Expanding Biosensing Abilities through Computer-Aided Design of Metabolic Pathways

Synthetic biology design principles enable efficient bioproduction of Heparosan with low molecular weight and low polydispersion index for the biomedical industry

Heparosan is a natural polymer with unique chemical and biological properties, that holds great promise for biomedical applications. The molecular weight (Mw) and polydispersion index (PDI) are critical factors influencing the performance of heparosan-based materials. Achieving precise control over the synthesis process to consistently produce heparosan with low Mw and low PDI can be challenging, as it requires tight regulation of reaction conditions, enzyme activity, and precursor concentrations. We propose a novel approach utilizing synthetic biology principles to precisely control heparosan biosynthesis in bacteria. Our strategy involves designing a biomolecular controller that can regulate the expression of genes involved in heparosan biosynthesis. This controller is activated by biosensors that detect heparosan precursors, allowing for fine-tuned control of the polymerization process. Through this approach, we foresee the implementation of this synthetic device, demonstrating the potential to produce low Mw and low PDI heparosan in the probiotic E. coli Nissle 1917 as a biosafe and biosecure biofactory. This study represents a significant advancement in the field of heparosan production, offering new opportunities for the development and manufacturing of biomaterials with tailored properties for diverse biomedical applications.

Biological Switches: Past and Future Milestones of Transcription Factor-Based Biosensors

Since the description of the lac operon in 1961 by Jacob and Monod, transcriptional regulation in prokaryotes has been studied extensively and has led to the development of transcription factor-based biosensors. Due to the broad variety of detectable small molecules and their various applications across biotechnology, biosensor research and development have increased exponentially over the past decades. Throughout this period, key milestones in fundamental knowledge, synthetic biology, analytical tools, and computational learning have led to an immense expansion of the biosensor repertoire and its application portfolio. Over the years, biosensor engineering became a more multidisciplinary discipline, combining high-throughput analytical tools, DNA randomization strategies, forward engineering, and advanced protein engineering workflows. Despite these advances, many obstacles remain to fully unlock the potential of biosensor technology. This review analyzes the timeline of key milestones on fundamental research (1960s to 2000s) and engineering strategies (2000s onward), on both the DNA and protein level of biosensors. Moreover, insights into the future perspectives, remaining hurdles, and unexplored opportunities of this promising field are discussed.

Microbial biosensors for diagnostics, surveillance and epidemiology: Today's achievements and tomorrow's prospects

Microbial biosensors hold great promise for engineering high-performance, field-deployable and affordable detection devices for medical and environmental applications. This review explores recent advances in the field, highlighting new sensing strategies and modalities for whole-cell biosensors as well as the remarkable expansion of microbial cell-free systems. We also discuss improvements in robustness that have enhanced the ability of biosensors to withstand the challenging conditions found in biological samples. However, limitations remain in expanding the detection repertoire, particularly for proteins. We anticipate that the AI-powered revolution in protein design will streamline the engineering of custom-made sensing modules and unlock the full potential of microbial biosensors.

Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology

Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.

In Vitro Enzyme Self-Selection Using Molecular Programs

Directed evolution provides a powerful route for in vitro enzyme engineering. State-of-the-art techniques functionally screen up to millions of enzyme variants using high throughput microfluidic sorters, whose operation remains technically challenging. Alternatively, in vitro self-selection methods, analogous to in vivo complementation strategies, open the way to even higher throughputs, but have been demonstrated only for a few specific activities. Here, we leverage synthetic molecular networks to generalize in vitro compartmentalized self-selection processes. We introduce a programmable circuit architecture that can link an arbitrary target enzymatic activity to the replication of its encoding gene. Microencapsulation of a bacterial expression library with this autonomous selection circuit results in the single-step and screening-free enrichment of genetic sequences coding for programmed enzymatic phenotypes. We demonstrate the potential of this approach for the nicking enzyme Nt.BstNBI (NBI). We applied autonomous selection conditions to enrich for thermostability or catalytic efficiency, manipulating up to 107 microcompartments and 5 × 105 variants at once. Full gene reads of the libraries using nanopore sequencing revealed detailed mutational activity landscapes, suggesting a key role of electrostatic interactions with DNA in the enzyme's turnover. The most beneficial mutations, identified after a single round of self-selection, provided variants with, respectively, 20 times and 3 °C increased activity and thermostability. Based on a modular molecular programming architecture, this approach does not require complex instrumentation and can be repurposed for other enzymes, including those that are not related to DNA chemistry.

A rapid and standardized workflow for functional assessment of bacterial biosensors in fecal samples

Gut metabolites are pivotal mediators of host-microbiome interactions and provide an important window on human physiology and disease. However, current methods to monitor gut metabolites rely on heavy and expensive technologies such as liquid chromatography-mass spectrometry (LC-MS). In that context, robust, fast, field-deployable, and cost-effective strategies for monitoring fecal metabolites would support large-scale functional studies and routine monitoring of metabolites biomarkers associated with pathological conditions. Living cells are an attractive option to engineer biosensors due to their ability to detect and process many environmental signals and their self-replicating nature. Here we optimized a workflow for feces processing that supports metabolite detection using bacterial biosensors. We show that simple centrifugation and filtration steps remove host microbes and support reproducible preparation of a physiological-derived media retaining important characteristics of human feces, such as matrix effects and endogenous metabolites. We measure the performance of bacterial biosensors for benzoate, lactate, anhydrotetracycline, and bile acids, and find that they are highly sensitive to fecal matrices. However, encapsulating the bacteria in hydrogel helps reduce this inhibitory effect. Sensitivity to matrix effects is biosensor-dependent but also varies between individuals, highlighting the need for case-by-case optimization for biosensors’ operation in feces. Finally, by detecting endogenous bile acids, we demonstrate that bacterial biosensors could be used for future metabolite monitoring in feces. This work lays the foundation for the optimization and use of bacterial biosensors for fecal metabolites monitoring. In the future, our method could also allow rapid pre-prototyping of engineered bacteria designed to operate in the gut, with applications to in situ diagnostics and therapeutics.

PeroxiHUB: A Modular Cell-Free Biosensing Platform Using H2O2 as Signal Integrator

Cell-free systems have great potential for delivering robust, inexpensive, and field-deployable biosensors. Many cell-free biosensors rely on transcription factors responding to small molecules, but their discovery and implementation still remain challenging. Here we report the engineering of PeroxiHUB, an optimized H₂O₂-centered sensing platform supporting cell-free detection of different metabolites. H₂O₂ is a central metabolite and a byproduct of numerous enzymatic reactions. PeroxiHUB uses enzymatic transducers to convert metabolites of interest into H₂O₂, enabling rapid reprogramming of sensor specificity using alternative transducers. We first screen several transcription factors and optimize OxyR for the transcriptional response to H₂O₂ in a cell-free system, highlighting the need for preincubation steps to obtain suitable signal-to-noise ratios. We then demonstrate modular detection of metabolites of clinical interest─lactate, sarcosine, and choline─using different transducers mined via a custom retrosynthesis workflow publicly available on the SynBioCAD Galaxy portal. We find that expressing the transducer during the preincubation step is crucial for optimal sensor operation. We then show that different reporters can be connected to PeroxiHUB, providing high adaptability for various applications. Finally, we demonstrate that a peroxiHUB lactate biosensor can detect endogenous levels of this metabolite in clinical samples. Given the wide range of enzymatic reactions producing H₂O₂, the PeroxiHUB platform will support cell-free detection of a large number of metabolites in a modular and scalable fashion.

Strategies for Improving Small-Molecule Biosensors in Bacteria

In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.

Cell-Free Biosensors and AI Integration

Cell-free biosensors hold a great potential as alternatives for traditional analytical chemistry methods providing low-cost low-resource measurement of specific chemicals. However, their large-scale use is limited by the complexity of their development.In this chapter, we present a standard methodology based on computer-aided design (CAD ) tools that enables fast development of new cell-free biosensors based on target molecule information transduction and reporting through metabolic and genetic layers, respectively. Such systems can then be repurposed to represent complex computational problems, allowing defined multiplex sensing of various inputs and integration of artificial intelligence in synthetic biological systems.

Modular tuning engineering and versatile applications of genetically encoded biosensors

Genetically encoded biosensors have a diverse range of detectable signals and potential applications in many fields, including metabolism control and high-throughput screening. Their ability to be used in situ with minimal interference to the bioprocess of interest could revolutionize synthetic biology and microbial cell factories. The performance and functions of these biosensors have been extensively studied and have been rapidly improved. We review here current biosensor tuning strategies and attempt to unravel how to obtain ideal biosensor functions through experimental adjustments. Strategies for expanding the biosensor input signals that increases the number of detectable compounds have also been summarized. Finally, different output signals and their practical requirements for biotechnology and biomedical applications and environmental safety concerns have been analyzed. This in-depth review of the responses and regulation mechanisms of genetically encoded biosensors will assist to improve their design and optimization in various application scenarios.

The Role of Metabolic Engineering Technologies for the Production of Fatty Acids in Yeast

Simple Summary

Metabolic engineering involves the sustainable production of high-value products. E. coli and yeast, in particular, are used for such processes. Using metabolic engineering, the biosynthetic pathways of these cells are altered to obtain a high production of desired products. Fatty acids (FAs) and their derivatives are products produced using metabolic engineering. However, classical methods used for engineering yeast metabolic pathways for the production of fatty acids and their derivatives face problems such as the low supply of key precursors and product tolerance. This review introduces the different ways FAs are being produced in E. coli and yeast and the genetic manipulations for enhanced production of FAs. The review also summarizes the latest techniques (i.e., CRISPR–Cas and synthetic biology) for developing FA-producing yeast cell factories.

Abstract

Metabolic engineering is a cutting-edge field that aims to produce simple, readily available, and inexpensive biomolecules by applying different genetic engineering and molecular biology techniques. Fatty acids (FAs) play an important role in determining the physicochemical properties of membrane lipids and are precursors of biofuels. Microbial production of FAs and FA-derived biofuels has several advantages in terms of sustainability and cost. Conventional yeast Saccharomyces cerevisiae is one of the models used for FA synthesis. Several genetic manipulations have been performed to enhance the citrate accumulation and its conversation into acetyl-CoA, a precursor for FA synthesis. Success has been achieved in producing different chemicals, including FAs and their derivatives, through metabolic engineering. However, several hurdles such as slow growth rate, low oleaginicity, and cytotoxicity are still need to be resolved. More robust research needs to be conducted on developing microbes capable of resisting diverse environments, chemicals, and cost-effective feed requirements. Redesigning microbes to produce FAs with cutting-edge synthetic biology and CRISPR techniques can solve these problems. Here, we reviewed the technological progression of metabolic engineering techniques and genetic studies conducted on S. cerevisiae, making it suitable as a model organism and a great candidate for the production of biomolecules, especially FAs.

Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models

BACKGROUND

Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology.

OBJECTIVE

In principle, we can perform a hand-on checking (Manual Curation). However, this is a challenging task due to the high number of combinations of pairs of nodes (possible metabolic reactions).

RESULTS

The CPTML linear model obtained using the LDA algorithm is able to discriminate nodes (metabolites) with the correct assignation of reactions from incorrect nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series.

METHODS

In this work, we used Combinatorial Perturbation Theory and Machine Learning techniques to seek a CPTML model for MRNs >40 organisms compiled by Barabasis' group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms.

CONCLUSION

Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens the door to the study of MRNs of multiple organisms using PTML models.

Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix

The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.

Compartmentalizing Cell-Free Systems: Toward Creating Life-Like Artificial Cells and Beyond

Building an artificial cell is a research area that is rigorously studied in the field of synthetic biology. It has brought about much attention with the aim of ultimately constructing a natural cell-like structure. In particular, with the more mature cell-free platforms and various compartmentalization methods becoming available, achieving this aim seems not far away. In this review, we discuss the various types of artificial cells capable of hosting several cellular functions. Different compartmental boundaries and the mature and evolving technologies that are used for compartmentalization are examined, and exciting recent advances that overcome or have the potential to address current challenges are discussed. Ultimately, we show how compartmentalization and cell-free systems have, and will, come together to fulfill the goal to assemble a fully synthetic cell that displays functionality and complexity as advanced as that in nature. The development of such artificial cell systems will offer insight into the fundamental study of evolutionary biology and the sea of applications as a result. Although several challenges remain, emerging technologies such as artificial intelligence also appear to help pave the way to address them and achieve the ultimate goal.

Rational programming of history-dependent logic in cellular populations

Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare.

Alternative Strategies for Microbial Remediation of Pollutants via Synthetic Biology

Continuous contamination of the environment with xenobiotics and related recalcitrant compounds has emerged as a serious pollution threat. Bioremediation is the key to eliminating persistent contaminants from the environment. Traditional bioremediation processes show limitations, therefore it is necessary to discover new bioremediation technologies for better results. In this review we provide an outlook of alternative strategies for bioremediation via synthetic biology, including exploring the prerequisites for analysis of research data for developing synthetic biological models of microbial bioremediation. Moreover, cell coordination in synthetic microbial community, cell signaling, and quorum sensing as engineered for enhanced bioremediation strategies are described, along with promising gene editing tools for obtaining the host with target gene sequences responsible for the degradation of recalcitrant compounds. The synthetic genetic circuit and two-component regulatory system (TCRS)-based microbial biosensors for detection and bioremediation are also briefly explained. These developments are expected to increase the efficiency of bioremediation strategies for best results.

A Förster Resonance Energy Transfer-Based Ratiometric Sensor with the Allosteric Transcription Factor TetR

A recent description of an antibody-free assay is significantly extended for small molecule analytes using allosteric transcription factors (aTFs) and Förster resonance energy transfer (FRET). The FRET signal indicates the differential binding of an aTF-DNA pair with a dose-dependent response to its effector molecule, i.e., the analyte. The new sensors described here, based on the well-characterized aTF TetR, demonstrate several new features of the assay approach: 1) the generalizability of the sensors to additional aTF-DNA-analyte systems, 2) sensitivity modulation through the choice of donor fluorophore (quantum dots or fluorescent proteins, FPs), and 3) sensor tuning using aTF variants with differing aTF-DNA binding affinities. While all of these modular sensors self-assemble, the design reported here based on a recombinant aTF-FP chimera with commercially available dye-labeled DNA uses readily accessible sensor components to facilitate easy adoption of the sensing approach by the broader community.

Reinforcement Learning for Bioretrosynthesis

Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bioretrosynthesis space using an artificial intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden data set of 20 manually curated experimental pathways as well as on a larger data set of 152 successful metabolic engineering projects. Moreover, we provide a novel feature that suggests potential media supplements to complement the enzymatic synthesis plan.

Organism Engineering for the Bioproduction of the Triaminotrinitrobenzene (TATB) Precursor Phloroglucinol (PG)

Organism engineering requires the selection of an appropriate chassis, editing its genome, combining traits from different source species, and controlling genes with synthetic circuits. When a strain is needed for a new target objective, for example, to produce a chemical-of-need, the best strains, genes, techniques, software, and expertise may be distributed across laboratories. Here, we report a project where we were assigned phloroglucinol (PG) as a target, and then combined unique capabilities across the United States Army, Navy, and Air Force service laboratories with the shared goal of designing an organism to produce this molecule. In addition to the laboratory strain Escherichia coli, organisms were screened from soil and seawater. Putative PG-producing enzymes were mined from a strain bank of bacteria isolated from aircraft and fuel depots. The best enzyme was introduced into the ocean strain Marinobacter atlanticus CP1 with its genome edited to redirect carbon flux from natural fatty acid ester (FAE) production. PG production was also attempted in Bacillus subtilis and Clostridium acetobutylicum. A genetic circuit was constructed in E. coli that responds to PG accumulation, which was then ported to an in vitro paper-based system that could serve as a platform for future low-cost strain screening or for in-field sensing. Collectively, these efforts show how distributed biotechnology laboratories with domain-specific expertise can be marshalled to quickly provide a solution for a targeted organism engineering project, and highlights data and material sharing protocols needed to accelerate future efforts.

Biosensor-based enzyme engineering approach applied to psicose biosynthesis

Bioproduction of chemical compounds is of great interest for modern industries, as it reduces their production costs and ecological impact. With the use of synthetic biology, metabolic engineering and enzyme engineering tools, the yield of production can be improved to reach mass production and cost-effectiveness expectations. In this study, we explore the bioproduction of D-psicose, also known as D-allulose, a rare non-toxic sugar and a sweetener present in nature in low amounts. D-psicose has interesting properties and seemingly the ability to fight against obesity and type 2 diabetes. We developed a biosensor-based enzyme screening approach as a tool for enzyme selection that we benchmarked with the Clostridium cellulolyticum D-psicose 3-epimerase for the production of D-psicose from D-fructose. For this purpose, we constructed and characterized seven psicose responsive biosensors based on previously uncharacterized transcription factors and either their predicted promoters or an engineered promoter. In order to standardize our system, we created the Universal Biosensor Chassis, a construct with a highly modular architecture that allows rapid engineering of any transcription factor-based biosensor. Among the seven biosensors, we chose the one displaying the most linear behavior and the highest increase in fluorescence fold change. Next, we generated a library of D-psicose 3-epimerase mutants by error-prone PCR and screened it using the biosensor to select gain of function enzyme mutants, thus demonstrating the framework's efficiency.

Metabolic perceptrons for neural computing in biological systems

Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a different approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model fitted on experimental data, we finally implement two four-input perceptrons for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing.

Reverse Engineering of an Aspirin-Responsive Transcriptional Regulator in Escherichia coli

Bacterial transcription factors (TFs) are key devices for the engineering of complex circuits in many biotechnological applications, yet there are few well-characterized inducer-responsive TFs that could be used in the context of an animal or human host. We have deciphered the inducer recognition mechanism of two AraC/XylS regulators from Pseudomonas putida (BenR and XylS) for creating a novel expression system responsive to acetyl salicylate (i.e., aspirin). Using protein homology modeling and molecular docking with the cognate inducer benzoate and a suite of chemical analogues, we identified the conserved binding pocket of BenR and XylS. By means of site-directed mutagenesis, we identified a single amino acid position required for efficient inducer recognition and transcriptional activation. Whereas this modification in BenR abolishes protein activity, in XylS, it increases the response to several inducers, including acetyl salicylic acid, to levels close to those achieved by the canonical inducer. Moreover, by constructing chimeric proteins with swapped N-terminal domains, we created novel regulators with mixed promoter and inducer recognition profiles. As a result, a collection of engineered TFs was generated with an enhanced response to benzoate, 3-methylbenzoate, 2-methylbenzoate, 4-methylbenzoate, salicylic acid, aspirin, and acetylsalicylic acid molecules for eliciting gene expression in E. coli.

Future Trends in Synthetic Biology-A Report

Leading researchers working on synthetic biology and its applications gathered at the University of Edinburgh in May 2018 to discuss the latest challenges and opportunities in the field. In addition to the potential socio-economic benefits of synthetic biology, they also examined the ethics and security risks arising from the development of these technologies. Speakers from industry, academia and not-for-profit organizations presented their vision for the future of the field and provided guidance to funding and regulatory bodies to ensure that synthetic biology research is carried out responsibly and can realize its full potential. This report aims to capture the collective views and recommendations that emerged from the discussions that took place. The meeting was held under the Chatham House Rule (i.e., a private invite-only meeting where comments can be freely used but not attributed) to promote open discussion; the findings and quotes included in the report are therefore not attributed to individuals. The goal of the meeting was to identify research priorities and bottlenecks. It also provided the opportunity to discuss how best to manage risk and earn public acceptance of this emerging and disruptive technology.

C1 Compound Biosensors: Design, Functional Study, and Applications

The microbial assimilation of one-carbon (C1) gases is a topic of interest, given that products developed using this pathway have the potential to act as promising substrates for the synthesis of valuable chemicals via enzymatic oxidation or C–C bonding. Despite extensive studies on C1 gas assimilation pathways, their key enzymes have yet to be subjected to high-throughput evolution studies on account of the lack of an efficient analytical tool for C1 metabolites. To address this challenging issue, we attempted to establish a fine-tuned single-cell–level biosensor system constituting a combination of transcription factors (TFs) and several C1-converting enzymes that convert target compounds to the ligand of a TF. This enzymatic conversion broadens the detection range of ligands by the genetic biosensor systems. In this study, we presented new genetic enzyme screening systems (GESSs) to detect formate, formaldehyde, and methanol from specific enzyme activities and pathways, named FA-GESS, Frm-GESS, and MeOH-GESS, respectively. All the biosensors displayed linear responses to their respective C1 molecules, namely, formate (1.0–250 mM), formaldehyde (1.0–50 μM), and methanol (5–400 mM), and they did so with high specificity. Consequently, the helper enzymes, including formaldehyde dehydrogenase and methanol dehydrogenase, were successfully combined to constitute new versatile combinations of the C1-biosensors.

Plug-and-play metabolic transducers expand the chemical detection space of cell-free biosensors

Cell-free transcription–translation systems have great potential for biosensing, yet the range of detectable chemicals is limited. Here we provide a workflow to expand the range of molecules detectable by cell-free biosensors through combining synthetic metabolic cascades with transcription factor-based networks. These hybrid cell-free biosensors have a fast response time, strong signal response, and a high dynamic range. In addition, they are capable of functioning in a variety of complex media, including commercial beverages and human urine, in which they can be used to detect clinically relevant concentrations of small molecules. This work provides a foundation to engineer modular cell-free biosensors tailored for many applications.

The range of chemicals detectable by cell-free systems is still limited. Here the authors combine metabolic cascades with transcription factor networks to detect small molecules in complex environments.

Building a minimal and generalizable model of transcription factor-based biosensors: Showcasing flavonoids

Progress in synthetic biology tools has transformed the way we engineer living cells. Applications of circuit design have reached a new level, offering solutions for metabolic engineering challenges that include developing screening approaches for libraries of pathway variants. The use of transcription-factor-based biosensors for screening has shown promising results, but the quantitative relationship between the sensors and the sensed molecules still needs more rational understanding. Herein, we have successfully developed a novel biosensor to detect pinocembrin based on a transcriptional regulator. The FdeR transcription factor (TF), known to respond to naringenin, was combined with a fluorescent reporter protein. By varying the copy number of its plasmid and the concentration of the biosensor TF through a combinatorial library, different responses have been recorded and modeled. The fitted model provides a tool to understand the impact of these parameters on the biosensor behavior in terms of dose-response and time curves and offers guidelines to build constructs oriented to increased sensitivity and or ability of linear detection at higher titers. Our model, the first to explicitly take into account the impact of plasmid copy number on biosensor sensitivity using Hill-based formalism, is able to explain uncharacterized systems without extensive knowledge of the properties of the TF. Moreover, it can be used to model the response of the biosensor to different compounds (here naringenin and pinocembrin) with minimal parameter refitting.

In vivo biosensors: mechanisms, development, and applications

In vivo biosensors can recognize and respond to specific cellular stimuli. In recent years, biosensors have been increasingly used in metabolic engineering and synthetic biology, because they can be implemented in synthetic circuits to control the expression of reporter genes in response to specific cellular stimuli, such as a certain metabolite or a change in pH. There are many types of natural sensing devices, which can be generally divided into two main categories: protein-based and nucleic acid-based. Both can be obtained either by directly mining from natural genetic components or by engineering the existing genetic components for novel specificity or improved characteristics. A wide range of new technologies have enabled rapid engineering and discovery of new biosensors, which are paving the way for a new era of biotechnological progress. Here, we review recent advances in the design, optimization, and applications of in vivo biosensors in the field of metabolic engineering and synthetic biology.

A Modular Receptor Platform To Expand the Sensing Repertoire of Bacteria

Engineered bacteria promise to revolutionize diagnostics and therapeutics, yet many applications are precluded by the limited number of detectable signals. Here we present a general framework to engineer synthetic receptors enabling bacterial cells to respond to novel ligands. These receptors are activated via ligand-induced dimerization of a single-domain antibody fused to monomeric DNA-binding domains (split-DBDs). Using E. coli as a model system, we engineer both transmembrane and cytosolic receptors using a VHH for ligand detection and demonstrate the scalability of our platform by using the DBDs of two different transcriptional regulators. We provide a method to optimize receptor behavior by finely tuning protein expression levels and optimizing interdomain linker regions. Finally, we show that these receptors can be connected to downstream synthetic gene circuits for further signal processing. The general nature of the split-DBD principle and the versatility of antibody-based detection should support the deployment of these receptors into various hosts to detect ligands for which no receptor is found in nature.

A review of computational tools for design and reconstruction of metabolic pathways

Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.

Tailor-made transcriptional biosensors for optimizing microbial cell factories

Monitoring cellular behavior and eventually properly adapting cellular processes is key to handle the enormous complexity of today's metabolic engineering questions. Hence, transcriptional biosensors bear the potential to augment and accelerate current metabolic engineering strategies, catalyzing vital advances in industrial biotechnology. The development of such transcriptional biosensors typically starts with exploring nature's richness. Hence, in a first part, the transcriptional biosensor architecture and the various modi operandi are briefly discussed, as well as experimental and computational methods and relevant ontologies to search for natural transcription factors and their corresponding binding sites. In the second part of this review, various engineering approaches are reviewed to tune the main characteristics of these (natural) transcriptional biosensors, i.e., the response curve and ligand specificity, in view of specific industrial biotechnology applications, which is illustrated using success stories of transcriptional biosensor engineering.

Sensing new chemicals with bacterial transcription factors

Bacteria rely on allosteric transcription factors (aTFs) to sense a wide range of chemicals. The variety of effectors has contributed in making aTFs the most used input system in synthetic biological circuits. Considering their enabling role in biotechnology, an important question concerns the size of the chemical space that can potentially be detected by these biosensors. From digging into the ever changing repertoire of natural regulatory circuits, to advances in aTF engineering, we review here different strategies that are pushing the boundaries of this chemical space. We also review natural and synthetic cases of indirect sensing, where aTFs work in combination with metabolism to enable detection of new molecules.

SensiPath: computer-aided design of sensing-enabling metabolic pathways

Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design for medical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable. Here, we present the SensiPath web server, accessible at http://sensipath.micalis.fr SensiPath implements a strategy to enlarge the set of detectable compounds by screening for multi-step enzymatic transformations converting non-detectable compounds into detectable ones. The SensiPath approach is based on the encoding of reactions through signature descriptors to explore sensing-enabling metabolic pathways, which are putative biochemical transformations of the target compound leading to known effectors of transcription factors. In that way, SensiPath enlarges the design space by broadening the potential use of biosensors in synthetic biology applications.