Engineering biological systems using automated biofoundries

Engineering of the Lrp/AsnC-type transcriptional regulator DecR as a genetically encoded biosensor for multilevel optimization of L-cysteine biosynthesis pathway in Escherichia coli

L-cysteine is an important sulfur-containing amino acid being difficult to produce by microbial fermentation. Due to the lack of high-throughput screening methods, existing genetically engineered bacteria have been developed by simply optimizing the expression of L-cysteine-related genes one by one. To overcome this limitation, in this study, a biosensor-based approach for multilevel biosynthetic pathway optimization of L-cysteine from the DecR regulator variant of Escherichia coli was applied. Through protein engineering, we obtained the DecRN29Y/C81E/M90Q/M99E variant-based biosensor with improved specificity and an 8.71-fold increase in dynamic range. Using the developed biosensor, we performed high-throughput screening of the constructed promoter and RBS combination library, and successfully obtained the optimized strain, which resulted in a 6.29-fold increase in L-cysteine production. Molecular dynamics (MD) simulations and electrophoretic mobility shift analysis (EMSA) showed that the N29Y/C81E/M90Q/M99E variant had enhanced induction activity. This enhancement may be due to the increased binding of the variant to DNA in the presence of L-cysteine, which enhances transcriptional activation. Overall, our biosensor-based strategy provides a promising approach for optimizing biosynthetic pathways at multiple levels. The successful implementation of this strategy demonstrates its potential for screening improved recombinant strains.

Intein-mediated temperature control for complete biosynthesis of sanguinarine and its halogenated derivatives in yeast

While sanguinarine has gained recognition for antimicrobial and antineoplastic activities, its complex conjugated structure and low abundance in plants impede broad applications. Here, we demonstrate the complete biosynthesis of sanguinarine and halogenated derivatives using highly engineered yeast strains. To overcome sanguinarine cytotoxicity, we establish a splicing intein-mediated temperature-responsive gene expression system (SIMTeGES), a simple strategy that decouples cell growth from product synthesis without sacrificing protein activity. To debottleneck sanguinarine biosynthesis, we identify two reticuline oxidases and facilitated functional expression of flavoproteins and cytochrome P450 enzymes via protein molecular engineering. After comprehensive metabolic engineering, we report the production of sanguinarine at a titer of 448.64 mg L-1. Additionally, our engineered strain enables the biosynthesis of fluorinated sanguinarine, showcasing the biotransformation of halogenated derivatives through more than 15 biocatalytic steps. This work serves as a blueprint for utilizing yeast as a scalable platform for biomanufacturing diverse benzylisoquinoline alkaloids and derivatives.

A pilot oral history of plant synthetic biology

The whole field of synthetic biology (SynBio) is only about 20 years old, and plant SynBio is younger still. Nevertheless, within that short time, SynBio in general has drawn more scientific, philosophical, government, and private-sector interest than anything in biology since the recombinant DNA revolution. Plant SynBio, in particular, is now drawing more and more interest in relation to plants' potential to help solve planetary problems such as carbon capture and storage and replacing fossil fuels and feedstocks. As plant SynBio is so young and so fast-developing, we felt it was too soon to try to analyze its history. Instead, we set out to capture the essence of plant SynBio's origins and early development through interviews with 8 of the field's founders, representing 5 countries and 3 continents. We then distilled these founders' personal recollections and reflections into this review, centering the narrative on timelines for pivotal events, articles, funding programs, and quoting from interviews. We have archived the interview recordings and documented timeline entries. This work provides a resource for future historical scholarship.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design

Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments. The findings indicate a noticeable increase in ethanol yield, ranging from 6 % to 10 %, in SDH subunit gene knockout strains compared to the wild-type strain. Moreover, in pursuit of a high-yielding strain for ethanol production, dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase (GPD) and SDH. The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1, Δsdh5Δgpd1, Δsdh6Δgpd1, Δsdh4Δgpd2, Δsdh5Δgpd2, and Δsdh6Δgpd2, with improvements of 21.6 %, 27.9 %, and 22.7 %, respectively. Overall, the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions. In addition, the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes, thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.

Mammalian cell growth characterisation by a non-invasive plate reader assay

Automated and non-invasive mammalian cell analysis is currently lagging behind due to a lack of methods suitable for a variety of cell lines and applications. Here, we report the development of a high throughput non-invasive method for tracking mammalian cell growth and performance based on plate reader measurements. We show the method to be suitable for both suspension and adhesion cell lines, and we demonstrate it can be adopted when cells are grown under different environmental conditions. We establish that the method is suitable to inform on effective drug treatments to be used depending on the cell line considered, and that it can support characterisation of engineered mammalian cells over time. This work provides the scientific community with an innovative approach to mammalian cell screening, also contributing to the current efforts towards high throughput and automated mammalian cell engineering.

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.

