Dynamic Control of Metabolism

Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling

Engineering metabolism to efficiently produce chemicals from multi-step pathways requires optimizing multi-gene expression programs to achieve enzyme balance. CRISPR-Cas transcriptional control systems are emerging as important tools for programming multi-gene expression, but poor predictability of guide RNA folding can disrupt expression control. Here, we correlate efficacy of modified guide RNAs (scRNAs) for CRISPR activation (CRISPRa) in E. coli with a computational kinetic parameter describing scRNA folding rate into the active structure (rS = 0.8). This parameter also enables forward design of scRNAs, allowing us to design a system of three synthetic CRISPRa promoters that can orthogonally activate (>35-fold) expression of chosen outputs. Through combinatorial activation tuning, we profile a three-dimensional design space expressing two different biosynthetic pathways, demonstrating variable production of pteridine and human milk oligosaccharide products. This RNA design approach aids combinatorial optimization of metabolic pathways and may accelerate routine design of effective multi-gene regulation programs in bacterial hosts.

Genome-wide transcription response of Staphylococcus epidermidis to heat shock and medically relevant glucose levels

Skin serves as both barrier and interface between body and environment. Skin microbes are intermediaries evolved to respond, transduce, or act in response to changing environmental or physiological conditions. We quantified genome-wide changes in gene expression levels for one abundant skin commensal, Staphylococcus epidermidis, in response to an internal physiological signal, glucose levels, and an external environmental signal, temperature. We found 85 of 2,354 genes change up to ~34-fold in response to medically relevant changes in glucose concentration (0-17 mM; adj p ≤0.05). We observed carbon catabolite repression in response to a range of glucose spikes, as well as upregulation of genes involved in glucose utilization in response to persistent glucose. We observed 366 differentially expressed genes in response to a physiologically relevant change in temperature (37-45°C; adj p ≤ 0.05) and an S. epidermidis heat-shock response that mostly resembles the heat-shock response of related staphylococcal species. DNA motif analysis revealed CtsR and CIRCE operator sequences arranged in tandem upstream of dnaK and groESL operons. We identified and curated 38 glucose-responsive genes as candidate ON or OFF switches for use in controlling synthetic genetic systems. Such systems might be used to instrument the in-situ skin microbiome or help control microbes bioengineered to serve as embedded diagnostics, monitoring, or treatment platforms.

DetSpace: a web server for engineering detectable pathways for bio-based chemical production

Tackling climate change challenges requires replacing current chemical industrial processes through the rational and sustainable use of biodiversity resources. To that end, production routes to key bio-based chemicals for the bioeconomy have been identified. However, their production still remains inefficient in terms of titers, rates, and yields; because of the hurdles found when scaling up. In order to make production more efficient, strategies like automated screening and dynamic pathway regulation through biosensors have been applied as part of strain optimization. However, to date, no systematic way exists to design a genetic circuit that is responsive to concentrations of a given target compound. Here, the DetSpace web server provides a set of integrated tools that allows a user to select and design a biological circuit that performs the sensing of a molecule of interest by its enzymatic conversion to a detectable molecule through a transcription factor. In that way, the DetSpace web server allows synthetic biologists to easily design biosensing routes for the dynamic regulation of metabolic pathways in applications ranging from genetic circuits design, screening, production, and bioremediation of bio-based chemicals, to diagnostics and drug delivery.

Microbial production of sulfur-containing amino acids using metabolically engineered Escherichia coli

L-Cysteine and L-methionine, as the only two sulfur-containing amino acids among the canonical 20 amino acids, possess distinct characteristics and find wide-ranging industrial applications. The use of different organisms for fermentative production of L-cysteine and L-methionine is gaining increasing attention, with Escherichia coli being extensively studied as the preferred strain. This preference is due to its ability to grow rapidly in cost-effective media, its robustness for industrial processes, the well-characterized metabolism, and the availability of molecular tools for genetic engineering. This review focuses on the genetic and molecular mechanisms involved in the production of these sulfur-containing amino acids in E. coli. Additionally, we systematically summarize the metabolic engineering strategies employed to enhance their production, including the identification of new targets, modulation of metabolic fluxes, modification of transport systems, dynamic regulation strategies, and optimization of fermentation conditions. The strategies and design principles discussed in this review hold the potential to facilitate the development of strain and process engineering for direct fermentation of sulfur-containing 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.

