laboratory automation to enhance experimental efficiency and operational resilience, particularly through the integration of automated liquid handlers (ALHs). This paper explores the integration of the low-cost Opentrons OT-2 liquid handling robot with F. Hoffmann-La Roche AG's in-house workflow orchestration software, AutoLab, to overcome barriers to lab automation. By leveraging the OT-2's development-oriented interfaces and AutoLab's modular architecture, we achieved a user-friendly, cost-efficient, and flexible automation solution that aligns with FAIR (findable, accessible, interoperable, reusable) data principles. We demonstrate an advanced workflow development methodology, utilizing the software architecture, that facilitates the creation of two flexible pipetting protocols and medium complexity assays. This deep integration approach diminishes the learning curve for novice users while simultaneously enhancing the overall efficiency and reliability of the experimental workflow. Our findings suggest that such integrations can significantly mitigate the challenges associated with lab automation, including cost, complexity, and adaptability, paving the way for more accessible and robust automated systems in pharmaceutical research.

BioCloneBot: A versatile, low-cost, and open-source automated liquid handler

Liquid handler systems can provide significant benefits to researchers by automating laboratory work, however, their unaffordable price provides a steep barrier to entry. Therefore, we provide the BioCloneBot, a versatile, low-cost, and open-source automated liquid handler. This system can be easily built with 3D-printed parts and readily available commercial components. The BioCloneBot is highly adaptive to user needs and facilitates various liquid handling tasks in research and diagnostics. Its user-friendly interface and programmable nature make it suitable for a wide range of applications, from small-scale experiments to larger laboratory setups. By utilizing BioCloneBot, researchers and scientists can streamline their liquid handling processes without the financial constraints posed by traditional systems. In this paper, we detail the design, construction, and validation of BioCloneBot, showcasing its precise control, accuracy, and repeatability in various liquid handling tasks. The open-source nature of the system encourages collaboration and customization, enabling researchers to contribute and adapt the technology to specific experimental requirements.

Fully in vitro iterative construction of a 24 kb-long artificial DNA sequence to store digital information

In the absence of a DNA template, the ab initio production of long double-stranded DNA molecules of predefined sequences is particularly challenging. The DNA synthesis step remains a bottleneck for many applications such as functional assessment of ancestral genes, analysis of alternative splicing or DNA-based data storage. In this report we propose a fully in vitro protocol to generate very long double-stranded DNA molecules starting from commercially available short DNA blocks in less than 3 days using Golden Gate assembly. This innovative application allowed us to streamline the process to produce a 24 kb-long DNA molecule storing part of the Declaration of the Rights of Man and of the Citizen of 1789 . The DNA molecule produced can be readily cloned into a suitable host/vector system for amplification and selection.

Automated high-throughput DNA synthesis and assembly

DNA synthesis and assembly primarily revolve around the innovation and refinement of tools that facilitate the creation of specific genes and the manipulation of entire genomes. This multifaceted process encompasses two fundamental steps: the synthesis of lengthy oligonucleotides and the seamless assembly of numerous DNA fragments. With the advent of automated pipetting workstations and integrated experimental equipment, a substantial portion of repetitive tasks in the field of synthetic biology can now be efficiently accomplished through integrated liquid handling workstations. This not only reduces the need for manual labor but also enhances overall efficiency. This review explores the ongoing advancements in the oligonucleotide synthesis platform, automated DNA assembly techniques, and biofoundries. The development of accurate and high-throughput DNA synthesis and assembly technologies presents both challenges and opportunities.

Physical Laboratory Automation in Synthetic Biology

Synthetic Biology has overcome many of the early challenges facing the field and is entering a systems era characterized by adoption of Design-Build-Test-Learn (DBTL) approaches. The need for automation and standardization to enable reproducible, scalable, and translatable research has become increasingly accepted in recent years, and many of the hardware and software tools needed to address these challenges are now in place or under development. However, the lack of connectivity between DBTL modules and barriers to access and adoption remain significant challenges to realizing the full potential of lab automation. In this review, we characterize and classify the state of automation in synthetic biology with a focus on the physical automation of experimental workflows. Though fully autonomous scientific discovery is likely a long way off, impressive progress has been made toward automating critical elements of experimentation by combining intelligent hardware and software tools. It is worth questioning whether total automation that removes humans entirely from the loop should be the ultimate goal, and considerations for appropriate automation versus total automation are discussed in this light while emphasizing areas where further development is needed in both contexts.

