Dynamic metabolic control: towards precision engineering of metabolism

Technological advancement spurs Komagataella phaffii as a next-generation platform for sustainable biomanufacturing

Biomanufacturing stands as a cornerstone of sustainable industrial development, necessitating a shift toward non-food carbon feedstocks to alleviate agricultural resource competition and advance a circular bioeconomy. Methanol, a renewable one‑carbon substrate, has emerged as a pivotal candidate due to its abundance, cost-effectiveness, and high reduction potential, further bolstered by breakthroughs in CO₂ hydrogenation-based synthesis. Capitalizing on this momentum, the methylotrophic yeast Komagataella phaffii has undergone transformative technological upgrades, evolving from a conventional protein expression workhorse into an intelligent bioproduction chassis. This paradigm shift is fundamentally driven by converging innovations across CRISPR-empowered advancement in genome editing and AI-powered metabolic pathway design in K. phaffii. The integration of CRISPR systems with droplet microfluidics high-throughput screening has redefined strain engineering efficiency, achieving much higher editing precision than traditional homologous recombination while compressing the "design-build-test-learn" cycle. Concurrently, machine learning-enhanced genome-scale metabolic models facilitate dynamic flux balancing, enabling simultaneous improvements in product titers, carbon yields, and volumetric productivity. Finally, technological advancement promotes the application of K. phaffii, including directing more efficiently metabolic flux toward nutrient products, and strengthening efficient synthesis of excreted proteins. As DNA synthesis automation and robotic experimentation platforms mature, next-generation breakthroughs in genome modification, cofactor engineering, and AI-guided autonomous evolution will further cement K. phaffii as a next-generation platform for decarbonizing global manufacturing paradigms. This technological trajectory positions methanol-based biomanufacturing as a cornerstone of the low-carbon circular economy.

Molecular process control for industrial biotechnology

The development of sustainable and economically competitive biotechnological processes is a central challenge of modern industrial biotechnology. Conventional strategies such as macroscopic and molecular bioprocess design are often insufficient to exploit their full potential. To circumvent this, molecular process control provides the missing link to further consolidate various optimization strategies to achieve multilayered process design. This review highlights the molecular mechanisms that can be exploited for molecular process control. These can either be endogenous or specifically implemented into the organism, and comprise regulatory mechanisms at the transcriptional, translational, and system levels. In addition to serving as a design tool to enhance existing bioprocesses, molecular process control is the gateway to biotechnological advances that will extend the boundaries of future process design.

ComQXPA Quorum Sensing Dynamic Regulation Enhanced Fengycin Production of Bacillus subtilis

Fengycin is an antifungal drug that could be used as a biocontrol agent if it could be produced in high amounts. The ComQXPA quorum sensing (QS) system is a natural mechanism, regulating cell density-dependent behaviors in Bacillus subtilis. This study employed the QS-targeted promoter PsrfA to express the pps gene cluster in B. subtilis, coupling the ComQXPA system to produce fengycin. Mutations in the ComA regulatory protein-binding site RE3 exhibited a 2.45-fold increase in promoter expression intensity and resulted in an elevation of fengycin production from 489 to 1832 mg/L, a 2.74-fold enhancement. Transcriptomic analysis revealed the upregulation of genes associated with carbon source uptake and utilization and metabolic pathways related to amino acids and fatty acids, which are precursors for fengycin synthesis. Additionally, knockout of rapJ and rapE increased fengycin production to 3190 mg/L. In a coculture system constructed with Corynebacterium glutamicum, fengycin production reached 4005 mg/L. This work provides a strategy for dynamically regulating fengycin synthesis.

Concentration-Dependent CsrA Regulation of the uxuB Transcript Leads to Development of a Post-Transcriptional Bandpass Filter

Post-transcriptional control systems offer new avenues for designing synthetic circuits that provide reduced burden and fewer synthetic regulatory components compared to transcriptionally based tools. Herein, we repurpose a newly identified post-transcriptional interaction between the uxuB mRNA transcript, specifically the 5' UTR + 100 nucleotides of coding sequence (100 nt CDS), and the E. coli Carbon Storage Regulatory A (CsrA) protein to design a biological post-transcriptional bandpass filter. In this work, we characterize the uxuB mRNA as a heterogeneous target of CsrA, where the protein can both activate and repress uxuB activity depending on its intracellular concentration. We leverage this interaction to implement a novel strategy of regulation within the 5' UTR of an mRNA. Specifically, we report a hierarchical binding strategy that may be leveraged by CsrA within uxuB to produce a dose-dependent response in regulatory outcomes. In our semisynthetic circuit, the uxuB 5' UTR + 100 nt CDS sequence is used as a scaffold that is fused to a gene of interest, which allows the circuit to transition between ON/OFF states based on the concentration range of free natively expressed CsrA. Notably, this system exerts regulation comparable to previously developed transcriptional bandpass filters while reducing the number of synthetic circuit components and can be used in concert with additional post-transcriptionally controlled circuits to achieve complex multi-signal control. We anticipate that future characterization of native regulatory RNA-protein systems will enable the development of more complex RNP-based circuits for synthetic biology applications.