A Multiplex MoClo Toolkit for Extensive and Flexible Engineering of Saccharomyces cerevisiae

Synthetic biology toolkits are one of the core foundations on which the field has been built, facilitating and accelerating efforts to reprogram cells and organisms for diverse biotechnological applications. The yeast Saccharomyces cerevisiae, an important model and industrial organism, has benefited from a wide range of toolkits. In particular, the MoClo Yeast Toolkit (YTK) enables the fast and straightforward construction of multigene plasmids from a library of highly characterized parts for programming new cellular behavior in a more predictable manner. While YTK has cultivated a strong parts ecosystem and excels in plasmid construction, it is limited in the extent and flexibility with which it can create new strains of yeast. Here, we describe a new and improved toolkit, the Multiplex Yeast Toolkit (MYT), that extends the capabilities of YTK and addresses strain engineering limitations. MYT provides a set of new integration vectors and selectable markers usable across common laboratory strains, as well as additional assembly cassettes to increase the number of transcriptional units in multigene constructs, CRISPR-Cas9 tools for highly efficient multiplexed vector integration, and three orthogonal and inducible promoter systems for conditional programming of gene expression. With these tools, we provide yeast synthetic biologists with a powerful platform to take their engineering ambitions to exciting new levels.

Establishing chromosomal design-build-test-learn through a synthetic chromosome and its combinatorial reconfiguration

Chromosome-level design-build-test-learn cycles (chrDBTLs) allow systematic combinatorial reconfiguration of chromosomes with ease. Here, we established chrDBTL with a redesigned synthetic Saccharomyces cerevisiae chromosome XV, synXV. We designed and built synXV to harbor strategically inserted features, modified elements, and synonymously recoded genes throughout the chromosome. Based on the recoded chromosome, we developed a method to enable chrDBTL: CRISPR-Cas9-mediated mitotic recombination with endoreduplication (CRIMiRE). CRIMiRE allowed the creation of customized wild-type/synthetic combinations, accelerating genotype-phenotype mapping and synthetic chromosome redesign. We also leveraged synXV as a "build-to-learn" model organism for translation studies by ribosome profiling. We conducted a locus-to-locus comparison of ribosome occupancy between synXV and the wild-type chromosome, providing insight into the effects of codon changes and redesigned features on translation dynamics in vivo. Overall, we established synXV as a versatile reconfigurable system that advances chrDBTL for understanding biological mechanisms and engineering strains.

CRAPS: Chromosomal-Repair-Assisted Pathway Shuffling in Yeast

A fundamental challenge of metabolic engineering involves assembling and screening vast combinations of orthologous enzymes across a multistep biochemical pathway. Current pathway assembly workflows involve combining genetic parts ex vivo and assembling one pathway configuration per tube or well. Here, we present CRAPS, Chromosomal-Repair-Assisted Pathway Shuffling, an in vivo pathway engineering technique that enables the self-assembly of one pathway configuration per cell. CRAPS leverages the yeast chromosomal repair pathway and utilizes a pool of inactive, chromosomally integrated orthologous gene variants corresponding to a target multistep pathway. Supplying gRNAs to the CRAPS host activates the expression of one gene variant per pathway step, resulting in a unique pathway configuration in each cell. We deployed CRAPS to build more than 1000 theoretical combinations of a four-step carotenoid biosynthesis network. Sampling the CRAPS pathway space yielded strains with distinct color phenotypes and carotenoid product profiles. We anticipate that CRAPS will expedite strain engineering campaigns by enabling the generation and sampling of vast biochemical spaces.

Research progress on the application of cell-free synthesis systems for enzymatic processes

Cell-free synthesis systems can complete the transcription and translation process in vitro to produce complex proteins that are difficult to be expressed in traditional cell-based systems. Such systems also can be used for the assembly of efficient localized multienzyme cascades to synthesize products that are toxic to cells. Cell-free synthesis systems provide a simpler and faster engineering solution than living cells, allowing unprecedented design freedom. This paper reviews the latest progress on the application of cell-free synthesis systems in the field of enzymatic catalysis, including cell-free protein synthesis and cell-free metabolic engineering. In cell-free protein synthesis: complex proteins, toxic proteins, membrane proteins, and artificial proteins containing non-natural amino acids can be easily synthesized by directly controlling the reaction conditions in the cell-free system. In cell-free metabolic engineering, the synthesis of desired products can be made more specific and efficient by designing metabolic pathways and screening biocatalysts based on purified enzymes or crude extracts. Through the combination of cell-free synthesis systems and emerging technologies, such as: synthetic biology, microfluidic control, cofactor regeneration, and artificial scaffolds, we will be able to build increasingly complex biomolecule systems. In the next few years, these technologies are expected to mature and reach industrialization, providing innovative platforms for a wide range of biotechnological applications.

In vitro continuous protein evolution empowered by machine learning and automation

Directed evolution has become one of the most successful and powerful tools for protein engineering. However, the efforts required for designing, constructing, and screening a large library of variants can be laborious, time-consuming, and costly. With the recent advent of machine learning (ML) in the directed evolution of proteins, researchers can now evaluate variants in silico and guide a more efficient directed evolution campaign. Furthermore, recent advancements in laboratory automation have enabled the rapid execution of long, complex experiments for high-throughput data acquisition in both industrial and academic settings, thus providing the means to collect a large quantity of data required to develop ML models for protein engineering. In this perspective, we propose a closed-loop in vitro continuous protein evolution framework that leverages the best of both worlds, ML and automation, and provide a brief overview of the recent developments in the field.