Technical upgrade of an open-source liquid handler to support bacterial colony screening

The optimization of genetically engineered biological constructs is a key step to deliver high-impact biotechnological applications. The use of high-throughput DNA assembly methods allows the construction of enough genotypic variants to successfully cover the target design space. This, however, entails extra workload for researchers during the screening stage of candidate variants. Despite the existence of commercial colony pickers, their high price excludes small research laboratories and budget-adjusted institutions from accessing such extensive screening capability. In this work we present COPICK, a technical solution to automatize colony picking in an open-source liquid handler Opentrons OT-2. COPICK relies on a mounted camera to capture images of regular Petri dishes and detect microbial colonies for automated screening. COPICK's software can then automatically select the best colonies according to different criteria (size, color and fluorescence) and execute a protocol to pick them for further analysis. Benchmark tests performed for E. coli and P. putida colonies delivers a raw picking performance over pickable colonies of 82% with an accuracy of 73.4% at an estimated rate of 240 colonies/h. These results validate the utility of COPICK, and highlight the importance of ongoing technical improvements in open-source laboratory equipment to support smaller research teams.

Technical assessment of different extraction methods and transcriptome profiling of RNA isolated from small volumes of blood

Transcriptome profiling of human whole blood is used to discover biomarkers of diseases and to assess phenotypic traits. Recently, finger-stick blood collection systems have allowed a less invasive and quicker collection of peripheral blood. Such non-invasive sampling of small volumes of blood offers practical advantages. The quality of gene expression data is strictly dependent on the steps used for the sample collection, extraction, preparation and sequencing. Here we have: (i) compared the manual and automated RNA extraction of small volumes of blood using the Tempus Spin RNA isolation kit and the MagMAX for Stabilized Blood RNA Isolation kit , respectively; and (ii) assessed the effect of TURBO DNA Free treatment on the transcriptomic data of RNA isolated from small volumes of blood. We have used the QuantSeq 3' FWD mRNA-Seq Library Prep kit to prepare RNA-seq libraries, which were sequenced on the Illumina NextSeq 500 system. The samples isolated manually displayed a higher variability in the transcriptomic data as compared to the other samples. The TURBO DNA Free treatment affected the RNA samples negatively, decreasing the RNA yield and reducing the quality and reproducibility of the transcriptomic data. We conclude that automated extraction systems should be preferred over manual extraction systems for data consistency, and that the TURBO DNA Free treatment should be avoided when working on RNA samples isolated manually from small volumes of blood.

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 (DBTL) approach to simultaneously learn how a 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 DBTL cycle. However, there are many fewer options for automating the build step. Build typically involves deoxyribonucleic acid (DNA) assembly, which remains manual, low throughput and unreliable in most cases and limits 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 polymerase chain reactions 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 (IVAs) 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 IVA 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.

Biofoundry Palette: Planning-Assistant Software for Liquid Handler-Based Experimentation and Operation in the Biofoundry Workflow

Lab automation has facilitated synthetic biology applications in an automated workflow, and biofoundry facilities have enabled automated high-throughput experiments of gene cloning and genome engineering to be conducted following a precise experimental design and protocol. However, before-experiment procedures in biofoundry applications have been underdetermined. We aimed to develop a Python-based planning-assistant software, namely Biofoundry Palette, for liquid handler-based experimentation and operation in the biofoundry workflow. Depending on the synthetic biology project, variable information and content information may vary; the Biofoundry Palette provides precise information for the before-experiment units for each process module in the biofoundry workflow. As a demonstration, more than 200 unique information sets, generated by Biofoundry Palette, were used in automated gene cloning or pathway construction. The information on planning and management can potentially help the operator faithfully execute the biofoundry workflow after securing the before-experiment unit, thereby lowering the risk of human errors and performing successful biofoundry operations for synthetic biology applications.

basicsynbio and the BASIC SEVA collection: software and vectors for an established DNA assembly method