Programming Nutrient Detection with Modular Regulators for Dynamic Control of Microbial Biosynthesis

Dynamic control of biosynthetic pathways improves the bioproduction efficiency. One common approach is to use genetic sensors that control pathway expression in response to a nutrient molecule in the target feedstock. However, programming the cellular response requires the engineering of numerous genetic parts, which poses a significant barrier to explore the use of different nutrients as cellular signals. Here we created a dynamic control platform based on a set of modular transcriptional regulators; these regulators control the same promoter for driving gene expression, but each of them responds to a unique signal. We demonstrated that by replacing only the regulator, a different nutrient molecule can then be used for induction of the same genetic circuit. To show host versatility, we implemented this platform in both Escherichia coli and Pseudomonas putida. This platform was then used to program the induction of ethanol production by three nutrients, fructose, cellobiose, and galactose, of which each molecule can be present in a different set of crops. These results suggest that our platform facilitates the use of different agricultural products for the dynamic control of biosynthesis.

Engineering microbial cell factories by multiplexed spatiotemporal control of cellular metabolism: Advances, challenges, and future perspectives

Generally, the metabolism in microbial organism is an intricate, spatiotemporal process that emerges from gene regulatory networks, which affects the efficiency of product biosynthesis. With the coming age of synthetic biology, spatiotemporal control systems have been explored as versatile strategies to promote product biosynthesis at both spatial and temporal levels. Meanwhile, the designer synthetic compartments provide new and promising approaches to engineerable spatiotemporal control systems to construct high-performance microbial cell factories. In this article, we comprehensively summarize recent developments in spatiotemporal control systems for tailoring advanced cell factories, and illustrate how to apply spatiotemporal control systems in different microbial species with desired applications. Future challenges of spatiotemporal control systems and perspectives are also discussed.

Molecular structural arrangement in quorum sensing and bacterial metabolic production

Quorum sensing (QS) regulates bacterial behaviors such as biofilm formation, virulence, and metabolite production through signaling molecules like acyl-homoserine lactones (AHLs), peptides, and AI-2. These signals are pivotal in bacterial communication, influencing pathogenicity and industrial applications. This review explores the molecular architecture of QS signals and their role in metabolite production, emphasizing structural modifications that disrupt bacterial communication to control virulence and enhance industrial processes. Key findings highlight the development of synthetic QS analogs, engineered inhibitors, and microbial consortia as innovative tools in biotechnology and medicine. The review underscores the potential of molecular engineering in managing microbial behaviors and optimizing applications like biofuel production, bioplastics, and anti-virulence therapies. Additionally, cross-species signaling mechanisms, particularly involving AI-2, reveal new opportunities for regulating interspecies cooperation and competition. This synthesis aims to bridge molecular insights with practical applications, showcasing how QS-based technologies can drive advancements in microbial biotechnology and therapeutic strategies.

Tuning and functionalization of logic gates for time resolved programming of bacterial populations

In order to increase our command over genetically engineered bacterial populations in bioprocessing and therapy, synthetic regulatory circuitry needs to enable the temporal programming of a number of consecutive functional tasks without external interventions. In this context, we have engineered a genetic circuit encoding an autonomous but chemically tunable timer in Escherichia coli, based on the concept of a transcription factor cascade mediated by the cytoplasmic dilution of repressors. As proof-of-concept, we used this circuit to impose a time-resolved two-staged synthetic pathway composed of a production-followed-by-lysis program, via a single input. Moreover, via a recombinase step, this synchronous timer was further engineered into an asynchronous timer in which the generational distance of differentiating daughter cells spawning off from a stem-cell like mother cell becomes a predictable driver and proxy for timer dynamics. Using this asynchronous timer circuit, a temporally defined population heterogeneity can be programmed in bacterial populations.