Enabling technology and core theory of synthetic biology

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

Automating the design-build-test-learn cycle towards next-generation bacterial cell factories

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.

Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments

Directed protein evolution applies repeated rounds of genetic mutagenesis and phenotypic screening and is often limited by experimental throughput. Through in silico prioritization of mutant sequences, machine learning has been applied to reduce wet lab burden to a level practical for human researchers. On the other hand, robotics permits large batches and rapid iterations for protein engineering cycles, but such capacities have not been well exploited in existing machine learning-assisted directed evolution approaches. Here, we report a scalable and batched method, Bayesian Optimization-guided EVOlutionary (BO-EVO) algorithm, to guide multiple rounds of robotic experiments to explore protein fitness landscapes of combinatorial mutagenesis libraries. We first examined various design specifications based on an empirical landscape of protein G domain B1. Then, BO-EVO was successfully generalized to another empirical landscape of an Escherichia coli kinase PhoQ, as well as simulated NK landscapes with up to moderate epistasis. This approach was then applied to guide robotic library creation and screening to engineer enzyme specificity of RhlA, a key biosynthetic enzyme for rhamnolipid biosurfactants. A 4.8-fold improvement in producing a target rhamnolipid congener was achieved after examining less than 1% of all possible mutants after four iterations. Overall, BO-EVO proves to be an efficient and general approach to guide combinatorial protein engineering without prior knowledge.

Challenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications

Metabolic engineering has evolved towards creating cell factories with increasingly complex pathways as economic criteria push biotechnology to higher value products to provide a sustainable source of speciality chemicals. Optimization of such pathways often requires high combinatory exploration of best pathway balance, and this has led to increasing use of high-throughput automated strain construction platforms or novel optimization techniques. In addition, the low catalytic efficiency of such pathways has shifted emphasis from gene expression strategies towards novel protein engineering to increase specific activity of the enzymes involved so as to limit the metabolic burden associated with excessively high pressure on ribosomal machinery when using massive overexpression systems. Metabolic burden is now generally recognized as a major hurdle to be overcome with consequences on genetic stability but also on the intensified performance needed industrially to attain the economic targets for successful product launch. Increasing awareness of the need to integrate novel genetic information into specific sites within the genome which not only enhance genetic stability (safe harbors) but also enable maximum expression profiles has led to genome-wide assessment of best integration sites, and bioinformatics will facilitate the identification of most probable landing pads within the genome.To facilitate the transfer of novel biotechnological potential to industrial-scale production, more attention, however, has to be paid to engineering metabolic fitness adapted to the specific stress conditions inherent to large-scale fermentation and the inevitable heterogeneity that will occur due to mass transfer limitations and the resulting deviation away from ideal conditions as seen in laboratory-scale validation of the engineered cells. To ensure smooth and rapid transfer of novel cell lines to industry with an accelerated passage through scale-up, better coordination is required form the onset between the biochemical engineers involved in process technology and the genetic engineers building the new strain so as to have an overall strategy able to maximize innovation at all levels. This should be one of our key objectives when building fermentation-friendly chassis organisms.

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

Robotic Construction and Screening of Lanthipeptide Variant Libraries in Escherichia coli

Lanthipeptides are a major class of ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by thioether cross-links called lanthionine (Lan) and methyllanthionine (MeLan). Previously, we developed a method to produce mature lanthipeptides in recombinant Escherichia coli, but manual steps hinder large-scale analogue screening. Here we devised an automated workflow for creating and screening variant libraries of haloduracin, a two-component class II lanthipeptide. An integrated work cell of a synthetic biology foundry was programmed to robotically execute DNA library construction, host transformation, peptide production, mass spectrometry analysis, and activity screening by agar diffusion assay. For recombinantly produced Halα peptides, the sequence-activity relationship of 380 single-residue variants and >1300 triple-residue combinatorial variants were rapidly analyzed in microplates within weeks. The peptide expression levels in E. coli were also visualized via robotic creation and analysis of GFP-lanthipeptide fusions for select peptide mutants. Following shake-flask fermentation and purification, one Halα mutant was confirmed with enhanced specific antimicrobial activity relative to the wild-type peptide. Overall, this approach may be generally applicable for the high-throughput characterization and engineering of RiPP natural products.

Automated high-throughput genome editing platform with an AI learning in situ prediction model

A great number of cell disease models with pathogenic SNVs are needed for the development of genome editing based therapeutics or broadly basic scientific research. However, the generation of traditional cell disease models is heavily dependent on large-scale manual operations, which is not only time-consuming, but also costly and error-prone. In this study, we devise an automated high-throughput platform, through which thousands of samples are automatically edited within a week, providing edited cells with high efficiency. Based on the large in situ genome editing data obtained by the automatic high-throughput platform, we develop a Chromatin Accessibility Enabled Learning Model (CAELM) to predict the performance of cytosine base editors (CBEs), both chromatin accessibility and the context-sequence are utilized to build the model, which accurately predicts the result of in situ base editing. This work is expected to accelerate the development of BE-based genetic therapies.

