Fully Automated One-Step Synthesis of Single-Transcript TALEN Pairs Using a Biological Foundry

AutoBioTech─A Versatile Biofoundry for Automated Strain Engineering

The inevitable transition from petrochemical production processes to renewable alternatives has sparked the emergence of biofoundries in recent years. Manual engineering of microbes will not be sufficient to meet the ever-increasing demand for novel producer strains. Here we describe the AutoBioTech platform, a fully automated laboratory system with 14 devices to perform operations for strain construction without human interaction. Using modular workflows, this platform enables automated transformations of Escherichia coli with plasmids assembled via modular cloning. A CRISPR/Cas9 toolbox compatible with existing modular cloning frameworks allows automated and flexible genome editing of E. coli. In addition, novel workflows have been established for the fully automated transformation of the Gram-positive model organism Corynebacterium glutamicum by conjugation and electroporation, with the latter proving to be the more robust technique. Overall, the AutoBioTech platform excels at versatility due to the modularity of workflows and seamless transitions between modules. This will accelerate strain engineering of Gram-negative and Gram-positive bacteria.

Automated high-throughput DNA synthesis and assembly

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

Physical Laboratory Automation in Synthetic Biology

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

Designing gene manipulation schedules for high throughput parallel construction of objective strains

Recent advances in biofoundries have enabled the construction of a large quantity of strains in parallel, accelerating the design-build-test-learn (DBTL) cycles for strain development. However, the construction of a large number of strains by iterative gene manipulation is still time-consuming and costly, posing a challenge for the development of commercial strains. Common gene manipulations among different objective strains open up the possibility of reducing cost and time for strain construction in biofoundries by optimizing genetic manipulation schedules. A method is introduced consisting of two complementary algorithms for designing optimal parent-children manipulation schedules for strain construction: greedy search of common ancestor strains (GSCAS) and minimizing total manipulations (MTM). By reusing common ancestor strains, the number of strains to be constructed can be effectively reduced, resulting in a tree-like structure of descendants instead of linear lineages for each strain. The GSCAS algorithm can quickly find common ancestor strains and clusters them together based on their genetic makeup, and the MTM algorithm subsequently minimize the genetic manipulations required, resulting in a further reduction in the total number of genetic manipulations. The effectiveness of our method is demonstrated through a case study of 94 target strains, where GSCAS reduces an average of 36% of the total gene manipulations, and MTM reduces an additional 10%. The performance of both algorithms is robust among case studies with different average occurrences of gene manipulations across objective strains. Our method potentially improves cost efficiency and accelerate the development of commercial strains significantly. The implementation of the methods can be freely accessed via https://gscas-mtm.biodesign.ac.cn/.

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.

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

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

Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis

The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.

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.

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

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

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.

A scalable platform to discover antimicrobials of ribosomal origin

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a promising source of new antimicrobials in the face of rising antibiotic resistance. Here, we report a scalable platform that combines high-throughput bioinformatics with automated biosynthetic gene cluster refactoring for rapid evaluation of uncharacterized gene clusters. As a proof of concept, 96 RiPP gene clusters that originate from diverse bacterial phyla involving 383 biosynthetic genes are refactored in a high-throughput manner using a biological foundry with a success rate of 86%. Heterologous expression of all successfully refactored gene clusters in Escherichia coli enables the discovery of 30 compounds covering six RiPP classes: lanthipeptides, lasso peptides, graspetides, glycocins, linear azol(in)e-containing peptides, and thioamitides. A subset of the discovered lanthipeptides exhibit antibiotic activity, with one class II lanthipeptide showing low µM activity against Klebsiella pneumoniae, an ESKAPE pathogen. Overall, this work provides a robust platform for rapidly discovering RiPPs.

Recent Advances, Challenges and Metabolic Engineering Strategies in the Biosynthesis of 3-Hydroxypropionic Acid

As an attractive and valuable platform chemical, 3-hydroxypropionic acid (3-HP) can be used to produce a variety of industrially important commodity chemicals and biodegradable polymers. Moreover, the biosynthesis of 3-HP has drawn much attention in recent years due to its sustainability and environmental friendliness. Here, we focus on recent advances, challenges and metabolic engineering strategies in the biosynthesis of 3-HP. While glucose and glycerol are major carbon sources for its production of 3-HP via microbial fermentation, other carbon sources have also been explored. To increase yield and titer, synthetic biology and metabolic engineering strategies have been explored, including modifying pathway enzymes, eliminating flux blockages due to byproduct synthesis, eliminating toxic byproducts, and optimizing via genome-scale models. This review also provides insights on future directions for 3-HP biosynthesis. This article is protected by copyright. All rights reserved.

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

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

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

Genomic markers on synthetic genomes

Genome synthesis endows scientists the ability of de novo creating genomes absent in nature, by thorough redesigning DNA sequences and introducing numerous custom features. However, the genome synthesis is a labor- and time-consuming work, and thus it is a challenge to verify and quantify the synthetic genome rapidly and precisely. Thus, specific DNA sequences different from native genomic sequences are designed into synthetic genomes during synthesis, namely genomic markers. Genomic markers can be easily detected by PCR reaction, whole-genome sequencing (WGS) and a variety of methods to identify the synthetic genome from native one. Here, we review types and applications of genomic markers utilized in synthetic genomes, with the hope of providing a guidance for future works.

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.

Debugging: putting the synthetic yeast chromosome to work

Synthetic genomics aims to de novo synthesize a functional genome redesigned from natural sequences with custom features. Designed genomes provide new toolkits for better understanding organisms, evolution and the construction of cellular factories. Currently maintaining the fitness of cells with synthetic genomes is particularly challenging as defective designs and unanticipated assembly errors frequently occur. Mapping and correcting bugs that arise during the synthetic process are imperative for the successful construction of a synthetic genome that can sustain a desired cellular function. Here, we review recently developed methods used to map and fix various bugs which arise during yeast genome synthesis with the hope of providing guidance for putting the synthetic yeast chromosome to work.

TALEN outperforms Cas9 in editing heterochromatin target sites

Genome editing critically relies on selective recognition of target sites. However, despite recent progress, the underlying search mechanism of genome-editing proteins is not fully understood in the context of cellular chromatin environments. Here, we use single-molecule imaging in live cells to directly study the behavior of CRISPR/Cas9 and TALEN. Our single-molecule imaging of genome-editing proteins reveals that Cas9 is less efficient in heterochromatin than TALEN because Cas9 becomes encumbered by local searches on non-specific sites in these regions. We find up to a fivefold increase in editing efficiency for TALEN compared to Cas9 in heterochromatin regions. Overall, our results show that Cas9 and TALEN use a combination of 3-D and local searches to identify target sites, and the nanoscopic granularity of local search determines the editing outcomes of the genome-editing proteins. Taken together, our results suggest that TALEN is a more efficient gene-editing tool than Cas9 for applications in heterochromatin.

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.

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.

Combinatorial metabolic pathway assembly approaches and toolkits for modular assembly

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

Is microfluidics the "assembly line" for CRISPR-Cas9 gene-editing?

Acclaimed as one of the biggest scientific breakthroughs, the technology of CRISPR has brought significant improvement in the biotechnological spectrum-from editing genetic defects in diseases for gene therapy to modifying organisms for the production of biofuels. Since its inception, the CRISPR-Cas9 system has become easier and more versatile to use. Many variants have been found, giving the CRISPR toolkit a great range that includes the activation and repression of genes aside from the previously known knockout and knockin of genes. Here, in this Perspective, we describe efforts on automating the gene-editing workflow, with particular emphasis given on the use of microfluidic technology. We discuss how automation can address the limitations of gene-editing and how the marriage between microfluidics and gene-editing will expand the application space of CRISPR.

A Library of Tunable, Portable, and Inducer-Free Promoters Derived from Cyanobacteria

Cyanobacteria are emerging as hosts for various biotechnological applications. The ability to engineer these photosynthetic prokaryotes greatly depends on the availability of well-characterized promoters. Inducer-free promoters of a range of activities may be desirable for the eventual large-scale, outdoor cultivations. Further, several native promoters of cyanobacteria are repressed by high carbon dioxide or light, and it would be of interest to alter this property. We started with P_rbcL and P_cpcB, the well-characterized native promoters of the model cyanobacterium Synechococcus elongatus PCC 7942, found upstream of the two abundantly expressed genes, Ribulose-1,5-Bisphosphate Carboxylase/Oxygenase, and phycocyanin β-1 subunit, respectively. The library of 48 promoters created via error-prone PCR of these 300-bp-long native promoters showed 2 orders of magnitude dynamic range with activities that were both lower and higher than those of the wild-type promoters. A few mutants of the P_rbcL showed greater strength than P_cpcB, which is widely considered a superstrong promoter. A number of mutant promoters did not show repression by high CO₂ or light, typically found for P_rbcL and P_cpcB, respectively. Further, the wild-type and mutant promoters showed comparable activities in the fast-growing and stress-tolerant strains S. elongatus PCC 11801 and PCC 11802, suggesting that the library can be used in different cyanobacteria. Interestingly, the majority of the promoters showed strong expression in E. coli, thus adding to the repertoire of inducer-free promoters for this heterotrophic workhorse. Our results have implications in the metabolic engineering of cyanobacteria and E. coli.

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

Multi-part DNA assembly is the physical starting point for many projects in Synthetic and Molecular Biology. The ability to explore a genetic design space by building extensive libraries of DNA constructs is essential for creating programmed biological systems. With multiple DNA assembly methods and standards adopted in the Synthetic Biology community, automation of the DNA assembly process is now receiving serious attention. Automation will enable larger builds using less researcher time, while increasing the accessible design space. However, these benefits currently incur high costs for both equipment and consumables. Here, we address this limitation by introducing low-cost DNA assembly with BASIC on OpenTrons (DNA-BOT). For this purpose, we developed an open-source software package and demonstrated the performance of DNA-BOT by simultaneously assembling 88 constructs composed of 10 genetic parts, evaluating the promoter, ribosome binding site and gene order design space for a three-gene operon. All 88 constructs were assembled with high accuracy, at a consumables cost of $1.50–$5.50 per construct. This illustrates the efficiency, accuracy and affordability of DNA-BOT, making it accessible for most labs and democratizing automated DNA assembly.

Yeasts as probiotics: Mechanisms, outcomes, and future potential

The presence of commensal fungal species in the human gut indicates that organisms from this kingdom have the potential to benefit the host as well. Saccharomyces boulardii, a yeast strain isolated about a hundred years ago, is the most well-characterized probiotic yeast. Though for the most part it genetically resembles Saccharomyces cerevisiae, specific phenotypic differences make it better suited for the gut microenvironment such as better acid and heat tolerance. Several studies using animal hosts suggest that S. boulardii can be used as a biotherapeutic in humans. Clinical trials indicate that it can alleviate symptoms from gastrointestinal (GI) tract infections to some extent, but further trials are needed to understand the full therapeutic potential of S. boulardii. Improvement on probiotic function using engineered yeast is an attractive future direction, though genome modification tools for use in S. boulardii have been limited until recently. However, some tools available for S. cerevisiae should be applicable for S. boulardii as well. In this review, we summarize the observed probiotic effect of this yeast and the state of the art for genome engineering tools that could help enhance its probiotic properties.

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.

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.

Building a global alliance of biofoundries

Biofoundries provide an integrated infrastructure to enable the rapid design, construction, and testing of genetically reprogrammed organisms for biotechnology applications and research. Many biofoundries are being built and a Global Biofoundry Alliance has recently been established to coordinate activities worldwide.

The Use of an Automated Platform to Assemble Multigenic Constructs for Plant Transformation

Compared to traditional means, modern DNA assembly methods allow cloning of large, multigenic vectors for plant transformation in rapid fashion. These methods are often robust and efficient and can assemble multiple DNA fragments into a single vector in one reaction. Here we describe the use of an automated DNA assembly platform for the generation of complex, multigenic T-DNA binary vectors using a hierarchical Golden Gate cloning strategy. These DNA constructs contained diverse DNA elements for the expression of multiple genes for trait stacking in the crop of interest. This platform streamlines the DNA assembly and validation process through high-efficiency cloning methods, integrated automation equipment, and increased throughput. The implementation of this platform removes bottlenecks for routine molecular biology and opens new possibilities for downstream experimental idea testing.

Automated Planning Enables Complex Protocols on Liquid-Handling Robots

Robotic automation in synthetic biology is especially relevant for liquid handling to facilitate complex experiments. However, research tasks that are not highly standardized are still rarely automated in practice. Two main reasons for this are the substantial investments required to translate molecular biological protocols into robot programs, and the fact that the resulting programs are often too specific to be easily reused and shared. Recent developments of standardized protocols and dedicated programming languages for liquid-handling operations addressed some aspects of ease-of-use and portability of protocols. However, either they focus on simplicity, at the expense of enabling complex protocols, or they entail detailed programming, with corresponding skills and efforts required from the users. To reconcile these trade-offs, we developed Roboliq, a software system that uses artificial intelligence (AI) methods to integrate (i) generic formal, yet intuitive, protocol descriptions, (ii) complete, but usually hidden, programming capabilities, and (iii) user-system interactions to automatically generate executable, optimized robot programs. Roboliq also enables high-level specifications of complex tasks with conditional execution. To demonstrate the system's benefits for experiments that are difficult to perform manually because of their complexity, duration, or time-critical nature, we present three proof-of-principle applications for the reproducible, quantitative characterization of GFP variants.

Advances in analytical tools for high throughput strain engineering

The emergence of inexpensive, base-perfect genome editing is revolutionising biology. Modern industrial biotechnology exploits the advances in genome editing in combination with automation, analytics and data integration to build high-throughput automated strain engineering pipelines also known as biofoundries. Biofoundries replace the slow and inconsistent artisanal processes used to build microbial cell factories with an automated design-build-test cycle, considerably reducing the time needed to deliver commercially viable strains. Testing and hence learning remains relatively shallow, but recent advances in analytical chemistry promise to increase the depth of characterization possible. Analytics combined with models of cellular physiology in automated systems biology pipelines should enable deeper learning and hence a steeper pitch of the learning cycle. This review explores the progress, advances and remaining bottlenecks of analytical tools for high throughput strain engineering.

Engineering biological systems using automated biofoundries

Engineered biological systems such as genetic circuits and microbial cell factories have promised to solve many challenges in the modern society. However, the artisanal processes of research and development are slow, expensive, and inconsistent, representing a major obstacle in biotechnology and bioengineering. In recent years, biological foundries or biofoundries have been developed to automate design-build-test engineering cycles in an effort to accelerate these processes. This review summarizes the enabling technologies for such biofoundries as well as their early successes and remaining challenges.

Automated multiplex genome-scale engineering in yeast

Genome-scale engineering is indispensable in understanding and engineering microorganisms, but the current tools are mainly limited to bacterial systems. Here we report an automated platform for multiplex genome-scale engineering in Saccharomyces cerevisiae, an important eukaryotic model and widely used microbial cell factory. Standardized genetic parts encoding overexpression and knockdown mutations of >90% yeast genes are created in a single step from a full-length cDNA library. With the aid of CRISPR-Cas, these genetic parts are iteratively integrated into the repetitive genomic sequences in a modular manner using robotic automation. This system allows functional mapping and multiplex optimization on a genome scale for diverse phenotypes including cellulase expression, isobutanol production, glycerol utilization and acetic acid tolerance, and may greatly accelerate future genome-scale engineering endeavours in yeast.