Metabolic flux optimization of iterative pathways through orthogonal gene expression control: Application to the β-oxidation reversal

Balancing relative expression of pathway genes to minimize flux bottlenecks and metabolic burden is one of the key challenges in metabolic engineering. This is especially relevant for iterative pathways, such as reverse β-oxidation (rBOX) pathway, which require control of flux partition at multiple nodes to achieve efficient synthesis of target products. Here, we develop a plasmid-based inducible system for orthogonal control of gene expression (referred to as the TriO system) and demonstrate its utility in the rBOX pathway. Leveraging effortless construction of TriO vectors in a plug-and-play manner, we simultaneously explored the solution space for enzyme choice and relative expression levels. Remarkably, varying individual expression levels led to substantial change in product specificity ranging from no production to optimal performance of about 90% of the theoretical yield of the desired products. We obtained titers of 6.3 g/L butyrate, 2.2 g/L butanol and 4.0 g/L hexanoate from glycerol in E. coli, which exceed the best titers previously reported using equivalent enzyme combinations. Since a similar system behavior was observed with alternative termination routes and higher-order iterations, we envision our approach to be broadly applicable to other iterative pathways besides the rBOX. Considering that high throughput, automated strain construction using combinatorial promoter and RBS libraries remain out of reach for many researchers, especially in academia, tools like the TriO system could democratize the testing and evaluation of pathway designs by reducing cost, time and infrastructure requirements.

Improving Microbial Cell Factory Performance by Engineering SAM Availability

Methylated natural products are widely spread in nature. S-Adenosyl-l-methionine (SAM) is the secondary abundant cofactor and the primary methyl donor, which confer natural products with structural and functional diversification. The increasing demand for SAM-dependent natural products (SdNPs) has motivated the development of microbial cell factories (MCFs) for sustainable and efficient SdNP production. Insufficient and unsustainable SAM availability hinders the improvement of SdNP MCF performance. From the perspective of developing MCF, this review summarized recent understanding of de novo SAM biosynthesis and its regulatory mechanism. SAM is just the methyl mediator but not the original methyl source. Effective and sustainable methyl source supply is critical for efficient SdNP production. We compared and discussed the innate and relatively less explored alternative methyl sources and identified the one involving cheap one-carbon compound as more promising. The SAM biosynthesis is synergistically regulated on multilevels and is tightly connected with ATP and NAD(P)H pools. We also covered the recent advancement of metabolic engineering in improving intracellular SAM availability and SdNP production. Dynamic regulation is a promising strategy to achieve accurate and dynamic fine-tuning of intracellular SAM pool size. Finally, we discussed the design and engineering constraints underlying construction of SAM-responsive genetic circuits and envisioned their future applications in developing SdNP MCFs.

Engineering bacteria for cancer immunotherapy

Bacterial therapeutics have emerged as promising delivery systems to target tumors. These engineered live therapeutics can be harnessed to modulate the tumor microenvironment or to deliver and selectively release therapeutic payloads to tumors. A major challenge is to deliver bacteria systemically without causing widespread inflammation, which is critical for the many tumors that are not accessible to direct intratumoral injection. We describe potential strategies to address this challenge, along with approaches for specific payload delivery and biocontainment to ensure safety. These strategies will pave the way for the development of cost-effective, widely applicable next-generation cancer therapeutics.

Building a highly efficient Streptomyces super-chassis for secondary metabolite production by reprogramming naturally-evolved multifaceted shifts

Streptomyces has an extensive array of bioactive secondary metabolites (SMs). Nevertheless, devising a framework for the heterologous production of these SMs remains challenging. We here reprogrammed a versatile plug-and-play Streptomyces super-chassis and established a universal pipeline for production of diverse SMs via understanding of the inherent pleiotropic effects of ethanol shock on jadomycin production in Streptomyces venezuelae. We initially identified and characterized a set of multiplex targets (afsQ1, bldD, bldA, and miaA) that contribute to SM (jadomycin) production when subjected to ethanol shock. Subsequently, we developed an ethanol-induced orthogonal amplification system (EOAS), enabling dynamic and precise control over targets. Ultimately, we integrated these multiplex targets into functional units governed by the EOAS, generating a universal and plug-and-play Streptomyces super-chassis. In addition to achieving the unprecedented titer and yield of jadomycin B, we also evidenced the potential of this super-chassis for production of diverse heterologous SMs, including antibiotic oxytetracycline, anticancer drug doxorubicins, agricultural herbicide thaxtomin A, and plant growth regulator guvermectin, all with the yields of >10 mg/g glucose in a simple mineral medium. Given that the production of SMs all required complexed medium and the cognate yields were usually much lower, our achievement of using a universal super-chassis and engineering pipeline in a simple mineral medium is promising for convenient heterologous production of SMs.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Progress and Prospects of Natural Glycoside Sweetener Biosynthesis: A Review