Standardized deoxyribonucleic acid (DNA) assembly methods utilizing modular components provide a powerful framework to explore designs and iterate through Design-Build-Test-Learn cycles. Biopart Assembly Standard for Idempotent Cloning (BASIC) DNA assembly uses modular parts and linkers, is highly accurate, easy to automate, free for academic and commercial use and enables hierarchical assemblies through an idempotent format. These features enable applications including pathway engineering, ribosome binding site (RBS) tuning, fusion protein engineering and multiplexed guide ribonucleic acid (RNA) expression. In this work, we present basicsynbio, open-source software encompassing a Web App (https://basicsynbio.web.app/) and Python Package (https://github.com/LondonBiofoundry/basicsynbio), enabling BASIC construct design via simple drag-and-drop operations or programmatically. With basicsynbio, users can access commonly used BASIC parts and linkers while designing new parts and assemblies with exception handling for common errors. Users can export sequence data and create instructions for manual or acoustic liquid-handling platforms. Instruction generation relies on the BasicBuild Open Standard, which is parsed for bespoke workflows and is serializable in JavaScript Object Notation for transfer and storage. We demonstrate basicsynbio, assembling 30 vectors using sequences including modules from the Standard European Vector Architecture (SEVA). The BASIC SEVA vector collection is compatible with BASIC and Golden Gate using BsaI. Vectors contain one of six antibiotic resistance markers and five origins of replication from different compatibility groups. The collection is available via Addgene under an OpenMTA agreement. Furthermore, vector sequences are available from within the basicsynbio application programming interface with other collections of parts and linkers, providing a powerful environment for designing assemblies for bioengineering applications. Graphical Abstract.

The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering

Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows.

Standardization of Synthetic Biology Tools and Assembly Methods for Saccharomyces cerevisiae and Emerging Yeast Species

As redesigning organisms using engineering principles is one of the purposes of synthetic biology (SynBio), the standardization of experimental methods and DNA parts is becoming increasingly a necessity. The synthetic biology community focusing on the engineering of Saccharomyces cerevisiae has been in the foreground in this area, conceiving several well-characterized SynBio toolkits widely adopted by the community. In this review, the molecular methods and toolkits developed for S. cerevisiae are discussed in terms of their contributions to the required standardization efforts. In addition, the toolkits designed for emerging nonconventional yeast species including Yarrowia lipolytica, Komagataella phaffii, and Kluyveromyces marxianus are also reviewed. Without a doubt, the characterized DNA parts combined with the standardized assembly strategies highlighted in these toolkits have greatly contributed to the rapid development of many metabolic engineering and diagnostics applications among others. Despite the growing capacity in deploying synthetic biology for common yeast genome engineering works, the yeast community has a long journey to go to exploit it in more sophisticated and delicate applications like bioautomation.

LOICA: Integrating Models with Data for Genetic Network Design Automation

Genetic design automation tools are necessary to expand the scale and complexity of possible synthetic genetic networks. These tools are enabled by abstraction of a hierarchy of standardized components and devices. Abstracted elements must be parametrized from data derived from relevant experiments, and these experiments must be related to the part composition of the abstract components. Here we present Logical Operators for Integrated Cell Algorithms (LOICA), a Python package for designing, modeling, and characterizing genetic networks based on a simple object-oriented design abstraction. LOICA uses classes to represent different biological and experimental components, which generate models through their interactions. These models can be parametrized by direct connection to data contained in Flapjack so that abstracted components of designs can characterize themselves. Models can be simulated using continuous or stochastic methods and the data published and managed using Flapjack. LOICA also outputs SBOL3 descriptions and generates graph representations of genetic network designs.

PlasmidMaker is a versatile, automated, and high throughput end-to-end platform for plasmid construction

Plasmids are used extensively in basic and applied biology. However, design and construction of plasmids, specifically the ones carrying complex genetic information, remains one of the most time-consuming, labor-intensive, and rate-limiting steps in performing sophisticated biological experiments. Here, we report the development of a versatile, robust, automated end-to-end platform named PlasmidMaker that allows error-free construction of plasmids with virtually any sequences in a high throughput manner. This platform consists of a most versatile DNA assembly method using Pyrococcus furiosus Argonaute (PfAgo)-based artificial restriction enzymes, a user-friendly frontend for plasmid design, and a backend that streamlines the workflow and integration with a robotic system. As a proof of concept, we used this platform to generate 101 plasmids from six different species ranging from 5 to 18 kb in size from up to 11 DNA fragments. PlasmidMaker should greatly expand the potential of synthetic biology.

Despite their broad utility, design and construction of plasmids remains laborious and time-consuming. Here the authors report a robust, versatile, and automated end-to-end platform that enables scarless construction of virtually any plasmid.