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.

Effects of DNA template preparation on variability in cell-free protein production

DNA templates for protein production remain an unexplored source of variability in the performance of cell-free expression (CFE) systems. To characterize this variability, we investigated the effects of two common DNA extraction methodologies, a postprocessing step and manual versus automated preparation on protein production using CFE. We assess the concentration of the DNA template, the quality of the DNA template in terms of physical damage and the quality of the DNA solution in terms of purity resulting from eight DNA preparation workflows. We measure the variance in protein titer and rate of protein production in CFE reactions associated with the biological replicate of the DNA template, the technical replicate DNA solution prepared with the same workflow and the measurement replicate of nominally identical CFE reactions. We offer practical guidance for preparing and characterizing DNA templates to achieve acceptable variability in CFE performance.

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.

Directed evolution of a cyclodipeptide synthase with new activities via label-free mass spectrometric screening

Directed evolution is a powerful approach to engineer enzymes via iterative creation and screening of variant libraries. However, assay development for high-throughput mutant screening remains challenging, particularly for new catalytic activities. Mass spectrometry (MS) analysis is label-free and well suited for untargeted discovery of new enzyme products but is traditionally limited by slow speed. Here we report an automated workflow for directed evolution of new enzymatic activities via high-throughput library creation and label-free MS screening. For a proof of concept, we chose to engineer a cyclodipeptide synthase (CDPS) that synthesizes diketopiperazine (DKP) compounds with therapeutic potential. In recombinant Escherichia coli, site-saturation mutagenesis (SSM) and error-prone PCR (epPCR) libraries expressing CDPS mutants were automatically created and cultivated on an integrated work cell. Culture supernatants were then robotically processed for matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS analysis at a rate of 5 s per sample. The resulting mass spectral data were processed via custom computational algorithms, which performed a multivariant analysis of 108 theoretical mass-to-charge (m/z) values of 190 possible DKP molecules within a mass window of 115–373 Da. An F186L CDPS mutant was isolated to produce cyclo(l-Phe–l-Val), which is undetectable in the product profile of the wild-type enzyme. This robotic, label-free MS screening approach may be generally applicable to engineering other enzymes with new activities in high throughput.

A robotic workflow for directed evolution of new enzymatic activities via high-throughput library creation and label-free MS screening.

Potential use of microbial engineering in single-cell protein production

Single-cell proteins (SCPs) have been widely used in human food and animal feed applications, still, there are challenges in their production and commercialization. Recently, advances in microbial synthetic biology, genomic engineering, and biofoundry technologies have offered capabilities to effectively and rapidly engineer microorganisms for improving the productivity, nutritional, and functional quality of SCPs. In this review, we discuss various synthetic biology, genomic engineering, and biofoundry tools that can be harnessed for SCP production and genetic modification. We also describe the current and potential applications of genetic modification in producing intermediate feedstocks, as well as biomass-based and multifunctional SCPs. Finally, we discuss the technological and policy-control related challenges encountered when deploying genetic modification in SCP production for animal feed and human food applications.

AutoESD: a web tool for automatic editing sequence design for genetic manipulation of microorganisms

Advances in genetic manipulation and genome engineering techniques have enabled on-demand targeted deletion, insertion, and substitution of DNA sequences. One important step in these techniques is the design of editing sequences (e.g. primers, homologous arms) to precisely target and manipulate DNA sequences of interest. Experimental biologists can employ multiple tools in a stepwise manner to assist editing sequence design (ESD), but this requires various software involving non-standardized data exchange and input/output formats. Moreover, necessary quality control steps might be overlooked by non-expert users. This approach is low-throughput and can be error-prone, which illustrates the need for an automated ESD system. In this paper, we introduce AutoESD (https://autoesd.biodesign.ac.cn/), which designs editing sequences for all steps of genetic manipulation of many common homologous-recombination techniques based on screening-markers. Notably, multiple types of manipulations for different targets (CDS or intergenic region) can be processed in one submission. Moreover, AutoESD has an entirely cloud-based serverless architecture, offering high reliability, robustness and scalability which is capable of parallelly processing hundreds of design tasks each having thousands of targets in minutes. To our knowledge, AutoESD is the first cloud platform enabling precise, automated, and high-throughput ESD across species, at any genomic locus for all manipulation types.

