Engineering biological systems using automated biofoundries

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.

Build a Sustainable Vaccines Industry with Synthetic Biology

The vaccines industry has not changed appreciably in decades regarding technology, and has struggled to remain viable, with large companies withdrawing from production. Meanwhile, there has been no let-up in outbreaks of viral disease, at a time when the biopharmaceuticals industry is discussing downsizing. The distributed manufacturing model aligns well with this, and the advent of synthetic biology promises much in terms of vaccine design. Biofoundries separate design from manufacturing, a hallmark of modern engineering. Once designed in a biofoundry, digital code can be transferred to a small-scale manufacturing facility close to the point of care, rather than physically transferring cold-chain-dependent vaccine. Thus, biofoundries and distributed manufacturing have the potential to open up a new era of biomanufacturing, one based on digital biology and information systems. This seems a better model for tackling future outbreaks and pandemics.

Setting Up an Automated Biomanufacturing Laboratory

Laboratory automation is a key enabling technology for genetic engineering that can lead to higher throughput, more efficient and accurate experiments, better data management and analysis, decrease in the DBT (Design, Build, and Test) cycle turnaround, increase of reproducibility, and savings in lab resources. Choosing the correct framework among so many options available in terms of software, hardware, and skills needed to operate them is crucial for the success of any automation project. This chapter explores the multiple aspects to be considered for the solid development of a biofoundry project including available software and hardware tools, resources, strategies, partnerships, and collaborations in the field needed to speed up the translation of research results to solve important society problems.

Using a Design of Experiments Approach to Inform the Design of Hybrid Synthetic Yeast Promoters

Hybrid promoter engineering takes advantage of the modular nature of eukaryotic promoters by combining discrete promoter motifs to confer novel regulatory function. By combinatorially screening sequence libraries for trans-acting transcriptional operators, activators, repressors and core promoter sequences, it is possible to derive constitutive or inducible promoter collections covering a broad range of expression strengths. However, combinatorial approaches to promoter design can result in highly complex, multidimensional design spaces, which can be experimentally costly to thoroughly explore in vivo. Here, we describe an in silico pipeline for the design of hybrid promoter libraries that employs a Design of Experiments (DoE) approach to reduce experimental burden and efficiently explore the promoter fitness landscape. We also describe a software pipeline to ensure that the designed promoter sequences are compatible with the YTK assembly standard.

Usage of Digital Twins Along a Typical Process Development Cycle

Digital methods for process design, monitoring, and control can convert classical trial-and-error bioprocess development to a quantitative engineering approach. By interconnecting hardware, software, data, and humans currently untapped process optimization potential can be accessed. The key component within such a framework is a digital twin interacting with its physical process counterpart. In this chapter, we show how digital twin guided process development can be applied on an exemplary microbial cultivation process. The usage of digital twins is described along a typical process development cycle, ranging from early strain characterization to real-time control applications. Along an illustrative case study on microbial upstream bioprocessing, we emphasize that digital twins can integrate entire process development cycles if the digital twin itself and the underlying models are continuously adapted to newly available data. Therefore, the digital twin can be regarded as a powerful knowledge management tool and a decision support system for efficient process development. Its full potential can be deployed in a real-time environment where targeted control actions can further improve process performance.

CRISPR/Cas9 Systems for the Development of Saccharomyces cerevisiae Cell Factories

Synthetic yeast cell factories provide a remarkable solution for the sustainable supply of a range of products, ranging from large-scale industrial chemicals to high-value pharmaceutical compounds. Synthetic biology is a field in which metabolic pathways are intensively studied and engineered. The clustered, regularly interspaced, short, palindromic repeat-associated (CRISPR)/CRISPR-associated protein 9 (Cas9) technology has emerged as the state-of-the-art gene editing technique for synthetic biology. Recently, the use of different CRISPR/Cas9 systems has been extended to the field of yeast engineering for single-nucleotide resolution editing, multiple-gene editing, transcriptional regulation, and genome-scale modifications. Such advancing systems have led to accelerated microbial engineering involving less labor and time and also enhanced the understanding of cellular genetics and physiology. This review provides a brief overview of the latest research progress and the use of CRISPR/Cas9 systems in genetic manipulation, with a focus on the applications of Saccharomyces cerevisiae cell factory engineering.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