Synthetic biology and bioelectrochemical tools for electrogenetic system engineering

Synthetic biology research and its industrial applications rely on deterministic spatiotemporal control of gene expression. Recently, electrochemical control of gene expression has been demonstrated in electrogenetic systems (redox-responsive promoters used alongside redox inducers and electrodes), allowing for the direct integration of electronics with biological processes. However, the use of electrogenetic systems is limited by poor activity, tunability, and standardization. In this work, we developed a strong, unidirectional, redox-responsive promoter before deriving a mutant promoter library with a spectrum of strengths. We constructed genetic circuits with these parts and demonstrated their activation by multiple classes of redox molecules. Last, we demonstrated electrochemical activation of gene expression under aerobic conditions using a novel, modular bioelectrochemical device. These genetic and electrochemical tools facilitate the design and improve the performance of electrogenetic systems. Furthermore, the genetic design strategies used can be applied to other redox-responsive promoters to further expand the available tools for electrogenetics.

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

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

This paper describes a new software package BioCRNpyler (pronounced “Biocompiler”) designed to support rapid development and exploration of mathematical models of biochemical networks and circuits by computational biologists, systems biologists, and synthetic biologists. BioCRNpyler allows its users to generate large complex models using very few lines of code in a way that is modular. To do this, BioCRNpyler uses a powerful new representation of biochemical circuits which defines their parts, underlying biochemical mechanisms, and chemical context independently. BioCRNpyler was developed as a Python scripting language designed to be accessible to beginning users as well as easily extendable and customizable for advanced users. Ultimately, we see Biocrnpyler being used to accelerate computer automated design of biochemical circuits and model driven hypothesis generation in biology.

A Linear Programming-Based Strategy to Save Pipette Tips in Automated DNA Assembly

Laboratory automation and mathematical optimization are key to improving the efficiency of synthetic biology research. While there are algorithms optimizing the construct designs and synthesis strategies for DNA assembly, the optimization of how DNA assembly reaction mixes are prepared remains largely unexplored. Here, we focus on reducing the pipette tip consumption of a liquid-handling robot as it delivers DNA parts across a multi-well plate where several constructs are being assembled in parallel. We propose a linear programming formulation of this problem based on the capacitated vehicle routing problem, as well as an algorithm which applies a linear programming solver to our formulation, hence providing a strategy to prepare a given set of DNA assembly mixes using fewer pipette tips. The algorithm performed well in randomly generated and real-life scenarios concerning several modular DNA assembly standards, proving to be capable of reducing the pipette tip consumption by up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$59\%$\end{document} in large-scale cases. Combining automatic process optimization and robotic liquid handling, our strategy promises to greatly improve the efficiency of DNA assembly, either used alone or combined with other algorithmic DNA assembly optimization methods.

Graphical Abstract

EMMA-CAD: Design Automation for Synthetic Mammalian Constructs

Computational design tools are the cornerstone of synthetic biology and have underpinned its rapid development over the past two decades. As the field has matured, the scale of biological investigation has expanded dramatically, and researchers often must rely on computational tools to operate in the high-throughput investigational space. This is especially apparent in the modular design of DNA expression circuits, where complexity is accumulated rapidly. Alongside our automated pipeline for the high-throughput construction of Extensible Modular Mammalian Assembly (EMMA) expression vectors, we recognized the need for an integrated software solution for EMMA vector design. Here we present EMMA-CAD (https://emma.cailab.org), a powerful web-based computer-aided design tool for the rapid design of bespoke mammalian expression vectors. EMMA-CAD features a variety of functionalities, including a user-friendly design interface, automated connector selection underpinned by rigorous computer optimization algorithms, customization of part libraries, and personalized design spaces. Capable of translating vector assembly designs into human- and machine-readable protocols for vector construction, EMMA-CAD integrates seamlessly into our automated EMMA pipeline, hence completing an end-to-end design to production workflow.