RoboMoClo: A Robotics-Assisted Modular Cloning Framework for Multiple Gene Assembly in Biofoundry

Efficient and versatile DNA assembly frameworks have had an impact on promoting synthetic biology to build complex biological systems. To accelerate system development, laboratory automation (or biofoundry) provides an opportunity to construct organisms and DNA assemblies via computer-aided design. However, a modular cloning (MoClo) system for multiple DNA assemblies limits the biofoundry workflow in terms of simplicity and feasibility by preparing the number of cloning materials such as destination vectors prior to the automation process. Herein, we propose robot-assisted MoClo (RoboMoClo) to accelerate a synthetic biology project with multiple gene expressions at the biofoundry. The architecture of the RoboMoClo framework provides a hybrid strategy of hierarchical gene assembly and iterative gene assembly, and fewer destination vectors compared with other MoClo systems. An industrial bacterium, Corynebacterium glutamicum, was used as a model host for RoboMoClo. After building a biopart library (promoter and terminator; level 0) and evaluating its features (level 1), various transcriptional directions in multiple gene assemblies (level 2) were studied using the RoboMoClo vectors. Among the constructs, the convergent construct exhibited potential transcriptional interference through the collision of RNA polymerases. To study design of experiment-guided lycopene biosynthesis in C. glutamicum (levels 1, 2, and 3), the biofoundry-assisted multiple gene assembly was demonstrated as a proof-of-concept by constructing various sub-pathway units (level 2) and pathway units (level 3) for C. glutamicum. The RoboMoClo framework provides an improved MoClo toolkit for laboratory automation in a synthetic biology application.

Strategies to increase tolerance and robustness of industrial microorganisms

The development of a cost-competitive bioprocess requires that the cell factory converts the feedstock into the product of interest at high rates and yields. However, microbial cell factories are exposed to a variety of different stresses during the fermentation process. These stresses can be derived from feedstocks, metabolism, or industrial production processes, limiting production capacity and diminishing competitiveness. Improving stress tolerance and robustness allows for more efficient production and ultimately makes a process more economically viable. This review summarises general trends and updates the most recent developments in technologies to improve the stress tolerance of microorganisms. We first look at evolutionary, systems biology and computational methods as examples of non-rational approaches. Then we review the (semi-)rational approaches of membrane and transcription factor engineering for improving tolerance phenotypes. We further discuss challenges and perspectives associated with these different approaches.

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.

Multiplexed direct detection of barcoded protein reporters on a nanopore array

Detection of specific proteins using nanopores is currently challenging. To address this challenge, we developed a collection of over twenty nanopore-addressable protein tags engineered as reporters (NanoporeTERs, or NTERs). NTERs are constructed with a secretion tag, folded domain and a nanopore-targeting C-terminal tail in which arbitrary peptide barcodes can be encoded. We demonstrate simultaneous detection of up to nine NTERs expressed in bacterial or human cells using MinION nanopore sensor arrays.

Synthetic or natural? Metabolic engineering for assimilation and valorization of methanol

Single carbon (C1) substrates such as methanol are gaining increasing attention as cost-effective and environmentally friendly microbial feedstocks. Recent impressive metabolic engineering efforts to import C1 catabolic pathways into the non-methylotrophic bacterium Escherichia coli have led to synthetic strains growing on methanol as the sole carbon source. However, the growth rate and product yield in these strains remain inferior to native methylotrophs. Meanwhile, an ever-expanding genetic engineering toolbox is increasing the tractability of native C1 utilizers, raising the question of whether it is best to use an engineered strain or a native host for the microbial assimilation of C1 substrates. Here we provide perspective on this debate, using recent work in E. coli and the methylotrophic acetogen Eubacterium limosum as case studies.

Directed genome evolution driven by structural rearrangement techniques

Directed genome evolution simulates the process of natural evolution at the genomic level in the laboratory to generate desired phenotypes. Here we review the applications of recent technological advances in genome writing and editing to directed genome evolution, with a focus on structural rearrangement techniques. We highlight how these techniques can be used to generate diverse genotypes, and to accelerate the evolution of phenotypic traits. We also discuss the perspectives of directed genome evolution.

GEDpm-cg: Genome Editing Automated Design Platform for Point Mutation Construction in Corynebacterium glutamicum

Advances in robotic system-assisted genome editing techniques and computer-aided design tools have significantly facilitated the development of microbial cell factories. Although multiple separate software solutions are available for vector DNA assembly, genome editing, and verification, by far there is still a lack of complete tool which can provide a one-stop service for the entire genome modification process. This makes the design of numerous genetic modifications, especially the construction of mutations that require strictly precise genetic manipulation, a laborious, time-consuming and error-prone process. Here, we developed a free online tool called GEDpm-cg for the design of genomic point mutations in C. glutamicum. The suicide plasmid-mediated counter-selection point mutation editing method and the overlap-based DNA assembly method were selected to ensure the editability of any single nucleotide at any locus in the C. glutamicum chromosome. Primers required for both DNA assembly of the vector for genetic modification and sequencing verification were provided as design results to meet all the experimental needs. An in-silico design task of over 10,000 single point mutations can be completed in 5 min. Finally, three independent point mutations were successfully constructed in C. glutamicum guided by GEDpm-cg, which confirms that the in-silico design results could accurately and seamlessly be bridged with in vivo or in vitro experiments. We believe this platform will provide a user-friendly, powerful and flexible tool for large-scale mutation analysis in the industrial workhorse C. glutamicum via robotic/software-assisted systems.

