An engineering biology approach to automated workflow and biodesign.

The paper addresses the application of engineering biology strategies and techniques to the automation of laboratory workflow-primarily in the context of biofoundries and biodesign applications based on the Design, Build, Test and Learn paradigm. The trend toward greater automation comes with its own set of challenges. On the one hand, automation is associated with higher throughput and higher replicability. On the other hand, the implementation of an automated workflow requires an instruction set that is far more extensive than that required for a manual workflow. Automated tasks must also be conducted in the order specified in the workflow, with the right logic, utilizing suitable biofoundry resources, and at scale-while simultaneously collecting measurements and associated data. The paper describes an approach to an automated workflow that is being trialed at the London Biofoundry at SynbiCITE. The solution represents workflows with directed graphs, uses orchestrators for their execution, and relies on existing standards. The approach is highly flexible and applies to not only workflow automation in single locations but also distributed workflows (e.g. for biomanufacturing). The final section presents an overview of the implementation-using the simple example of an assay based on a dilution, measurement, and data analysis workflow.

Machine Learning-Guided Optimization of p-Coumaric Acid Production in Yeast.

Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a biobased economy. To use them effectively, appropriate connections between the phases of the cycle are crucial. Using p-coumaric acid (pCA) production in Saccharomyces cerevisiae as a case study, we propose the use of one-pot library generation, random screening, targeted sequencing, and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of the ML models strongly enable pathway optimization and propose feature importance and Shapley additive explanation values as a guide to expand the design space of original libraries. This approach allowed a 68% increased production of pCA within two DBTL cycles, leading to a 0.52 g/L titer and a 0.03 g/g yield on glucose.

Investigating and Modeling the Factors That Affect Genetic Circuit Performance.

Over the past 2 decades, synthetic biology has yielded ever more complex genetic circuits that are able to perform sophisticated functions in response to specific signals. Yet, genetic circuits are not immediately transferable to an outside-the-lab setting where their performance is highly compromised. We propose introducing a broader test step to the design-build-test-learn workflow to include factors that might contribute to unexpected genetic circuit performance. As a proof of concept, we have designed and evaluated a genetic circuit in various temperatures, inducer concentrations, nonsterilized soil exposure, and bacterial growth stages. We determined that the circuit's performance is dramatically altered when these factors differ from the optimal lab conditions. We observed significant changes in the time for signal detection as well as signal intensity when the genetic circuit was tested under nonoptimal lab conditions. As a learning effort, we then proceeded to generate model predictions in untested conditions, which is currently lacking in synthetic biology application design. Furthermore, broader test and learn steps uncovered a negative correlation between the time it takes for a gate to turn ON and the bacterial growth phases. As the synthetic biology discipline transitions from proof-of-concept genetic programs to appropriate and safe application implementations, more emphasis on test and learn steps (i.e., characterizing parts and circuits for a broad range of conditions) will provide missing insights on genetic circuit behavior outside the lab.

Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering.

Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.

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.

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

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

Harnessing synthetic biology for sustainable biomining with Fe/S-oxidizing microbes.

Biomining is a biotechnological approach where microorganisms are used to recover metals from ores and waste materials. While biomining applications are motivated by critical issues related to the climate crisis (e.g., habitat destruction due to mine effluent pollution, metal supply chains, increasing demands for cleantech-critical metals), its drawbacks hinder its widespread commercial applications: lengthy processing times, low recovery, and metal selectivity. Advances in synthetic biology provide an opportunity to engineer iron/sulfur-oxidizing microbes to address these limitations. In this forum, we review recent progress in synthetic biology-enhanced biomining with iron/sulfur-oxidizing microbes and delineate future research avenues.

The bright frontiers of microbial metabolic optogenetics.