To achieve an adequate sense of sweetness with a healthy low-sugar diet, it is necessary to explore and produce sugar alternatives. Recently, glycoside sweeteners and their biosynthetic approaches have attracted the attention of researchers. In this review, we first outlined the synthetic pathways of glycoside sweeteners, including the key enzymes and rate-limiting steps. Next, we reviewed the progress in engineered microorganisms producing glycoside sweeteners, including de novo synthesis, whole-cell catalysis synthesis, and in vitro synthesis. The applications of metabolic engineering strategies, such as cofactor engineering and enzyme modification, in the optimization of glycoside sweetener biosynthesis were summarized. Finally, the prospects of combining enzyme engineering and machine learning strategies to enhance the production of glycoside sweeteners were discussed. This review provides a perspective on synthesizing glycoside sweeteners in microbial cells, theoretically guiding the bioproduction of glycoside sweeteners.

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.

Improved Synthesis of Deoxyadenosine Triphosphate by Saccharomyces cerevisiae Using an Efficient ATP Regeneration System: Optimization of Response Surface Analysis

Deoxyadenosine triphosphate (dATP) is an important biochemical molecule. In this paper, the synthesis of dATP from deoxyadenosine monophosphate (dAMP), catalyzed by Saccharomyces cerevisiae, was studied. By adding chemical effectors, an efficient ATP regeneration and coupling system was constructed to achieve efficient synthesis of dATP. Factorial and response surface designs were used to optimize process conditions. Optimal reaction conditions were as follows: dAMP 1.40 g/L, glucose 40.97 g/L, MgCl₂·6H₂O 4.00 g/L, KCl 2.00 g/L, NaH₂PO₄ 31.20 g/L, yeast 300.00 g/L, ammonium chloride 0.67 g/L, acetaldehyde 11.64 mL/L, pH 7.0, temperature 29.6 °C. Under these conditions, the substrate conversion was 93.80% and the concentration of dATP in the reaction system was 2.10 g/L, which was 63.10% higher than before optimization, and the concentration of product was 4 times higher than before optimization. The effects of glucose, acetaldehyde, and temperature on the accumulation of dATP were analyzed.

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.

Toward a synthetic hydrogen sensor in cyanobacteria: Functional production of an oxygen-tolerant regulatory hydrogenase in Synechocystis sp. PCC 6803

Cyanobacteria have raised great interest in biotechnology, e.g., for the sustainable production of molecular hydrogen (H₂) using electrons from water oxidation. However, this is hampered by various constraints. For example, H₂-producing enzymes compete with primary metabolism for electrons and are usually inhibited by molecular oxygen (O₂). In addition, there are a number of other constraints, some of which are unknown, requiring unbiased screening and systematic engineering approaches to improve the H₂ yield. Here, we introduced the regulatory [NiFe]-hydrogenase (RH) of Cupriavidus necator (formerly Ralstonia eutropha) H16 into the cyanobacterial model strain Synechocystis sp. PCC 6803. In its natural host, the RH serves as a molecular H₂ sensor initiating a signal cascade to express hydrogenase-related genes when no additional energy source other than H₂ is available. Unlike most hydrogenases, the C. necator enzymes are O₂-tolerant, allowing their efficient utilization in an oxygenic phototroph. Similar to C. necator, the RH produced in Synechocystis showed distinct H₂ oxidation activity, confirming that it can be properly matured and assembled under photoautotrophic, i.e., oxygen-evolving conditions. Although the functional H₂-sensing cascade has not yet been established in Synechocystis yet, we utilized the associated two-component system consisting of a histidine kinase and a response regulator to drive and modulate the expression of a superfolder gfp gene in Escherichia coli. This demonstrates that all components of the H₂-dependent signal cascade can be functionally implemented in heterologous hosts. Thus, this work provides the basis for the development of an intrinsic H₂ biosensor within a cyanobacterial cell that could be used to probe the effects of random mutagenesis and systematically identify promising genetic configurations to enable continuous and high-yield production of H₂ via oxygenic photosynthesis.

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.