Plant Biosystems Design Research Roadmap 1.0

Human life intimately depends on plants for food, biomaterials, health, energy, and a sustainable environment. Various plants have been genetically improved mostly through breeding, along with limited modification via genetic engineering, yet they are still not able to meet the ever-increasing needs, in terms of both quantity and quality, resulting from the rapid increase in world population and expected standards of living. A step change that may address these challenges would be to expand the potential of plants using biosystems design approaches. This represents a shift in plant science research from relatively simple trial-and-error approaches to innovative strategies based on predictive models of biological systems. Plant biosystems design seeks to accelerate plant genetic improvement using genome editing and genetic circuit engineering or create novel plant systems through de novo synthesis of plant genomes. From this perspective, we present a comprehensive roadmap of plant biosystems design covering theories, principles, and technical methods, along with potential applications in basic and applied plant biology research. We highlight current challenges, future opportunities, and research priorities, along with a framework for international collaboration, towards rapid advancement of this emerging interdisciplinary area of research. Finally, we discuss the importance of social responsibility in utilizing plant biosystems design and suggest strategies for improving public perception, trust, and acceptance.

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.

Automation in the Life Science Research Laboratory

Protocols in the academic life science laboratory are heavily reliant on the manual manipulation of tools, reagents and instruments by a host of research staff and students. In contrast to industrial and clinical laboratory environments, the usage of automation to augment or replace manual tasks is limited. Causes of this 'automation gap' are unique to academic research, with rigid short-term funding structures, high levels of protocol variability and a benevolent culture of investment in people over equipment. Automation, however, can bestow multiple benefits through improvements in reproducibility, researcher efficiency, clinical translation, and safety. Less immediately obvious are the accompanying limitations, including obsolescence and an inhibitory effect on the freedom to innovate. Growing the range of automation options suitable for research laboratories will require more flexible, modular and cheaper designs. Academic and commercial developers of automation will increasingly need to design with an environmental awareness and an understanding that large high-tech robotic solutions may not be appropriate for laboratories with constrained financial and spatial resources. To fully exploit the potential of laboratory automation, future generations of scientists will require both engineering and biology skills. Automation in the research laboratory is likely to be an increasingly critical component of future research programs and will continue the trend of combining engineering and science expertise together to answer novel research questions.

Rapid, robust plasmid verification by de novo assembly of short sequencing reads

Plasmids are a foundational tool for basic and applied research across all subfields of biology. Increasingly, researchers in synthetic biology are relying on and developing massive libraries of plasmids as vectors for directed evolution, combinatorial gene circuit tests, and for CRISPR multiplexing. Verification of plasmid sequences following synthesis is a crucial quality control step that creates a bottleneck in plasmid fabrication workflows. Crucially, researchers often elect to forego the cumbersome verification step, potentially leading to reproducibility and—depending on the application—security issues. In order to facilitate plasmid verification to improve the quality and reproducibility of life science research, we developed a fast, simple, and open source pipeline for assembly and verification of plasmid sequences from Illumina reads. We demonstrate that our pipeline, which relies on de novo assembly, can also be used to detect contaminating sequences in plasmid samples. In addition to presenting our pipeline, we discuss the role for verification and quality control in the increasingly complex life science workflows ushered in by synthetic biology.

The transition of Rhodobacter sphaeroides into a microbial cell factory

Microbial cell factories are the workhorses of industrial biotechnology and improving their performances can significantly optimize industrial bioprocesses. Microbial strain engineering is often employed for increasing the competitiveness of bio‐based product synthesis over more classical petroleum‐based synthesis. Recently, efforts for strain optimization have been standardized within the iterative concept of “design‐build‐test‐learn” (DBTL). This approach has been successfully employed for the improvement of traditional cell factories like Escherichia coli and Saccharomyces cerevisiae. Within the past decade, several new‐to‐industry microorganisms have been investigated as novel cell factories, including the versatile α‐proteobacterium Rhodobacter sphaeroides. Despite its history as a laboratory strain for fundamental studies, there is a growing interest in this bacterium for its ability to synthesize relevant compounds for the bioeconomy, such as isoprenoids, poly‐β‐hydroxybutyrate, and hydrogen. In this study, we reflect on the reasons for establishing R. sphaeroides as a cell factory from the perspective of the DBTL concept. Moreover, we discuss current and future opportunities for extending the use of this microorganism for the bio‐based economy. We believe that applying the DBTL pipeline for R. sphaeroides will further strengthen its relevance as a microbial cell factory. Moreover, the proposed use of strain engineering via the DBTL approach may be extended to other microorganisms that have not been critically investigated yet for industrial applications.

Metabolic pathway engineering: Perspectives and applications

BACKGROUND

Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION

In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Unlocking nature's biosynthetic potential by directed genome evolution

Microorganisms have been increasingly explored as microbial cell factories for production of fuels, chemicals, drugs, and materials. Among the various metabolic engineering strategies, directed genome evolution has emerged as one of the most powerful tools to unlock the full biosynthetic potential of microorganisms. Here we summarize the directed genome evolution strategies that have been developed in recent years, including adaptive laboratory evolution and various targeted genome-scale engineering strategies, and discuss their applications in basic and applied biological research.

Biosystems Design by Machine Learning

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

Rapid prototyping of microbial production strains for the biomanufacture of potential materials monomers

Bio-based production of industrial chemicals using synthetic biology can provide alternative green routes from renewable resources, allowing for cleaner production processes. To efficiently produce chemicals on-demand through microbial strain engineering, biomanufacturing foundries have developed automated pipelines that are largely compound agnostic in their time to delivery. Here we benchmark the capabilities of a biomanufacturing pipeline to enable rapid prototyping of microbial cell factories for the production of chemically diverse industrially relevant material building blocks. Over 85 days the pipeline was able to produce 17 potential material monomers and key intermediates by combining 160 genetic parts into 115 unique biosynthetic pathways. To explore the scale-up potential of our prototype production strains, we optimized the enantioselective production of mandelic acid and hydroxymandelic acid, achieving gram-scale production in fed-batch fermenters. The high success rate in the rapid design and prototyping of microbially-produced material building blocks reveals the potential role of biofoundries in leading the transition to sustainable materials production.

Beyond natural: synthetic expansions of botanical form and function

Powered by developments that enabled genome-scale investigations, systems biology emerged as a field aiming to understand how phenotypes emerge from network functions. These advances fuelled a new engineering discipline focussed on synthetic reconstructions of complex biological systems with the goal of predictable rational design and control. Initially, progress in the nascent field of synthetic biology was slow due to the ad hoc nature of molecular biology methods such as cloning. The application of engineering principles such as standardisation, together with several key technical advances, enabled a revolution in the speed and accuracy of genetic manipulation. Combined with mathematical and statistical modelling, this has improved the predictability of engineering biological systems of which nonlinearity and stochasticity are intrinsic features leading to remarkable achievements in biotechnology as well as novel insights into biological function. In the past decade, there has been slow but steady progress in establishing foundations for synthetic biology in plant systems. Recently, this has enabled model-informed rational design to be successfully applied to the engineering of plant gene regulation and metabolism. Synthetic biology is now poised to transform the potential of plant biotechnology. However, reaching full potential will require conscious adjustments to the skillsets and mind sets of plant scientists.

Role of Digital Microfluidics in Enabling Access to Laboratory Automation and Making Biology Programmable

Digital microfluidics (DMF) is a liquid handling technique that has been demonstrated to automate biological experimentation in a low-cost, rapid, and programmable manner. This review discusses the role of DMF as a "digital bioconverter"-a tool to connect the digital aspects of the design-build-learn cycle with the physical execution of experiments. Several applications are reviewed to demonstrate the utility of DMF as a digital bioconverter, namely, genetic engineering, sample preparation for sequencing and mass spectrometry, and enzyme-, immuno-, and cell-based screening assays. These applications show that DMF has great potential in the role of a centralized execution platform in a fully integrated pipeline for the production of novel organisms and biomolecules. In this paper, we discuss how the function of a DMF device within such a pipeline is highly dependent on integration with different sensing techniques and methodologies from machine learning and big data. In addition to that, we examine how the capacity of DMF can in some cases be limited by known technical and operational challenges and how consolidated efforts in overcoming these challenges will be key to the development of DMF as a major enabling technology in the computer-aided biology framework.

Cell-Free Protein Synthesis as a Prototyping Platform for Mammalian Synthetic Biology

The field of mammalian synthetic biology is expanding quickly, and technologies for engineering large synthetic gene circuits are increasingly accessible. However, for mammalian cell engineering, traditional tissue culture methods are slow and cumbersome, and are not suited for high-throughput characterization measurements. Here we have utilized mammalian cell-free protein synthesis (CFPS) assays using HeLa cell extracts and liquid handling automation as an alternative to tissue culture and flow cytometry-based measurements. Our CFPS assays take a few hours, and we have established optimized protocols for small-volume reactions using automated acoustic liquid handling technology. As a proof-of-concept, we characterized diverse types of genetic regulation in CFPS, including T7 constitutive promoter variants, internal ribosomal entry sites (IRES) constitutive translation-initiation sequence variants, CRISPR/dCas9-mediated transcription repression, and L7Ae-mediated translation repression. Our data shows simple regulatory elements for use in mammalian cells can be quickly prototyped in a CFPS model system.

Systems and synthetic biology tools for advanced bioproduction hosts

The genomic revolution ushered in an era of discovery and characterization of enzymes from novel organisms that fueled engineering of microbes to produce commodity and high-value compounds. Over the past decade advances in synthetic biology tools in recent years contributed to significant progress in metabolic engineering efforts to produce both biofuels and bioproducts resulting in several such related items being brought to market. These successes represent a burgeoning bio-economy; however, significant resources and time are still necessary to progress a system from proof-of-concept to market. In order to fully realize this potential, methods that examine biological systems in a comprehensive, systematic and high-throughput manner are essential. Recent success in synthetic biology has coincided with the development of systems biology and analytical approaches that kept pace and scaled with technology development. Here, we review a selection of systems biology methods and their use in synthetic biology approaches for microbial biotechnology platforms.

Advanced strategy for metabolite exploration in filamentous fungi

Filamentous fungi comprise an abundance of gene clusters that encode high-value metabolites, whereas affluent gene clusters remain silent during laboratory conditions. Complex cellular metabolism further limits these metabolite yields. Therefore, diverse strategies such as genetic engineering and chemical mutagenesis have been developed to activate these cryptic pathways and improve metabolite productivity. However, lower efficiencies of gene modifications and screen tools delayed the above processes. To address the above issues, this review describes an alternative design-construction evaluation optimization (DCEO) approach. The DCEO tool provides theoretical and practical principles to identify potential pathways, modify endogenous pathways, integrate exogenous pathways, and exploit novel pathways for their diverse metabolites and desirable productivities. This DCEO method also offers different tactics to balance the cellular metabolisms, facilitate the genetic engineering, and exploit the scalable metabolites in filamentous fungi.

Multi-functional genome-wide CRISPR system for high throughput genotype-phenotype mapping

Genome-scale engineering is an indispensable tool to understand genome functions due to our limited knowledge of cellular networks. Unfortunately, most existing methods for genome-wide genotype–phenotype mapping are limited to a single mode of genomic alteration, i.e. overexpression, repression, or deletion. Here we report a multi-functional genome-wide CRISPR (MAGIC) system to precisely control the expression level of defined genes to desired levels throughout the whole genome. By combining the tri-functional CRISPR system and array-synthesized oligo pools, MAGIC is used to create, to the best of our knowledge, one of the most comprehensive and diversified genomic libraries in yeast ever reported. The power of MAGIC is demonstrated by the identification of previously uncharacterized genetic determinants of complex phenotypes, particularly those having synergistic interactions when perturbed to different expression levels. MAGIC represents a powerful synthetic biology tool to investigate fundamental biological questions as well as engineer complex phenotypes for biotechnological applications.