BioParts-A Biological Parts Search Portal and Updates to the ICE Parts Registry Software Platform

Capturing, storing, and sharing biological DNA parts data are integral parts of synthetic biology research. Here, we detail updates to the ICE biological parts registry software platform that enable these processes, describe our implementation of the Web of Registries concept using ICE, and establish Bioparts, a search portal for biological parts available in the public domain. The Web of Registries enables standalone ICE installations to securely connect and form a distributed parts database. This distributed database allows users from one registry to query and access plasmid, strain, (DNA) part, plant seed, and protein entry types in other connected registries. Users can also transfer entries from one ICE registry to another or make them publicly accessible. Bioparts, the new search portal, combines the ease and convenience of modern web search engines with the capabilities of bioinformatics search tools such as BLAST. This portal, available at bioparts.org, allows anyone to search for publicly accessible biological part information (e.g., NCBI, iGEM, SynBioHub, Addgene), including parts publicly accessible through ICE Registries. Additionally, the portal offers a REST API that enables third-party applications and tools to access the portal's functionality programmatically.

Biofoundry-assisted expression and characterization of plant proteins

Many goals in synthetic biology, including the elucidation and refactoring of biosynthetic pathways and the engineering of regulatory circuits and networks, require knowledge of protein function. In plants, the prevalence of large gene families means it can be particularly challenging to link specific functions to individual proteins. However, protein characterization has remained a technical bottleneck, often requiring significant effort to optimize expression and purification protocols. To leverage the ability of biofoundries to accelerate design-built-test-learn cycles, we present a workflow for automated DNA assembly and cell-free expression of plant proteins that accelerates optimization and enables rapid screening of enzyme activity. First, we developed a phytobrick-compatible Golden Gate DNA assembly toolbox containing plasmid acceptors for cell-free expression using Escherichia coli or wheat germ lysates as well as a set of N- and C-terminal tag parts for detection, purification and improved expression/folding. We next optimized automated assembly of miniaturized cell-free reactions using an acoustic liquid handling platform and then compared tag configurations to identify those that increase expression. We additionally developed a luciferase-based system for rapid quantification that requires a minimal 11-amino acid tag and demonstrate facile removal of tags following synthesis. Finally, we show that several functional assays can be performed with cell-free protein synthesis reactions without the need for protein purification. Together, the combination of automated assembly of DNA parts and cell-free expression reactions should significantly increase the throughput of experiments to test and understand plant protein function and enable the direct reuse of DNA parts in downstream plant engineering workflows.

Growth-coupled selection of synthetic modules to accelerate cell factory development

Synthetic biology has brought about a conceptual shift in our ability to redesign microbial metabolic networks. Combining metabolic pathway-modularization with growth-coupled selection schemes is a powerful tool that enables deep rewiring of the cell factories’ biochemistry for rational bioproduction.

The evolving art of creating genetic diversity: From directed evolution to synthetic biology

The ability to engineer biological systems, whether to introduce novel functionality or improved performance, is a cornerstone of biotechnology and synthetic biology. Typically, this requires the generation of genetic diversity to explore variations in phenotype, a process that can be performed at many levels, from single molecule targets (i.e., in directed evolution of enzymes) to whole organisms (e.g., in chassis engineering). Recent advances in DNA synthesis technology and automation have enhanced our ability to create variant libraries with greater control and throughput. This review highlights the latest developments in approaches to create such a hierarchy of diversity from the enzyme level to entire pathways in vitro, with a focus on the creation of combinatorial libraries that are required to navigate a target's vast design space successfully to uncover significant improvements in function.

Recent Advances in Heterologous Synthesis Paving Way for Future Green-Modular Bioindustries: A Review With Special Reference to Isoflavonoids

Isoflavonoids are well-known plant secondary metabolites that have gained importance in recent time due to their multiple nutraceutical and pharmaceutical applications. In plants, isoflavonoids play a role in plant defense and can confer the host plant a competitive advantage to survive and flourish under environmental challenges. In animals, isoflavonoids have been found to interact with multiple signaling pathways and have demonstrated estrogenic, antioxidant and anti-oncologic activities in vivo. The activity of isoflavonoids in the estrogen pathways is such that the class has also been collectively called phytoestrogens. Over 2,400 isoflavonoids, predominantly from legumes, have been identified so far. The biosynthetic pathways of several key isoflavonoids have been established, and the genes and regulatory components involved in the biosynthesis have been characterized. The biosynthesis and accumulation of isoflavonoids in plants are regulated by multiple complex environmental and genetic factors and interactions. Due to this complexity of secondary metabolism regulation, the export and engineering of isoflavonoid biosynthetic pathways into non-endogenous plants are difficult, and instead, the microorganisms Saccharomyces cerevisiae and Escherichia coli have been adapted and engineered for heterologous isoflavonoid synthesis. However, the current ex-planta production approaches have been limited due to slow enzyme kinetics and traditionally laborious genetic engineering methods and require further optimization and development to address the required titers, reaction rates and yield for commercial application. With recent progress in metabolic engineering and the availability of advanced synthetic biology tools, it is envisaged that highly efficient heterologous hosts will soon be engineered to fulfill the growing market demand.