Parallel engineering of environmental bacteria and performance over years under jungle-simulated conditions

Engineered bacteria could perform many functions in the environment, for example, to remediate pollutants, deliver nutrients to crops or act as in-field biosensors. Model organisms can be unreliable in the field, but selecting an isolate from the thousands that naturally live there and genetically manipulating them to carry the desired function is a slow and uninformed process. Here, we demonstrate the parallel engineering of isolates from environmental samples by using the broad-host-range XPORT conjugation system (Bacillus subtilis mini-ICEBs1) to transfer a genetic payload to many isolates in parallel. Bacillus and Lysinibacillus species were obtained from seven soil and water samples from different locations in Israel. XPORT successfully transferred a genetic function (reporter expression) into 25 of these isolates. They were then screened to identify the best-performing chassis based on the expression level, doubling time, functional stability in soil, and environmentally-relevant traits of its closest annotated reference species, such as the ability to sporulate and temperature tolerance. From this library, we selected Bacillus frigoritolerans A3E1, re-introduced it to soil, and measured function and genetic stability in a contained environment that replicates jungle conditions. After 21 months of storage, the engineered bacteria were viable, could perform their function, and did not accumulate disruptive mutations.

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.

Optimizing microbial networks through metabolic bypasses

Metabolism has long been considered as a relatively stiff set of biochemical reactions. This somewhat outdated and dogmatic view has been challenged over the last years, as multiple studies exposed unprecedented plasticity of metabolism by exploring rational and evolutionary modifications within the metabolic network of cell factories. Of particular importance is the emergence of metabolic bypasses, which consist of enzymatic reaction(s) that support unnatural connections between metabolic nodes. Such novel topologies can be generated through the introduction of heterologous enzymes or by upregulating native enzymes (sometimes relying on promiscuous activities thereof). Altogether, the adoption of bypasses resulted in an expansion in the capacity of the host's metabolic network, which can be harnessed for bioproduction. In this review, we discuss modifications to the canonical architecture of central carbon metabolism derived from such bypasses towards six optimization purposes: stoichiometric gain, overcoming kinetic limitations, solving thermodynamic barriers, circumventing toxic intermediates, uncoupling product synthesis from biomass formation, and altering redox cofactor specificity. The metabolic costs associated with bypass-implementation are likewise discussed, including tailoring their design towards improving bioproduction.

Strategies for Enhancing Microbial Production of 2'-Fucosyllactose, the Most Abundant Human Milk Oligosaccharide

Human milk oligosaccharides (HMOs), a group of structurally diverse unconjugated glycans in breast milk, act as important prebiotics and have plenty of unique health effects for growing infants. 2'-Fucosyllactose (2'-FL) is the most abundant HMO, accounting for approximately 30%, among approximately 200 identified HMOs with different structures. 2'-FL can be enzymatically produced by α1,2-fucosyltransferase, using GDP-l-fucose as donor and lactose as acceptor. Metabolic engineering strategies have been widely used for enhancement of GDP-l-fucose supply and microbial production of 2'-FL with high productivity. GDP-l-fucose supply can be enhanced by two main pathways, including de novo and salvage pathways. 2'-FL-producing α1,2-fucosyltransferases have widely been identified from various microorganisms. Metabolic pathways for 2'-FL synthesis can be basically constructed by enhancing GDP-l-fucose supply and introducing α1,2-fucosyltransferase. Various strategies have been attempted to enhance 2'-FL production, such as acceptor enhancement, donor enhancement, and improvement of the functional expression of α1,2-fucosyltransferase. In this review, current progress in GDP-l-fucose synthesis and bacterial α1,2-fucosyltransferases is described in detail, various metabolic engineering strategies for enhancing 2'-FL production are comprehensively reviewed, and future research focuses in biotechnological production of 2'-FL are suggested.

Production of cholesterol-like molecules impacts Escherichia coli robustness, production capacity, and vesicle trafficking

The economic viability of bioprocesses is constrained by the limited range of operating conditions that can be tolerated by the cell factory. Engineering of the microbial cell membrane is one strategy that can increase robustness and thus alter this range. In this work, we targeted cellular components that contribute to maintenance of appropriate membrane function, such as: flotillin-like proteins, membrane structural proteins, and membrane lipids. Specifically, we exploited the promiscuity of squalene hopene cyclase (SHC) to produce polycyclic terpenoids with properties analogous to cholesterol. Strains producing these cholesterol-like molecules were visualized by AFM and height features were observed. Production of these cholesterol-like molecules was associated with increased tolerance towards a diversity of chemicals, particularly alcohols, and membrane trafficking processes such as lipid droplet accumulation and production of extracellular vesicles. This engineering approach improved the production titers for wax-esters and ethanol by 80- and 10-fold, respectively. Expression of SHC resulted in the production of steroids. Strains engineered to also express truncated squalene synthase (tERG9) produced diplopterol and generally did not perform as well. Increased expression of several membrane-associated proteins, such as YqiK, was observed to impact vesicle trafficking and further improve tolerance relative to SHC alone, but did not improve bio-production. Deletion of YbbJ increased lipid droplet accumulation as well as production of intracellular wax esters. This work serves as a proof of concept for engineering strategies targeting membrane physiology and trafficking to expand the production capacity of microbial cell factories.