Genome-scale engineering is generally limited to single methods of alteration such as overexpression, repression or deletion. Here the authors present a tri-functional CRISPR system that can engineer complex synergistic interactions in a genome-wide manner.

Towards a fully automated algorithm driven platform for biosystems design

Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms.

Existing efforts have been focused on one of the elements in the automation of the design, build, test, and learn (DBTL) cycle for biosystems design. Here, the authors integrate a robotic system with machine learning algorithms to fully automate the DBTL cycle and apply it in optimizing the lycopene biosynthetic pathway.

Synthetic biology and healthcare

Through the application of the engineering paradigm of ‘design–build–test–learn’ allied to recent advances in DNA sequencing, bioinformatics and, critically, the falling cost of DNA synthesis, Synthetic Biology promises to make existing therapies more accessible and be at the centre of the development of new types of advanced therapies. As existing pharmaceutical companies integrate Synthetic Biology tools into their normal ways of working, existing products are being produced by cheaper and more sustainable methods. Vaccine design and production is becoming driven by the molecular design allied to rapidly scalable production methods to combat the threat of pandemics and the ability of pathogens to escape the immune system by mutation. Advanced therapies, such as chimeric antigen receptor T cell therapy, are able to capitalise on the tools of Synthetic Biology to design new proteins and molecular ‘kill switches’ as well as design scalable and effective vectors for cellular transduction. This review highlights how Synthetic Biology is having an impact across the various therapeutic modalities from existing products to new therapies.

From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

Harnessing the process of natural selection to obtain and understand new microbial phenotypes has become increasingly possible due to advances in culturing techniques, DNA sequencing, bioinformatics, and genetic engineering. Accordingly, Adaptive Laboratory Evolution (ALE) experiments represent a powerful approach both to investigate the evolutionary forces influencing strain phenotypes, performance, and stability, and to acquire production strains that contain beneficial mutations. In this review, we summarize and categorize the applications of ALE to various aspects of microbial physiology pertinent to industrial bioproduction by collecting case studies that highlight the multitude of ways in which evolution can facilitate the strain construction process. Further, we discuss principles that inform experimental design, complementary approaches such as computational modeling that help maximize utility, and the future of ALE as an efficient strain design and build tool driven by growing adoption and improvements in automation.

A seamless and iterative DNA assembly method named PS-Brick and its assisted metabolic engineering for threonine and 1-propanol production

BACKGROUND

DNA assembly is an essential technique enabling metabolic engineering and synthetic biology. Combining novel DNA assembly technologies with rational metabolic engineering can facilitate the construction of microbial cell factories. Amino acids and derived biochemicals are important products in industrial biotechnology with wide application and huge markets. DNA assembly scenarios encountered in metabolic engineering for the construction of amino acid and related compound producers, such as design-build-test-learn cycles, construction of precise genetic circuits and repetitive DNA molecules, usually require for iterative, scarless and repetitive sequence assembly methods, respectively.