Optimizing HMG-CoA Synthase Expression for Enhanced Limonene Production in Escherichia coli through Temporal Transcription Modulation Using Optogenetics

Overexpression of a single enzyme in a multigene heterologous pathway may be out of balance with the other enzymes in the pathway, leading to accumulated toxic intermediates, imbalanced carbon flux, reduced productivity of the pathway, or an inhibited growth phenotype. Therefore, optimal, balanced, and synchronized expression levels of enzymes in a particular metabolic pathway is critical to maximize production of desired compounds while maintaining cell fitness in a growing culture. Furthermore, the optimal intracellular concentration of an enzyme is determined by the expression strength, specific timing/duration, and degradation rate of the enzyme. Here, we modulated the intracellular concentration of a key enzyme, namely HMG-CoA synthase (HMGS), in the heterologous mevalonate pathway by tuning its expression level and period of transcription to enhance limonene production in Escherichia coli. Facilitated by the tuned blue-light inducible BLADE/pBad system, we observed that limonene production was highest (160 mg/L) with an intermediate transcription level of HMGS from moderate light illumination (41 au, 150 s ON/150 s OFF) throughout the growth. Owing to the easy penetration and removal of blue-light illumination from the growing culture which is hard to obtain using conventional chemical-based induction, we further explored different induction patterns of HMGS under strong light illumination (2047 au, 300 s ON) for different durations along the growth phases. We identified a specific timing of HMGS expression in the log phase (3-9 h) that led to optimal limonene production (200 mg/L). This is further supported by a mathematical model that predicts several periods of blue-light illumination (3-9 h, 0-9 h, 3-12 h, 0-12 h) to achieve an optimal expression level of HMGS that maximizes limonene production and maintains cell fitness. Compared to moderate and prolonged transcription (41 au, 150 s ON/150 s OFF, 0-73 h), strong but time-limited transcription (2047 au, 300 s ON, 3-9 h) of HMGS could maintain its optimal intracellular concentration and further increased limonene production up to 92% (250 mg/L) in the longer incubation (up to 73 h) without impacting cell fitness. This work has provided new insight into the "right amount" and "just-in-time" expression of a critical metabolite enzyme in the upper module of the mevalonate pathway using optogenetics. This study would complement previous findings in modulating HMGS expression and potentially be applicable to heterologous production of other terpenoids in E. coli.

Systems-Level Modeling for CRISPR-Based Metabolic Engineering

The CRISPR-Cas system has enabled the development of sophisticated, multigene metabolic engineering programs through the use of guide RNA-directed activation or repression of target genes. To optimize biosynthetic pathways in microbial systems, we need improved models to inform design and implementation of transcriptional programs. Recent progress has resulted in new modeling approaches for identifying gene targets and predicting the efficacy of guide RNA targeting. Genome-scale and flux balance models have successfully been applied to identify targets for improving biosynthetic production yields using combinatorial CRISPR-interference (CRISPRi) programs. The advent of new approaches for tunable and dynamic CRISPR activation (CRISPRa) promises to further advance these engineering capabilities. Once appropriate targets are identified, guide RNA prediction models can lead to increased efficacy in gene targeting. Developing improved models and incorporating approaches from machine learning may be able to overcome current limitations and greatly expand the capabilities of CRISPR-Cas9 tools for metabolic engineering.

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

Improving the genetic stability of bacterial growth control for long-term bioproduction

Using microorganisms for bioproduction requires the reorientation of metabolic fluxes from biomass synthesis to the production of compounds of interest. We previously engineered a synthetic growth switch in Escherichia coli based on inducible expression of the β- and β'-subunits of RNA polymerase. Depending on the level of induction, the cells stop growing or grow at a rate close to that of the wild-type strain. This strategy has been successful in transforming growth-arrested bacteria into biofactories with a high production yield, releasing cellular resources from growth towards biosynthesis. However, high selection pressure is placed on a growth-arrested population, favoring mutations that allow cells to escape from growth control. Accordingly, we made the design of the growth switch more robust by building in genetic redundancy. More specifically, we added the rpoA gene, encoding for the α-subunit of RNA polymerase, under the control of a copy of the same inducible promoter used for expression control of ββ'. The improved growth switch is much more stable (escape frequency <10-9), while preserving the capacity to improve production yields. Moreover, after a long period of growth inhibition the population can be regenerated within a few generations. This opens up the possibility to alternate biomass accumulation and product synthesis over a longer period of time and is an additional step towards the dynamical control of bioproduction.

Biotransformation of ethylene glycol by engineered Escherichia coli

There has been extensive research on the biological recycling of PET waste to address the issue of plastic waste pollution, with ethylene glycol (EG) being one of the main components recovered from this process. Therefore, finding ways to convert PET monomer EG into high-value products is crucial for effective PET waste recycling. In this study, we successfully engineered Escherichia coli to utilize EG and produce glycolic acid (GA), expecting to facilitate the biological recycling of PET waste. The engineered E. coli, able to utilize 10 g/L EG to produce 1.38 g/L GA within 96 h, was initially constructed. Subsequently, strategies based on overexpression of key enzymes and knock-out of the competing pathways are employed to enhance EG utilization along with GA biosynthesis. An engineered E. coli, characterized by the highest GA production titer and substrate conversion rate, was obtained. The GA titer increased to 5.1 g/L with a yield of 0.75 g/g EG, which is the highest level in the shake flake experiments. Transcriptional level analysis and metabolomic analysis were then conducted, revealing that overexpression of key enzymes and knock-out of the competing pathways improved the metabolic flow in the EG utilization. The improved metabolic flow also leads to accelerated synthesis and metabolism of amino acids.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.