Platforms for Optogenetic Stimulation and Feedback Control

Harnessing the potential of optogenetics in biology requires methodologies from different disciplines ranging from biology, to mechatronics engineering, to control engineering. Light stimulation of a synthetic optogenetic construct in a given biological species can only be achieved via a suitable light stimulation platform. Emerging optogenetic applications entail a consistent, reproducible, and regulated delivery of light adapted to the application requirement. In this review, we explore the evolution of light-induction hardware-software platforms from simple illumination set-ups to sophisticated microscopy, microtiter plate and bioreactor designs, and discuss their respective advantages and disadvantages. Here, we examine design approaches followed in performing optogenetic experiments spanning different cell types and culture volumes, with induction capabilities ranging from single cell stimulation to entire cell culture illumination. The development of automated measurement and stimulation schemes on these platforms has enabled researchers to implement various in silico feedback control strategies to achieve computer-controlled living systems—a theme we briefly discuss in the last part of this review.

Intracellular biosensor-based dynamic regulation to manipulate gene expression at the spatiotemporal level

The use of intracellular, biosensor-based dynamic regulation strategies to regulate and improve the production of useful compounds have progressed significantly over previous decades. By employing such an approach, it is possible to simultaneously realize high productivity and optimum growth states. However, industrial fermentation conditions contain a mixture of high- and low-performance non-genetic variants, as well as young and aged cells at all growth phases. Such significant individual variations would hinder the precise controlling of metabolic flux at the single-cell level to achieve high productivity at the macroscopic population level. Intracellular biosensors, as the regulatory centers of metabolic networks, can real-time sense intra- and extracellular conditions and, thus, could be synthetically adapted to balance the biomass formation and overproduction of compounds by individual cells. Herein, we highlight advances in the designing and engineering approaches to intracellular biosensors. Then, the spatiotemporal properties of biosensors associated with the distribution of inducers are compared. Also discussed is the use of such biosensors to dynamically control the cellular metabolic flux. Such biosensors could achieve single-cell regulation or collective regulation goals, depending on whether or not the inducer distribution is only intracellular.

Bacterial two-component systems as sensors for synthetic biology applications

Two-component systems (TCSs) are a ubiquitous family of signal transduction pathways that enable bacteria to sense and respond to diverse physical, chemical, and biological stimuli outside and inside the cell. Synthetic biologists have begun to repurpose TCSs for applications in optogenetics, materials science, gut microbiome engineering, and soil nutrient biosensing, among others. New engineering methods including genetic refactoring, DNA-binding domain swapping, detection threshold tuning, and phosphorylation cross-talk insulation are being used to increase the reliability of TCS sensor performance and tailor TCS signaling properties to the requirements of specific applications. There is now potential to combine these methods with large-scale gene synthesis and laboratory screening to discover the inputs sensed by many uncharacterized TCSs and develop a large new family of genetically-encoded sensors that respond to an unrivaled breadth of stimuli.

Two-stage dynamic deregulation of metabolism improves process robustness & scalability in engineered E. coli

We report that two-stage dynamic control improves bioprocess robustness as a result of the dynamic deregulation of central metabolism. Dynamic control is implemented during stationary phase using combinations of CRISPR interference and controlled proteolysis to reduce levels of central metabolic enzymes. Reducing the levels of key enzymes alters metabolite pools resulting in deregulation of the metabolic network. Deregulated networks are less sensitive to environmental conditions improving process robustness. Process robustness in turn leads to predictable scalability, minimizing the need for traditional process optimization. We validate process robustness and scalability of strains and bioprocesses synthesizing the important industrial chemicals alanine, citramalate and xylitol. Predictive high throughput approaches that translate to larger scales are critical for metabolic engineering programs to truly take advantage of the rapidly increasing throughput and decreasing costs of synthetic biology.