RESULTS

Restriction endonuclease (RE)-assisted strategies constitute one of the major categories of DNA assembly. Here, we developed a Type IIP and IIS RE-assisted method named PS-Brick that comprehensively takes advantage of the properties of PCR fragments and REs for iterative, seamless and repetitive sequence assembly. One round of PS-Brick reaction using purified plasmids and PCR fragments was accomplished within several hours, and transformation of the resultant reaction product from this PS-Brick assembly reaction exhibited high efficiency (104-105 CFUs/µg DNA) and high accuracy (~ 90%). An application of metabolic engineering to threonine production, including the release of feedback regulation, elimination of metabolic bottlenecks, intensification of threonine export and inactivation of threonine catabolism, was stepwise resolved in E. coli by rounds of "design-build-test-learn" cycles through the iterative PS-Brick paradigm, and 45.71 g/L threonine was obtained through fed-batch fermentation. In addition to the value of the iterative character of PS-Brick for sequential strain engineering, seamless cloning enabled precise in-frame fusion for codon saturation mutagenesis and bicistronic design, and the repetitive sequence cloning ability of PS-Brick enabled construction of tandem CRISPR sgRNA arrays for genome editing. Moreover, the heterologous pathway deriving 1-propanol pathway from threonine, composed of Lactococcus lactis kivD and Saccharomyces cerevisiae ADH2, was assembled by one cycle of PS-Brick, resulting in 1.35 g/L 1-propanol in fed-batch fermentation.

CONCLUSIONS

To the best of our knowledge, the PS-Brick framework is the first RE-assisted DNA assembly method using the strengths of both Type IIP and IIS REs. In this study, PS-Brick was demonstrated to be an efficient DNA assembly method for pathway construction and genome editing and was successfully applied in design-build-test-learn (DBTL) cycles of metabolic engineering for the production of threonine and threonine-derived 1-propanol. The PS-Brick presents a valuable addition to the current toolbox of synthetic biology and metabolic engineering.

Synthetic biology strategies for microbial biosynthesis of plant natural products

Metabolic engineers endeavor to create a bio-based manufacturing industry using microbes to produce fuels, chemicals, and medicines. Plant natural products (PNPs) are historically challenging to produce and are ubiquitous in medicines, flavors, and fragrances. Engineering PNP pathways into new hosts requires finding or modifying a suitable host to accommodate the pathway, planning and implementing a biosynthetic route to the compound, and discovering or engineering enzymes for missing steps. In this review, we describe recent developments in metabolic engineering at the level of host, pathway, and enzyme, and discuss how the field is approaching ever more complex biosynthetic opportunities.

Engineering Plant Secondary Metabolism in Microbial Systems

An overview of common challenges and strategies underlying efforts to reconstruct plant isoprenoid, alkaloid, phenylpropanoid, and polyketide biosynthetic pathways in microbial systems.

Improving Reproducibility in Synthetic Biology

Synthetic biology holds great promise to deliver transformative technologies to the world in the coming years. However, several challenges still remain to be addressed before it can deliver on its promises. One of the most important issues to address is the lack of reproducibility within research of the life sciences. This problem is beginning to be recognised by the community and solutions are being developed to tackle the problem. The recent emergence of automated facilities that are open for use by researchers (such as biofoundries and cloud labs) may be one of the ways that synthetic biologists can improve the quality and reproducibility of their work. In this perspective article, we outline these and some of the other technologies that are currently being developed which we believe may help to transform how synthetic biologists approach their research activities.

Utilising datasheets for the informed automated design and build of a synthetic metabolic pathway

BACKGROUND

The automation of modular cloning methodologies permits the assembly of many genetic designs. Utilising characterised biological parts aids in the design and redesign of genetic pathways. The characterisation information held on datasheets can be used to determine whether a biological part meets the design requirements. To manage the design of genetic pathways, researchers have turned to modelling-based computer aided design software tools.

RESULT

An automated workflow has been developed for the design and build of heterologous metabolic pathways. In addition, to demonstrate the powers of electronic datasheets we have developed software which can transfer part information from a datasheet to the Design of Experiment software JMP. To this end we were able to use Design of Experiment software to rationally design and test randomised samples from the design space of a lycopene pathway in E. coli. This pathway was optimised by individually modulating the promoter strength, RBS strength, and gene order targets.

CONCLUSION

The use of standardised and characterised biological parts will empower a design-oriented synthetic biology for the forward engineering of heterologous expression systems. A Design of Experiment approach streamlines the design-build-test cycle to achieve optimised solutions in biodesign. Developed automated workflows provide effective transfer of information between characterised information (in the form of datasheets) and DoE software.