Advances in the dynamic control of metabolic pathways in Saccharomyces cerevisiae

The metabolic engineering of Saccharomyces cerevisiae has great potential for enhancing the production of high-value chemicals and recombinant proteins. Recent studies have demonstrated the effectiveness of dynamic regulation as a strategy for optimizing metabolic flux and improving production efficiency. In this review, we provide an overview of recent advancements in the dynamic regulation of S. cerevisiae metabolism. Here, we focused on the successful utilization of transcription factor (TF)-based biosensors within the dynamic regulatory network of S. cerevisiae. These biosensors are responsive to a wide range of endogenous and exogenous signals, including chemical inducers, light, temperature, cell density, intracellular metabolites, and stress. Additionally, we explored the potential of omics tools for the discovery of novel responsive promoters and their roles in fine-tuning metabolic networks. We also provide an outlook on the development trends in this field.

Applications of artificial intelligence and machine learning in dynamic pathway engineering

Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.

Bayesian Optimization for Design of Multiscale Biological Circuits

Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.

Designing artificial pathways for improving chemical production

Metabolic engineering exploits manipulation of catalytic and regulatory elements to improve a specific function of the host cell, often the synthesis of interesting chemicals. Although naturally occurring pathways are significant resources for metabolic engineering, these pathways are frequently inefficient and suffer from a series of inherent drawbacks. Designing artificial pathways in a rational manner provides a promising alternative for chemicals production. However, the entry barrier of designing artificial pathway is relatively high, which requires researchers a comprehensive and deep understanding of physical, chemical and biological principles. On the other hand, the designed artificial pathways frequently suffer from low efficiencies, which impair their further applications in host cells. Here, we illustrate the concept and basic workflow of retrobiosynthesis in designing artificial pathways, as well as the most currently used methods including the knowledge- and computer-based approaches. Then, we discuss how to obtain desired enzymes for novel biochemistries, and how to trim the initially designed artificial pathways for further improving their functionalities. Finally, we summarize the current applications of artificial pathways from feedstocks utilization to various products synthesis, as well as our future perspectives on designing artificial pathways.

Direct evolution of riboflavin kinase significantly enhance flavin mononucleotide synthesis by design and optimization of flavin mononucleotide riboswitch

Flavin mononucleotide (FMN) is the active form of riboflavin. It has a wide range of application scenarios in the pharmaceutical and food additives. However, there are limitations in selecting generic high-throughput screening platforms that improve the properties of enzymes. First, the biosensor in response to FMN concentration was constructed using the FMN riboswitch and confirmed the function of this sensor. Next, the FMN binding site of the sensor was saturated with a mutation that increased its fluorescence range by approximately 127%. Then, the biosensor and the base editing system based on T7RNAP were combined to construct a platform for rapid mutation and screening of riboflavin kinase gene ribC mutants. The mutants screened using this platform increased the yield of FMN by 8-fold. These results indicate that the high-throughput screening platform can rapidly and effectively improve the activity of target enzymes, and provide a new route for screening industrial enzymes.

Diverse mechanisms of bioproduction heterogeneity in fermentation and their control strategies

Microbial bioproduction often faces challenges related to populational heterogeneity, where cells exhibit varying biosynthesis capabilities. Bioproduction heterogeneity can stem from genetic and non-genetic factors, resulting in decreased titer, yield, stability, and reproducibility. Consequently, understanding and controlling bioproduction heterogeneity are crucial for enhancing the economic competitiveness of large-scale biomanufacturing. In this review, we provide a comprehensive overview of current understandings of the various mechanisms underlying bioproduction heterogeneity. Additionally, we examine common strategies for controlling bioproduction heterogeneity based on these mechanisms. By implementing more robust measures to mitigate heterogeneity, we anticipate substantial enhancements in the scalability and stability of bioproduction processes.

ONE-SENTENCE SUMMARY

This review summarizes current understandings of different mechanisms of bioproduction heterogeneity and common control strategies based on these mechanisms.

Multiomics and optobiotechnological approaches for the development of microalgal strain for production of aviation biofuel and biorefinery

Demand and consumption of fossil fuels is increasing daily, and oil reserves are depleting. Technological developments are required towards developing sustainable renewable energy sources and microalgae are emerging as a potential candidate for various application-driven research. Molecular understanding attained through omics and system biology approach empowering researchers to modify various metabolic pathways of microalgal system for efficient extraction of biofuel and important biomolecules. This review furnish insight into different "advanced approaches" like optogenetics, systems biology and multi-omics for enhanced production of FAS (Fatty Acid Synthesis) and lipids in microalgae and their associated challenges. These new approaches would be helpful in the path of developing microalgae inspired technological platforms for optobiorefinery, which could be explored as source material to produce biofuels and other valuable bio-compounds on a large scale.