Synthetic biology as driver for the biologization of materials sciences

Materials in nature have fascinating properties that serve as a continuous source of inspiration for materials scientists. Accordingly, bio-mimetic and bio-inspired approaches have yielded remarkable structural and functional materials for a plethora of applications. Despite these advances, many properties of natural materials remain challenging or yet impossible to incorporate into synthetic materials. Natural materials are produced by living cells, which sense and process environmental cues and conditions by means of signaling and genetic programs, thereby controlling the biosynthesis, remodeling, functionalization, or degradation of the natural material. In this context, synthetic biology offers unique opportunities in materials sciences by providing direct access to the rational engineering of how a cell senses and processes environmental information and translates them into the properties and functions of materials. Here, we identify and review two main directions by which synthetic biology can be harnessed to provide new impulses for the biologization of the materials sciences: first, the engineering of cells to produce precursors for the subsequent synthesis of materials. This includes materials that are otherwise produced from petrochemical resources, but also materials where the bio-produced substances contribute unique properties and functions not existing in traditional materials. Second, engineered living materials that are formed or assembled by cells or in which cells contribute specific functions while remaining an integral part of the living composite material. We finally provide a perspective of future scientific directions of this promising area of research and discuss science policy that would be required to support research and development in this field.

Rapid in vitro prototyping of O-methyltransferases for pathway applications in Escherichia coli

O-Methyltransferases are ubiquitous enzymes involved in biosynthetic pathways for secondary metabolites such as bacterial antibiotics, human catecholamine neurotransmitters, and plant phenylpropanoids. While thousands of putative O-methyltransferases are found in sequence databases, few examples are functionally characterized. From a pathway engineering perspective, however, it is crucial to know the substrate and product ranges of the respective enzymes to fully exploit their catalytic power. In this study, we developed an in vitro prototyping workflow that allowed us to screen ∼30 enzymes against five substrates in 3 days with high reproducibility. We combined in vitro transcription/translation of the genes of interest with a microliter-scale enzymatic assay in 96-well plates. The substrate conversion was indirectly measured by quantifying the consumption of the S-adenosyl-L-methionine co-factor by time-resolved fluorescence resonance energy transfer rather than time-consuming product analysis by chromatography. This workflow allowed us to rapidly prototype thus far uncharacterized O-methyltransferases for future use as biocatalysts.

Tasting the terroir of wine yeast innovation

Wine is an archetypal traditional fermented beverage with strong territorial and socio-cultural connotations. Its 7000 year history is patterned by a tradition of innovation. Every value-adding innovation − whether in the vineyard, winery, supply chain or marketplace − that led to the invention of a new tradition spurred progress and created a brighter future from past developments. In a way, wine traditions can be defined as remembered innovations from the distant past − inherited knowledge and wisdom that withstood the test of time. Therefore, it should not be assumed a priori that tradition and innovation are polar opposites. The relations between the forces driven by the anchors of tradition and the wings of innovation do not necessarily involve displacement, conflict or exclusiveness. Innovation can strengthen wine tradition, and the reinvention of a tradition-bound practice, approach or concept can foster innovation. In cases where a paradigm-shifting innovation disrupts a tradition, the process of such an innovation transitioning into a radically new tradition can become protracted while proponents of divergent opinions duke it out. Sometimes these conflicting opinions are based on fact, and sometimes not. The imperfections of such a debate between the ‘ancients’ and the ‘moderns’ can, from time to time, obscure the line between myth and reality. Therefore, finding the right balance between traditions worth keeping and innovations worth implementing can be complex. The intent here is to harness the creative tension between science fiction and science fact when innovation's first-principles challenge the status quo by re-examining the foundational principles about a core traditional concept, such as terroir. Poignant questions are raised about the importance of the terroir (biogeography) of yeasts and the value of the microbiome of grapes to wine quality. This article imagines a metaphorical terroir free from cognitive biases where diverse perspectives can converge to uncork the effervescent power of territorial yeast populations as well as ‘nomadic’ yeast starter cultures. At the same time, this paper also engages in mental time-travel. A future scenario is imagined, explored, tested and debated where terroir-less yeast avatars are equipped with designer genomes to safely and consistently produce, individually or in combination with region-specific wild yeasts and or other starter cultures, high-quality wine according to the preferences of consumers in a range of markets. The purpose of this review is to look beyond the horizon and to synthesize a link between what we know now and what could be. This article informs readers where to look without suggesting what they must see as a way forward. In the context of one of the world's oldest fermentation industries − steeped in a rich history of tradition and innovation − the mantra here is: respect the past, lead the present and secure the future of wine.

A Perspective on Synthetic Biology in Drug Discovery and Development-Current Impact and Future Opportunities

The global impact of synthetic biology has been accelerating, because of the plummeting cost of DNA synthesis, advances in genetic engineering, growing understanding of genome organization, and explosion in data science. However, much of the discipline's application in the pharmaceutical industry remains enigmatic. In this review, we highlight recent examples of the impact of synthetic biology on target validation, assay development, hit finding, lead optimization, and chemical synthesis, through to the development of cellular therapeutics. We also highlight the availability of tools and technologies driving the discipline. Synthetic biology is certainly impacting all stages of drug discovery and development, and the recognition of the discipline's contribution can further enhance the opportunities for the drug discovery and development value chain.

Computer-Aided Design and Pre-validation of Large Batches of DNA Assemblies

Type-2S restriction enzymes allow the routine assembly of large batches of synthetic constructs from individual genetic parts. However, design flaws in the part sequence can cause assembly failures, incurring troubleshooting costs and project delays. As a result, the careful design and checking of the assembly plan is often a bottleneck of large assembly projects, and may require computational support. This chapter demonstrates the use of two free and open-source web applications accelerating this task by automating genetic part design and simulating type-2S cloning to detect potential assembly issues.

Joint Universal Modular Plasmids: A Flexible Platform for Golden Gate Assembly in Any Microbial Host

Modular cloning standards based on Golden Gate DNA assembly allow for construction of complex DNA constructs over several rounds of assembly. Despite being reliable and automation-friendly, each standard uses a specific set of vectors, requiring researchers to generate new tool kits for novel hosts and cloning applications. JUMP vectors (Valenzuela-Ortega and French, bioRxiv 799585, 2019) combine the robustness of modular cloning standards with the Standard European Vector Architecture and a flexible design that allows researchers to easily modify the vector backbone via secondary cloning sites. This flexibility allows for JUMP vectors to be used in a wide variety of applications and hosts.

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.

100th Anniversary of Macromolecular Science Viewpoint: Data-Driven Protein Design

The design of synthetic proteins with the desired function is a long-standing goal in biomolecular science, with broad applications in biochemical engineering, agriculture, medicine, and public health. Rational de novo design and experimental directed evolution have achieved remarkable successes but are challenged by the requirement to find functional "needles" in the vast "haystack" of protein sequence space. Data-driven models for fitness landscapes provide a predictive map between protein sequence and function and can prospectively identify functional candidates for experimental testing to greatly improve the efficiency of this search. This Viewpoint reviews the applications of machine learning and, in particular, deep learning as part of data-driven protein engineering platforms. We highlight recent successes, review promising computational methodologies, and provide an outlook on future challenges and opportunities. The article is written for a broad audience comprising both polymer and protein scientists and computer and data scientists interested in an up-to-date review of recent innovations and opportunities in this rapidly evolving field.

Joint universal modular plasmids (JUMP): a flexible vector platform for synthetic biology

Generation of new DNA constructs is an essential process in modern life science and biotechnology. Modular cloning systems based on Golden Gate cloning, using Type IIS restriction endonucleases, allow assembly of complex multipart constructs from reusable basic DNA parts in a rapid, reliable and automation-friendly way. Many such toolkits are available, with varying degrees of compatibility, most of which are aimed at specific host organisms. Here, we present a vector design which allows simple vector modification by using modular cloning to assemble and add new functions in secondary sites flanking the main insertion site (used for conventional modular cloning). Assembly in all sites is compatible with the PhytoBricks standard, and vectors are compatible with the Standard European Vector Architecture (SEVA) as well as BioBricks. We demonstrate that this facilitates the construction of vectors with tailored functions and simplifies the workflow for generating libraries of constructs with common elements. We have made available a collection of vectors with 10 different microbial replication origins, varying in copy number and host range, and allowing chromosomal integration, as well as a selection of commonly used basic parts. This design expands the range of hosts which can be easily modified by modular cloning and acts as a toolkit which can be used to facilitate the generation of new toolkits with specific functions required for targeting further hosts.

Genetic Toolkits to Design and Build Mammalian Synthetic Systems

Construction of DNA-encoded programs is central to synthetic biology and the chosen method often determines the time required to design and build constructs for testing. Here, we describe and summarise key features of the available toolkits for DNA construction for mammalian cells. We compare the different cloning strategies based on their complexity and the time needed to generate constructs of different sizes, and we reflect on why Golden Gate toolkits now dominate due to their modular design. We look forward to future advances, including accessory packs for cloning toolkits that can facilitate editing, orthogonality, advanced regulation, and integration into synthetic chromosome construction.

A biofoundry workflow for the identification of genetic determinants of microbial growth inhibition

Biofoundries integrate high-throughput software and hardware platforms with synthetic biology approaches to enable the design, execution and analyses of large-scale experiments. The unique and powerful combination of laboratory infrastructure and expertise in molecular biology and automation programming, provide flexible resources for a wide range of workflows and research areas. Here, we demonstrate the applicability of biofoundries to molecular microbiology, describing the development and application of automated workflows to identify the genetic basis of growth inhibition of the plant pathogen Streptomyces scabies by a Pseudomonas strain isolated from a potato field. Combining transposon mutagenesis with automated high-throughput antagonistic assays, the workflow accelerated the screening of 2880 mutants to correlate growth inhibition with a biosynthetic gene cluster within 2 weeks.