Discovery and Engineering of Cytochrome P450s for Terpenoid Biosynthesis

Terpenoids represent 60% of known natural products, including many drugs and drug candidates, and their biosynthesis is attracting great interest. However, the unknown cytochrome P450s (CYPs) in terpenoid biosynthetic pathways make the heterologous production of related terpenoids impossible, while the slow kinetics of some known CYPs greatly limit the efficiency of terpenoid biosynthesis. Thus, there is a compelling need to discover and engineer CYPs for terpenoid biosynthesis to fully realize their great potential for industrial application. This review article summarizes the current state of CYP discovery and engineering in terpenoid biosynthesis, focusing on recent synthetic biology approaches toward prototyping CYPs in heterologous hosts. We also propose several strategies for further accelerating CYP discovery and engineering.

Blueprints for Biosensors: Design, Limitations, and Applications

Biosensors are enabling major advances in the field of analytics that are both facilitating and being facilitated by advances in synthetic biology. The ability of biosensors to rapidly and specifically detect a wide range of molecules makes them highly relevant to a range of industrial, medical, ecological, and scientific applications. Approaches to biosensor design are as diverse as their applications, with major biosensor classes including nucleic acids, proteins, and transcription factors. Each of these biosensor types has advantages and limitations based on the intended application, and the parameters that are required for optimal performance. Specifically, the choice of biosensor design must consider factors such as the ligand specificity, sensitivity, dynamic range, functional range, mode of output, time of activation, ease of use, and ease of engineering. This review discusses the rationale for designing the major classes of biosensor in the context of their limitations and assesses their suitability to different areas of biotechnological application.

The Smell of Synthetic Biology: Engineering Strategies for Aroma Compound Production in Yeast

Yeast—especially Saccharomyces cerevisiae—have long been a preferred workhorse for the production of numerous recombinant proteins and other metabolites. S. cerevisiae is a noteworthy aroma compound producer and has also been exploited to produce foreign bioflavour compounds. In the past few years, important strides have been made in unlocking the key elements in the biochemical pathways involved in the production of many aroma compounds. The expression of these biochemical pathways in yeast often involves the manipulation of the host strain to direct the flux towards certain precursors needed for the production of the given aroma compound. This review highlights recent advances in the bioengineering of yeast—including S. cerevisiae—to produce aroma compounds and bioflavours. To capitalise on recent advances in synthetic yeast genomics, this review presents yeast as a significant producer of bioflavours in a fresh context and proposes new directions for combining engineering and biology principles to improve the yield of targeted aroma compounds.

The Multiplanetary Future of Plant Synthetic Biology

The interest in human space journeys to distant planets and moons has been re-ignited in recent times and there are ongoing plans for sending the first manned missions to Mars in the near future. In addition to generating oxygen, fixing carbon, and recycling waste and water, plants could play a critical role in producing food and biomass feedstock for the microbial manufacture of materials, chemicals, and medicines in long-term interplanetary outposts. However, because life on Earth evolved under the conditions of the terrestrial biosphere, plants will not perform optimally in different planetary habitats. The construction or transportation of plant growth facilities and the availability of resources, such as sunlight and liquid water, may also be limiting factors, and would thus impose additional challenges to efficient farming in an extraterrestrial destination. Using the framework of the forthcoming human missions to Mars, here we discuss a series of bioengineering endeavors that will enable us to take full advantage of plants in the context of a Martian greenhouse. We also propose a roadmap for research on adapting life to Mars and outline our opinion that synthetic biology efforts towards this goal will contribute to solving some of the main agricultural and industrial challenges here on Earth.