Lignocellulolytic Biocatalysts: The Main Players Involved in Multiple Biotechnological Processes for Biomass Valorization

Human population growth, industrialization, and globalization have caused several pressures on the planet's natural resources, culminating in the severe climate and environmental crisis which we are facing. Aiming to remedy and mitigate the impact of human activities on the environment, the use of lignocellulolytic enzymes for biofuel production, food, bioremediation, and other various industries, is presented as a more sustainable alternative. These enzymes are characterized as a group of enzymes capable of breaking down lignocellulosic biomass into its different monomer units, making it accessible for bioconversion into various products and applications in the most diverse industries. Among all the organisms that produce lignocellulolytic enzymes, microorganisms are seen as the primary sources for obtaining them. Therefore, this review proposes to discuss the fundamental aspects of the enzymes forming lignocellulolytic systems and the main microorganisms used to obtain them. In addition, different possible industrial applications for these enzymes will be discussed, as well as information about their production modes and considerations about recent advances and future perspectives in research in pursuit of expanding lignocellulolytic enzyme uses at an industrial scale.

A tunable metabolic valve for precise growth control and increased product formation in Pseudomonas putida

Metabolic engineering of microorganisms aims to design strains capable of producing valuable compounds under relevant industrial conditions and in an economically competitive manner. From this perspective, and beyond the need for a catalyst, biomass is essentially a cost-intensive, abundant by-product of a microbial conversion. Yet, few broadly applicable strategies focus on the optimal balance between product and biomass formation. Here, we present a genetic control module that can be used to precisely modulate growth of the industrial bacterial chassis Pseudomonas putida KT2440. The strategy is based on the controllable expression of the key metabolic enzyme complex pyruvate dehydrogenase (PDH) which functions as a metabolic valve. By tuning the PDH activity, we accurately controlled biomass formation, resulting in six distinct growth rates with parallel overproduction of excess pyruvate. We deployed this strategy to identify optimal growth patterns that improved the production yield of 2-ketoisovalerate and lycopene by 2.5- and 1.38-fold, respectively. This ability to dynamically steer fluxes to balance growth and production substantially enhances the potential of this remarkable microbial chassis for a wide range of industrial applications.

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.

A novel nonlinear sliding mode observer to estimate biomass for lactic acid production

This study deals with the problem of estimating the amount of biomass and lactic acid concentration in a lactic acid production process. A continuous stirred tank bioreactor was used for the culture of Lactobacillus helveticus. A nonlinear sliding mode observer is proposed and designed, which gives an estimate of both the biomass and lactic acid concentrations as a function of glucose uptake from the culture medium. Numerical results are given to illustrate the effectiveness of the proposed observer against a standard sliding-mode observer. It was found that the proposed observer worked very well for the benchmark bioreactor model. Also, the numerical results indicated that the proposed estimation methodology was robust to the uncertainties associated with un-modelled dynamics. These new sensing technologies, when coupled to software models, improve performance for smart process control, monitoring, and prediction.

A p-Coumaroyl-CoA Biosensor for Dynamic Regulation of Naringenin Biosynthesis in Saccharomyces cerevisiae

In vivo biosensors that can convert metabolite concentrations into measurable output signals are valuable tools for high-throughput screening and dynamic pathway control in the field of metabolic engineering. Here, we present a novel biosensor in Saccharomyces cerevisiae that is responsive to p-coumaroyl-CoA, a central precursor of many flavonoids. The sensor is based on the transcriptional repressor CouR from Rhodopseudomonas palustris and was applied in combination with a previously developed malonyl-CoA biosensor for dual regulation of p-coumaroyl-CoA synthesis within the naringenin production pathway. Using this approach, we obtained a naringenin titer of 47.3 mg/L upon external precursor feeding, representing a 15-fold increase over the nonregulated system.

Sustainable citric acid production from CO2 in an engineered cyanobacterium

Citric acid is one of the most widely used organic acids in the world, with applications ranging from acidity regulation in food and beverages to metal chelation in hydrometallurgical processes. Most of its production is currently derived from fermentative processes, using plant-derived carbon feedstocks. While these are currently dominant, there is an increasing need to develop closed-loop production systems that reduce process carbon footprint. In this work, we demonstrate for the first time that an engineered marine cyanobacterium Synechococcus sp. PCC 7002 can be used as a sustainable chassis for the photosynthetic conversion of CO₂ to citric acid. Decreased citric acid cycle flux, through the use of a theophylline-responsive riboswitch, was combined with improved flux through citrate synthase and enhanced citric acid excretion, resulting in a significant improvement to citric acid production. While allowing citrate production, this strategy induces a growth defect which can be overcome by glutamate supplementation or by fine-tuning aconitase levels, resulting in an increase in production relative to WT of over 100-fold. This work represents a first step toward sustainable production of a commodity organic acid from CO₂.