Automation and Expansion of EMMA Assembly for Fast-Tracking Mammalian System Engineering

With applications from functional genomics to the production of therapeutic biologics, libraries of mammalian expression vectors have become a cornerstone of modern biological investigation and engineering. Multiple modular vector platforms facilitate the rapid design and assembly of vectors. However, such systems approach a technical bottleneck when a library of bespoke vectors is required. Utilizing the flexibility and robustness of the Extensible Mammalian Modular Assembly (EMMA) toolkit, we present an automated workflow for the library-scale design, assembly, and verification of mammalian expression vectors. Vector design is simplified using our EMMA computer-aided design tool (EMMA-CAD), while the precision and speed of acoustic droplet ejection technology are applied in vector assembly. Our pipeline facilitates significant reductions in both reagent usage and researcher hands-on time compared with manual assembly, as shown by system Q-metrics. To demonstrate automated EMMA performance, we compiled a library of 48 distinct plasmid vectors encoding either CRISPR interference or activation modalities. Characterization of the workflow parameters shows that high assembly efficiency is maintained across vectors of various sizes and design complexities. Our system also performs strongly compared with manual assembly efficiency benchmarks. Alongside our automated pipeline, we present a straightforward strategy for integrating gRNA and Cas modules into the EMMA platform, enabling the design and manufacture of valuable genome editing resources.

A modular RNA interference system for multiplexed gene regulation

The rational design and realisation of simple-to-use genetic control elements that are modular, orthogonal and robust is essential to the construction of predictable and reliable biological systems of increasing complexity. To this effect, we introduce modular Artificial RNA interference (mARi), a rational, modular and extensible design framework that enables robust, portable and multiplexed post-transcriptional regulation of gene expression in Escherichia coli. The regulatory function of mARi was characterised in a range of relevant genetic contexts, demonstrating its independence from other genetic control elements and the gene of interest, and providing new insight into the design rules of RNA based regulation in E. coli, while a range of cellular contexts also demonstrated it to be independent of growth-phase and strain type. Importantly, the extensibility and orthogonality of mARi enables the simultaneous post-transcriptional regulation of multi-gene systems as both single-gene cassettes and poly-cistronic operons. To facilitate adoption, mARi was designed to be directly integrated into the modular BASIC DNA assembly framework. We anticipate that mARi-based genetic control within an extensible DNA assembly framework will facilitate metabolic engineering, layered genetic control, and advanced genetic circuit applications.

Fast biofoundries: coping with the challenges of biomanufacturing

Biofoundries are highly automated facilities that enable the rapid and efficient design, build, test, and learn cycle of biomanufacturing and engineering biology, which is applicable to both research and industrial production. However, developing a biofoundry platform can be expensive and time consuming. A biofoundry should grow organically, starting from a basic platform but with a vision for automation, equipment interoperability, and efficiency. By thinking about strategies early in the process through process planning, simulation, and optimization, bottlenecks can be identified and resolved. Here, we provide a survey of technological solutions in biofoundries and their advantages and limitations. We explore possible pathways towards the creation of a functional, early-phase biofoundry, and strategies towards long-term sustainability.

Automated Rational Strain Construction Based on High-Throughput Conjugation

Molecular cloning is the core of synthetic biology, as it comprises the assembly of DNA and its expression in target hosts. At present, however, cloning is most often a manual, time-consuming, and repetitive process that highly benefits from automation. The automation of a complete rational cloning procedure, i.e., from DNA creation to expression in the target host, involves the integration of different operations and machines. Examples of such workflows are sparse, especially when the design is rational (i.e., the DNA sequence design is fixed and not based on randomized libraries) and the target host is less genetically tractable (e.g., not sensitive to heat-shock transformation). In this study, an automated workflow for the rational construction of plasmids and their subsequent conjugative transfer into the biotechnological platform organism Corynebacterium glutamicum is presented. The whole workflow is accompanied by a custom-made software tool. As an application example, a rationally designed library of transcription factor-biosensors based on the regulator Lrp was constructed and characterized. A sensor with an improved dynamic range was obtained, and insights from the screening provided evidence for a dual regulator function of C. glutamicum Lrp.

Accelerating strain engineering in biofuel research via build and test automation of synthetic biology

Biofuels are a type of sustainable and renewable energy. However, for the economical production of bulk-volume biofuels, biosystems design is particularly challenging to achieve sufficient yield, titer, and productivity. Because of the lack of predictive modeling, high-throughput screening remains essential. Recently established biofoundries provide an emerging infrastructure to accelerate biological design-build-test-learn (DBTL) cycles through the integration of robotics, synthetic biology, and informatics. In this review, we first introduce the technical advances of build and test automation in synthetic biology, focusing on the use of industry-standard microplates for DNA assembly, chassis engineering, and enzyme and strain screening. Proof-of-concept studies on prototypes of automated foundries are then discussed, for improving biomass deconstruction, metabolic conversion, and host robustness. We conclude with future challenges and opportunities in creating a flexible, versatile, and data-driven framework to support biofuel research and development in biofoundries.

Flapjack: Data Management and Analysis for Genetic Circuit Characterization

Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different laboratories and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parametrize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting, and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated with circuit part composition via SBOL (Synthetic Biology Open Language). We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.

Automating Cloning by Natural Transformation

Affordable and automated cloning platforms are essential to many synthetic biology studies. However, the traditional E. coli-based cloning is a major bottleneck as it requires heat shock or electroporation implemented in the robotic workflows. To overcome this problem, we explored bacterial natural transformation for automatic DNA cloning and engineering. Recombinant plasmids are efficiently generated from Gibson or overlap extension PCR (OE-PCR) products by simply adding the DNA into Acinetobacter baylyi ADP1 cultures. No DNA purification, competence induction, or special equipment is required. Up to 10,000 colonies were obtained per microgram of DNA, while the number of false positive colonies was low. We cloned and engineered 21 biosynthetic gene clusters (BGCs) of various types, with length from 1.5 to 19 kb and GC content from 35% to 72%. One of them, a nucleoside BGC, showed antibacterial activity. Furthermore, the method was easily transferred to a low-cost benchtop robot with consistent cloning efficiency. Thus, this automatic natural transformation (ANT) cloning provides an easy, robust, and affordable platform for high throughput DNA engineering.

Building a biofoundry

A biofoundry provides automation and analytics infrastructure to support the engineering of biological systems. It allows scientists to perform synthetic biology and aligned experimentation on a high-throughput scale, massively increasing the solution space that can be examined for any given problem or question. However, establishing a biofoundry is a challenging undertaking, with numerous technical and operational considerations that must be addressed. Using collated learnings, here we outline several considerations that should be addressed prior to and during establishment. These include drivers for establishment, institutional models, funding and revenue models, personnel, hardware and software, data management, interoperability, client engagement and biosecurity issues. The high cost of establishment and operation means that developing a long-term business model for biofoundry sustainability in the context of funding frameworks, actual and potential client base, and costing structure is critical. Moreover, since biofoundries are leading a conceptual shift in experimental design for bioengineering, sustained outreach and engagement with the research community are needed to grow the client base. Recognition of the significant, long-term financial investment required and an understanding of the complexities of operationalization is critical for a sustainable biofoundry venture. To ensure state-of-the-art technology is integrated into planning, extensive engagement with existing facilities and community groups, such as the Global Biofoundries Alliance, is recommended.

Combinatorial metabolic pathway assembly approaches and toolkits for modular assembly

Synthetic Biology is a rapidly growing interdisciplinary field that is primarily built upon foundational advances in molecular biology combined with engineering design principles such as modularity and interoperability. The field considers living systems as programmable at the genetic level and has been defined by the development of new platform technologies and methodological advances. A key concept driving the field is the Design-Build-Test-Learn cycle which provides a systematic framework for building new biological systems. One major application area for synthetic biology is biosynthetic pathway engineering that requires the modular assembly of different genetic regulatory elements and biosynthetic enzymes. In this review we provide an overview of modular DNA assembly and describe and compare the plethora of in vitro and in vivo assembly methods for combinatorial pathway engineering. Considerations for part design and methods for enzyme balancing are also presented, and we briefly discuss alternatives to intracellular pathway assembly including microbial consortia and cell-free systems for biosynthesis. Finally, we describe computational tools and automation for pathway design and assembly and argue that a deeper understanding of the many different variables of genetic design, pathway regulation and cellular metabolism will allow more predictive pathway design and engineering.

Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

Unlocking the efficiency of genomics laboratories with robotic liquid-handling

In research and clinical genomics laboratories today, sample preparation is the bottleneck of experiments, particularly when it comes to high-throughput next generation sequencing (NGS). More genomics laboratories are now considering liquid-handling automation to make the sequencing workflow more efficient and cost effective. The question remains as to its suitability and return on investment. A number of points need to be carefully considered before introducing robots into biological laboratories. Here, we describe the state-of-the-art technology of both sophisticated and do-it-yourself (DIY) robotic liquid-handlers and provide a practical review of the motivation, implications and requirements of laboratory automation for genome sequencing experiments.