Applications of Yeast Synthetic Biology Geared towards the Production of Biopharmaceuticals

Engineered yeast are an important production platform for the biosynthesis of high-value compounds with medical applications. Recent years have witnessed several new developments in this area, largely spurred by advances in the field of synthetic biology and the elucidation of natural metabolic pathways. This minireview presents an overview of synthetic biology applications for the heterologous biosynthesis of biopharmaceuticals in yeast and demonstrates the power and potential of yeast cell factories by highlighting several recent examples. In addition, an outline of emerging trends in this rapidly-developing area is discussed, hinting upon the potential state-of-the-art in the years ahead.

Emerging Opportunities for Synthetic Biology in Agriculture

Rapid expansion in the emerging field of synthetic biology has to date mainly focused on the microbial sciences and human health. However, the zeitgeist is that synthetic biology will also shortly deliver major outcomes for agriculture. The primary industries of agriculture, fisheries and forestry, face significant and global challenges; addressing them will be assisted by the sector’s strong history of early adoption of transformative innovation, such as the genetic technologies that underlie synthetic biology. The implementation of synthetic biology within agriculture may, however, be hampered given the industry is dominated by higher plants and mammals, where large and often polyploid genomes and the lack of adequate tools challenge the ability to deliver outcomes in the short term. However, synthetic biology is a rapidly growing field, new techniques in genome design and synthesis, and more efficient molecular tools such as CRISPR/Cas9 may harbor opportunities more broadly than the development of new cultivars and breeds. In particular, the ability to use synthetic biology to engineer biosensors, synthetic speciation, microbial metabolic engineering, mammalian multiplexed CRISPR, novel anti microbials, and projects such as Yeast 2.0 all have significant potential to deliver transformative changes to agriculture in the short, medium and longer term. Specifically, synthetic biology promises to deliver benefits that increase productivity and sustainability across primary industries, underpinning the industry’s prosperity in the face of global challenges.

An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals

The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design–Build-Test–Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2S)-pinocembrin in Escherichia coli, to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L⁻¹. The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest.

Pablo Carbonell et al. present an automated pipeline for the discovery and optimization of biosynthetic pathways for microbial production of fine chemicals. They apply their pipeline to the production of the flavonoid (2S)-pinocembrin in Escherichia coli and show improvement of the pathway by 500-fold.

A High-throughput workflow for CRISPR/Cas9 mediated combinatorial promoter replacements and phenotype characterization in yeast

Due to the rapidly increasing sequence information on gene variants generated by evolution and our improved abilities to engineer novel biological activities, microbial cells can be evolved for the production of a growing spectrum of compounds. For high productivity, efficient carbon channeling towards the end product is a key element. In large scale production systems the genetic modifications that ensure optimal performance cannot be dependent on plasmid-based regulators, but need to be engineered stably into the host genome. Here we describe a CRISPR/Cas9 mediated high-throughput workflow for combinatorial and multiplexed replacement of native promoters with synthetic promoters and the following high-throughput phenotype characterization in the yeast Saccharomyces cerevisiae. The workflow is demonstrated with three central metabolic genes, ZWF1, PGI1 and TKL1 encoding a glucose-6-phosphate dehydrogenase, phosphoglucose isomerase and transketolase, respectively. The synthetic promoter donor DNA libraries were generated by PCR and transformed to yeast cells. A 50% efficiency was achieved for simultaneous replacement at three individual loci using short 60-bp flanking homology sequences in the donor promoters. Phenotypic strain characterization was validated and demonstrated using liquid handling automation and 150 µl cultivation volume in 96-well plate format. The established workflow offers a robust platform for automated engineering and improvement of yeast strains.

Advancing Metabolic Engineering of Saccharomyces cerevisiae Using the CRISPR/Cas System

Thanks to its ease of use, modularity, and scalability, the clustered regularly interspaced short palindromic repeats (CRISPR) system has been increasingly used in the design and engineering of Saccharomyces cerevisiae, one of the most popular hosts for industrial biotechnology. This review summarizes the recent development of this disruptive technology for metabolic engineering applications, including CRISPR-mediated gene knock-out and knock-in as well as transcriptional activation and interference. More importantly, multi-functional CRISPR systems that combine both gain- and loss-of-function modulations for combinatorial metabolic engineering are highlighted.