The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors

Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.

Synthetic microbiology applications powered by light

Synthetic biology is a field of research in which molecular parts (mostly nucleic acids and proteins) are de novo created or modified and then used either alone or in combination to achieve new functions that can help solve the problems of our modern society. In synthetic microbiology, microbes are employed rather than other organisms or cell-free systems. Optogenetics, a relatively recently established technology that relies on the use of genetically encoded photosensitive proteins to control biological processes with high spatiotemporal precision, offers the possibility to empower synthetic (micro)biology applications due to the many positive features that light has as an external trigger. In this review, we describe recent synthetic microbiology applications that made use of optogenetics after briefly introducing the molecular mechanism behind some of the most employed optogenetic tools. We highlight the power and versatility of this technique, which opens up new horizons for both research and industry.

Redesigning regulatory components of quorum-sensing system for diverse metabolic control

Quorum sensing (QS) is a ubiquitous cell–cell communication mechanism that can be employed to autonomously and dynamically control metabolic fluxes. However, since the functions of genetic components in the circuits are not fully understood, the developed QS circuits are still less sophisticated for regulating multiple sets of genes or operons in metabolic engineering applications. Here, we discover the regulatory roles of a CRP-binding site and the lux box to −10 region within luxR-luxI intergenic sequence in controlling the lux-type QS promoters. By varying the numbers of the CRP-binding site and redesigning the lux box to −10 site sequence, we create a library of QS variants that possess both high dynamic ranges and low leakiness. These circuits are successfully applied to achieve diverse metabolic control in salicylic acid and 4-hydroxycoumarin biosynthetic pathways in Escherichia coli. This work expands the toolbox for dynamic control of multiple metabolic fluxes under complex metabolic background and presents paradigms to engineer metabolic pathways for high-level synthesis of target products.

Existing quorum sensing (QS) circuits are less sophisticated for regulating multiple sets of genes or operons. Here, the authors redesign the luxR-luxI intergenic sequence of the lux-type QS system and apply it to achieve diverse metabolic control in salicylic acid and 4-hydroxycoumarin biosynthesis in E. coli.

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

Modeling and Optimization of a Molecular Biocontroller for the Regulation of Complex Metabolic Pathways

Achieving optimal production in microbial cell factories, robustness against changing intracellular and environmental perturbations requires the dynamic feedback regulation of the pathway of interest. Here, we consider a merging metabolic pathway motif, which appears in a wide range of metabolic engineering applications, including the production of phenylpropanoids among others. We present an approach to use a realistic model that accounts for in vivo implementation and then propose a methodology based on multiobjective optimization for the optimal tuning of the gene circuit parts composing the biomolecular controller and biosensor devices for a dynamic regulation strategy. We show how this approach can deal with the trade-offs between the performance of the regulated pathway, robustness to perturbations, and stability of the feedback loop. Using realistic models, our results suggest that the strategies for fine-tuning the trade-offs among performance, robustness, and stability in dynamic pathway regulation are complex. It is not always possible to infer them by simple inspection. This renders the use of the multiobjective optimization methodology valuable and necessary.

Harnessing plasmid replication mechanism to enable dynamic control of gene copy in bacteria

Dynamic regulation has been proved efficient in controlling gene expression at transcriptional, translational, and post-translational level. However, the dynamic regulation at gene replication level has been rarely explored so far. In this study, we established dynamic regulation at gene copy level through engineering controllable plasmid replication to dynamically control the gene expression. Prototypic genetic circuits with different control logic were applied to enable diversified dynamic behaviors of gene copy. To explore the applicability of this strategy, the dynamic gene copy control was employed in regulating the biosynthesis of p-coumaric acid, which resulted in an up to 78% increase in p-coumaric acid titer to 1.69 g/L in shake flasks. These results indicated the great potential of applying dynamic gene copy control for engineering biosynthesis of valuable compounds in metabolic engineering.