In recent years, light-responsive systems from the field of optogenetics have been applied to several areas of metabolic engineering with remarkable success. By taking advantage of light's high tunability, reversibility, and orthogonality to host endogenous processes, optogenetic systems have enabled unprecedented dynamical controls of microbial fermentations for chemical production, metabolic flux analysis, and population compositions in co-cultures. In this article, we share our opinions on the current state of this new field of metabolic optogenetics.We make the case that it will continue to impact metabolic engineering in increasingly new directions, with the potential to challenge existing paradigms for metabolic pathway and strain optimization as well as bioreactor operation.

Merging automation and fundamental discovery into the design-build-test-learn cycle of nontraditional microbes.

Major advances toward a bio-based industry enabled cost-efficient bioproduction of multiple chemicals, yet successful industrial processes are relatively scarce and limited to the use of few workhorse microbes as hosts. An in-depth understanding of the physiology and metabolism of nontraditional microorganisms is key to unleash their biotechnological potential. The inception of biofoundries multiplied the capacity of constructing and testing a large number of microbial strains tailored for bioproduction - and we argue that automation workflows therein can be adapted to gain fundamental knowledge of nontraditional hosts. Here, we propose a 'metabolism-centric' approach to the design-build-test-learn cycle of synthetic biology, supported by multi-omic analyses, to facilitate the deployment of microbial cell factories designed for bioproduction beyond the typical landscape of target products.

Synthetic Biology Advanced Natural Product Discovery.

A wide variety of bacteria, fungi and plants can produce bioactive secondary metabolites, which are often referred to as natural products. With the rapid development of DNA sequencing technology and bioinformatics, a large number of putative biosynthetic gene clusters have been reported. However, only a limited number of natural products have been discovered, as most biosynthetic gene clusters are not expressed or are expressed at extremely low levels under conventional laboratory conditions. With the rapid development of synthetic biology, advanced genome mining and engineering strategies have been reported and they provide new opportunities for discovery of natural products. This review discusses advances in recent years that can accelerate the design, build, test, and learn (DBTL) cycle of natural product discovery, and prospects trends and key challenges for future research directions.

History-Driven Genetic Modification Design Technique Using a Domain-Specific Lexical Model for the Acceleration of DBTL Cycles for Microbial Cell Factories.

The development of microbes for conducting bioprocessing via synthetic biology involves design-build-test-learn (DBTL) cycles. To aid the designing step, we developed a computational technique that suggests next genetic modifications on the basis of relatedness to the user's design history of genetic modifications accumulated through former DBTL cycles conducted by the user. This technique, which comprehensively retrieves well-known designs related to the history, involves searching text for previous literature and then mining genes that frequently co-occur in the literature with those modified genes. We further developed a domain-specific lexical model that weights literature that is more related to the domain of metabolic engineering to emphasize genes modified for bioprocessing. Our technique made a suggestion by using a history of creating a Corynebacterium glutamicum strain producing shikimic acid that had 18 genetic modifications. Inspired by the suggestion, eight genes were considered by biologists for further modification, and modifying four of these genes proved experimentally efficient in increasing the production of shikimic acid. These results indicated that our proposed technique successfully utilized the former cycles to suggest relevant designs that biologists considered worth testing. Comprehensive retrieval of well-tested designs will help less-experienced researchers overcome the entry barrier as well as inspire experienced researchers to formulate design concepts that have been overlooked or suspended. This technique will aid DBTL cycles by feeding histories back to the next genetic design, thereby complementing the designing step.

The Design-Build-Test-Learn cycle for metabolic engineering of Streptomycetes.

Streptomycetes are producers of a wide range of specialized metabolites of great medicinal and industrial importance, such as antibiotics, antifungals, or pesticides. Having been the drivers of the golden age of antibiotics in the 1950s and 1960s, technological advancements over the last two decades have revealed that very little of their biosynthetic potential has been exploited so far. Given the great need for new antibiotics due to the emerging antimicrobial resistance crisis, as well as the urgent need for sustainable biobased production of complex molecules, there is a great renewed interest in exploring and engineering the biosynthetic potential of streptomycetes. Here, we describe the Design-Build-Test-Learn (DBTL) cycle for metabolic engineering experiments in streptomycetes and how it can be used for the discovery and production of novel specialized metabolites.

Model reduction of genome-scale metabolic models as a basis for targeted kinetic models.

Constraint-based, genome-scale metabolic models are an essential tool to guide metabolic engineering. However, they lack the detail and time dimension that kinetic models with enzyme dynamics offer. Model reduction can be used to bridge the gap between the two methods and allow for the integration of kinetic models into the Design-Built-Test-Learn cycle. Here we show that these reduced size models can be representative of the dynamics of the original model and demonstrate the automated generation and parameterisation of such models. Using these minimal models of metabolism could allow for further exploration of dynamic responses in metabolic networks.

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-ß-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.

Streamlining the Analysis of Dynamic 13C-Labeling Patterns for the Metabolic Engineering of Corynebacterium glutamicum as l-Histidine Production Host.

Today's possibilities of genome editing easily create plentitudes of strain mutants that need to be experimentally qualified for configuring the next steps of strain engineering. The application of design-build-test-learn cycles requires the identification of distinct metabolic engineering targets as design inputs for subsequent optimization rounds. Here, we present the pool influx kinetics (PIK) approach that identifies promising metabolic engineering targets by pairwise comparison of up- and downstream 13C labeling dynamics with respect to a metabolite of interest. Showcasing the complex l-histidine production with engineered Corynebacterium glutamicuml-histidine-on-glucose yields could be improved to 8.6 ± 0.1 mol% by PIK analysis, starting from a base strain. Amplification of purA, purB, purH, and formyl recycling was identified as key targets only analyzing the signal transduction kinetics mirrored in the PIK values.

Dynamic Metabolomics for Engineering Biology: Accelerating Learning Cycles for Bioproduction.

Metabolomics is a powerful tool to rationally guide the metabolic engineering of synthetic bioproduction pathways. Current reports indicate great potential to further develop metabolomics-directed synthetic bioproduction. Advanced mass metabolomics methods including isotope flux analysis, untargeted metabolomics, and system-wide approaches are assisting the characterization of metabolic pathways and enabling the biosynthesis of more complex products. More importantly, a design, build, test, and learn (DBTL) cycle is accelerating synthetic biology research and is highly compatible with metabolomics data to further expand bioproduction capability. However, learning processes are currently the weakest link in this workflow. Therefore, guidelines for the development of metabolic learning processes are proposed based on bioproduction examples. Linking dynamic mass spectrometry (MS) methodologies together with automated learning workflows is encouraged.

Lessons from Two Design-Build-Test-Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning.

The Design-Build-Test-Learn (DBTL) cycle, facilitated by exponentially improving capabilities in synthetic biology, is an increasingly adopted metabolic engineering framework that represents a more systematic and efficient approach to strain development than historical efforts in biofuels and biobased products. Here, we report on implementation of two DBTL cycles to optimize 1-dodecanol production from glucose using 60 engineered Escherichia coli MG1655 strains. The first DBTL cycle employed a simple strategy to learn efficiently from a relatively small number of strains (36), wherein only the choice of ribosome-binding sites and an acyl-ACP/acyl-CoA reductase were modulated in a single pathway operon including genes encoding a thioesterase (UcFatB1), an acyl-ACP/acyl-CoA reductase (Maqu_2507, Maqu_2220, or Acr1), and an acyl-CoA synthetase (FadD). Measured variables included concentrations of dodecanol and all proteins in the engineered pathway. We used the data produced in the first DBTL cycle to train several machine-learning algorithms and to suggest protein profiles for the second DBTL cycle that would increase production. These strategies resulted in a 21% increase in dodecanol titer in Cycle 2 (up to 0.83 g/L, which is more than 6-fold greater than previously reported batch values for minimal medium). Beyond specific lessons learned about optimizing dodecanol titer in E. coli, this study had findings of broader relevance across synthetic biology applications, such as the importance of sequencing checks on plasmids in production strains as well as in cloning strains, and the critical need for more accurate protein expression predictive tools.