Stabilization of antithetic control via molecular buffering

A key goal in synthetic biology is the construction of molecular circuits that robustly adapt to perturbations. Although many natural systems display perfect adaptation, whereby stationary molecular concentrations are insensitive to perturbations, its de novo engineering has proven elusive. The discovery of the antithetic control motif was a significant step towards a universal mechanism for engineering perfect adaptation. Antithetic control provides perfect adaptation in a wide range of systems, but it can lead to oscillatory dynamics due to loss of stability; moreover, it can lose perfect adaptation in fast growing cultures. Here, we introduce an extended antithetic control motif that resolves these limitations. We show that molecular buffering, a widely conserved mechanism for homeostatic control in Nature, stabilizes oscillations and allows for near-perfect adaptation during rapid growth. We study multiple buffering topologies and compare their performance in terms of their stability and adaptation properties. We illustrate the benefits of our proposed strategy in exemplar models for biofuel production and growth rate control in bacterial cultures. Our results provide an improved circuit for robust control of biomolecular systems.

Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.

In Vivo Screening Method for the Identification and Characterization of Prokaryotic, Metabolite-Responsive Transcription Factors

In prokaryotes, transcription factors (TFs) are of uttermost importance for the regulation of gene expression. However, the majority of TFs are not characterized today, which hampers both the understanding of fundamental processes and the development of TF-based applications, such as biosensors, used in metabolic engineering, synthetic biology, diagnostics, etc. One way of analyzing TFs is through in vivo screening, enabling the study of TF-promoter interactions, ligand inducibility, and ligand specificity in a high-throughput fashion. Here, an approach is described for the selection and cloning of TF-promoter pairs, the development of a reporter system, and the measurement and analysis of fluorescent reporter assays. Furthermore, the importance of a suitable inducible plasmid system is illustrated together with prospective adaptations to modify a reporter system's output signal. The given approach can be used for the investigation of native, heterologous, or even artificially created TFs in Escherichia coli, and can be extended toward use in other microorganisms.

Fine-tuning gene expression for improved biosynthesis of natural products: From transcriptional to post-translational regulation

Microbial production of natural compounds has attracted extensive attention due to their high value in pharmaceutical, cosmetic, and food industries. Constructing efficient microbial cell factories for biosynthesis of natural products requires the fine-tuning of gene expressions to minimize the accumulation of toxic metabolites, reduce the competition between cell growth and product generation, as well as achieve the balance of redox or co-factors. In this review, we focus on recent advances in fine-tuning gene expression at the DNA, RNA, and protein levels to improve the microbial biosynthesis of natural products. Commonly used regulatory toolsets in each level are discussed, and perspectives for future direction in this area are provided.

Integrated engineering of enzymes and microorganisms for improving the efficiency of industrial lignocellulose deconstruction

Bioconversion of lignocellulosic biomass to fuels and chemicals represents a new manufacturing paradigm that can help address society's energy, resource, and environmental problems. However, the low efficiency and high cost of lignocellulolytic enzymes currently used hinder their use in the industrial deconstruction of lignocellulose. To overcome these challenges, research efforts have focused on engineering the properties, synergy, and production of lignocellulolytic enzymes. First, lignocellulolytic enzymes' catalytic efficiency, stability, and tolerance to inhibitory compounds have been improved through enzyme mining and engineering. Second, synergistic actions between different enzyme components have been strengthened to construct customized enzyme cocktails for the degradation of specific lignocellulosic substrates. Third, biological processes for protein synthesis and cell morphogenesis in microorganisms have been engineered to achieve a high level and low-cost production of lignocellulolytic enzymes. In this review, the relevant progresses and challenges in these fields are summarized. Integrated engineering is proposed to be essential to achieve cost-effective enzymatic deconstruction of lignocellulose in the future.

Modeling the time course of ComX: towards molecular process control for Bacillus wild-type cultivations

Wild-type cultivations are of invaluable relevance for industrial biotechnology when it comes to the agricultural or food sector. Here, genetic engineering is hardly applicable due to legal barriers and consumer’s demand for GMO-free products. An important pillar for wild-type cultivations displays the genus Bacillus. One of the challenges for Bacillus cultivations is the global ComX-dependent quorum sensing system. Here, molecular process control can serve as a tool to optimize the production process without genetic engineering. To realize this approach, quantitative knowledge of the mechanism is essential, which, however, is often available only to a limited extent. The presented work provides a case study based on the production of cyclic lipopeptide surfactin, whose expression is in dependence of ComX, using natural producer B. subtilis DSM 10 ^T. First, a surfactin reference process with 40 g/L of glucose was performed as batch fermentation in a pilot scale bioreactor system to gain novel insights into kinetic behavior of ComX in relation to surfactin production. Interestingly, the specific surfactin productivity did not increase linearly with ComX activity. The data were then used to derive a mathematic model for the time course of ComX in dependence of existing biomass, biomass growth as well as a putative ComX-specific protease. The newly adapted model was validated and transferred to other batch fermentations, employing 20 and 60 g/L glucose. The applied approach can serve as a model system for molecular process control strategies, which can thus be extended to other quorum sensing dependent wild-type cultivations.

Challenges and opportunities in biological funneling of heterogeneous and toxic substrates beyond lignin

Significant developments in the understanding and manipulation of microbial metabolism have enabled the use of engineered biological systems toward a more sustainable energy and materials economy. While developments in metabolic engineering have primarily focused on the conversion of carbohydrates, substantial opportunities exist for using these same principles to extract value from more heterogeneous and toxic waste streams, such as those derived from lignin, biomass pyrolysis, or industrial waste. Funneling heterogeneous substrates from these streams toward valuable products, termed biological funneling, presents new challenges in balancing multiple catabolic pathways competing for shared cellular resources and engineering against perturbation from toxic substrates. Solutions to many of these challenges have been explored within the field of lignin valorization. This perspective aims to extend beyond lignin to highlight the challenges and discuss opportunities for use of biological systems to upgrade previously inaccessible waste streams.

Modular optimization in metabolic engineering

There is an increasing demand for bioproducts produced by metabolically engineered microbes, such as pharmaceuticals, biofuels, biochemicals and other high value compounds. In order to meet this demand, modular optimization, the optimizing of subsections instead of the whole system, has been adopted to engineer cells to overproduce products. Research into modularity has focused on traditional approaches such as DNA, RNA, and protein-level modularity of intercellular machinery, by optimizing metabolic pathways for enhanced production. While research into these traditional approaches continues, limitations such as scale-up and time cost hold them back from wider use, while at the same time there is a shift to more novel methods, such as moving from episomal expression to chromosomal integration. Recently, nontraditional approaches such as co-culture systems and cell-free metabolic engineering (CFME) are being investigated for modular optimization. Co-culture modularity looks to optimally divide the metabolic burden between different hosts. CFME seeks to modularly optimize metabolic pathways in vitro, both speeding up the design of such systems and eliminating the issues associated with live hosts. In this review we will examine both traditional and nontraditional approaches for modular optimization, examining recent developments and discussing issues and emerging solutions for future research in metabolic engineering.

Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity

Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, productivity rate, and yield (TRY). Here we use a multiscale model incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and expression of the protein of interest. Our model predicts cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the following main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. We evaluated the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature.

Designing an irreversible metabolic switch for scalable induction of microbial chemical production

Bacteria can be harnessed to synthesise high-value chemicals. A promising strategy for increasing productivity uses inducible control systems to switch metabolism from growth to chemical synthesis once a large population of cell factories are generated. However, use of expensive chemical inducers limits scalability of this approach for biotechnological applications. Switching using cheap nutrients is an appealing alternative, but their tightly regulated uptake and consumption again limits scalability. Here, using mathematical models of fatty acid uptake in E. coli as an exemplary case study, we unravel how the cell's native regulation and program of induction can be engineered to minimise inducer usage. We show that integrating positive feedback loops into the circuitry creates an irreversible metabolic switch, which, requiring only temporary induction, drastically reduces inducer usage. Our proposed switch should be widely applicable, irrespective of the product of interest, and brings closer the realization of scalable and sustainable microbial chemical production.

Recent advances in tuning the expression and regulation of genes for constructing microbial cell factories

To overcome environmental problems caused by the use of fossil resources, microbial cell factories have become a promising technique for the sustainable and eco-friendly development of valuable products from renewable resources. Constructing microbial cell factories with high titers, yields, and productivity requires a balance between growth and production; to this end, tuning gene expression and regulation is necessary to optimise and precisely control complicated metabolic fluxes. In this article, we review the current trends and advances in tuning gene expression and regulation and consider their engineering at each of the three stages of gene regulation: genomic, mRNA, and protein. In particular, the technological approaches utilised in a diverse range of genetic-engineering-based tools for the construction of microbial cell factories are reviewed and representative applications of these strategies are presented. Finally, the prospects for strategies and systems for tuning gene expression and regulation are discussed.

Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits

Qualitative modeling approaches are promising and still underexploited tools for the analysis and design of synthetic circuits. They can make predictions of circuit behavior in the absence of precise, quantitative information. Moreover, they provide direct insight into the relation between the feedback structure and the dynamical properties of a network. We review qualitative modeling approaches by focusing on two specific formalisms, Boolean networks and piecewise-linear differential equations, and illustrate their application by means of three well-known synthetic circuits. We describe various methods for the analysis of state transition graphs, discrete representations of the network dynamics that are generated in both modeling frameworks. We also briefly present the problem of controlling synthetic circuits, an emerging topic that could profit from the capacity of qualitative modeling approaches to rapidly scan a space of design alternatives.

Massively parallel gene expression variation measurement of a synonymous codon library

BACKGROUND

Cell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood.

RESULTS

Here, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV2), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance.

CONCLUSIONS

Our results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications.