Tuning the Performance of Synthetic Riboswitches using Machine Learning

RNA-Based Sensor Systems for Affordable Diagnostics in the Age of Pandemics

In the era of the COVID-19 pandemic, the significance of point-of-care (POC) diagnostic tools has become increasingly vital, driven by the need for quick and precise virus identification. RNA-based sensors, particularly toehold sensors, have emerged as promising candidates for POC detection systems due to their selectivity and sensitivity. Toehold sensors operate by employing an RNA switch that changes the conformation when it binds to a target RNA molecule, resulting in a detectable signal. This review focuses on the development and deployment of RNA-based sensors for POC viral RNA detection with a particular emphasis on toehold sensors. The benefits and limits of toehold sensors are explored, and obstacles and future directions for improving their performance within POC detection systems are presented. The use of RNA-based sensors as a technology for rapid and sensitive detection of viral RNA holds great potential for effectively managing (dealing/coping) with present and future pandemics in resource-constrained settings.

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

Flipping the script: Understanding riboswitches from an alternative perspective

Riboswitches are broadly distributed regulatory elements most frequently found in the 5'-leader sequence of bacterial mRNAs that regulate gene expression in response to the binding of a small molecule effector. The occupancy status of the ligand-binding aptamer domain manipulates downstream information in the message that instructs the expression machinery. Currently, there are over 55 validated riboswitch classes, where each class is defined based on the identity of the ligand it binds and/or sequence and structure conservation patterns within the aptamer domain. This classification reflects an "aptamer-centric" perspective that dominates our understanding of riboswitches. In this review, we propose a conceptual framework that groups riboswitches based on the mechanism by which RNA manipulates information directly instructing the expression machinery. This scheme does not replace the established aptamer domain-based classification of riboswitches but rather serves to facilitate hypothesis-driven investigation of riboswitch regulatory mechanisms. Based on current bioinformatic, structural, and biochemical studies of a broad spectrum of riboswitches, we propose three major mechanistic groups: (1) "direct occlusion", (2) "interdomain docking", and (3) "strand exchange". We discuss the defining features of each group, present representative examples of riboswitches from each group, and illustrate how these RNAs couple small molecule binding to gene regulation. While mechanistic studies of the occlusion and docking groups have yielded compelling models for how these riboswitches function, much less is known about strand exchange processes. To conclude, we outline the limitations of our mechanism-based conceptual framework and discuss how critical information within riboswitch expression platforms can inform gene regulation.

Engineering U1-Based Tetracycline-Inducible Riboswitches to Control Gene Expression in Mammals

Synthetic riboswitches are promising regulatory devices due to their small size, lack of immunogenicity, and ability to fine-tune gene expression in the absence of exogenous trans-acting factors. Based on a gene inhibitory system developed at our lab, termed U1snRNP interference (U1i), we developed tetracycline (TC)-inducible riboswitches that modulate mRNA polyadenylation through selective U1 snRNP recruitment. First, we engineered different TC-U1i riboswitches, which repress gene expression unless TC is added, leading to inductions of gene expression of 3-to-4-fold. Second, we developed a technique called Systematic Evolution of Riboswitches by Exponential Enrichment (SEREX), to isolate riboswitches with enhanced U1 snRNP binding capacity and activity, achieving inducibilities of up to 8-fold. Interestingly, by multiplexing riboswitches we increased inductions up to 37-fold. Finally, we demonstrated that U1i-based riboswitches are dose-dependent and reversible and can regulate the expression of reporter and endogenous genes in culture cells and mouse models, resulting in attractive systems for gene therapy applications. Our work probes SEREX as a much-needed technology for the in vitro identification of riboswitches capable of regulating gene expression in vivo.

A journey with psychedelic mushrooms: From historical relevance to biology, cultivation, medicinal uses, biotechnology, and beyond

Psychedelic mushrooms containing psilocybin and related tryptamines have long been used for ethnomycological purposes, but emerging evidence points to the potential therapeutic value of these mushrooms to address modern neurological, psychiatric health, and related disorders. As a result, psilocybin containing mushrooms represent a re-emerging frontier for mycological, biochemical, neuroscience, and pharmacology research. This work presents crucial information related to traditional use of psychedelic mushrooms, as well as research trends and knowledge gaps related to their diversity and distribution, technologies for quantification of tryptamines and other tryptophan-derived metabolites, as well as biosynthetic mechanisms for their production within mushrooms. In addition, we explore the current state of knowledge for how psilocybin and related tryptamines are metabolized in humans and their pharmacological effects, including beneficial and hazardous human health implications. Finally, we describe opportunities and challenges for investigating the production of psychedelic mushrooms and metabolic engineering approaches to alter secondary metabolite profiles using biotechnology integrated with machine learning. Ultimately, this critical review of all aspects related to psychedelic mushrooms represents a roadmap for future research efforts that will pave the way to new applications and refined protocols.

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.

Switchable Fluorescent Light-Up Aptamers Based on Riboswitch Architectures

Fluorescent light-up RNA aptamers (FLAPs) such as Spinach or Mango can bind small fluorogens and activate their fluorescence. Here, we adopt a switching mechanism otherwise found in riboswitches and use it to engineer switchable FLAPs that can be activated or repressed by trigger oligonucleotides or small metabolites. The fluorophore binding pocket of the FLAPs comprises guanine (G) quadruplexes, whose critical nucleotides can be sequestered by corresponding anti-FLAP sequences, leading to an inactive conformation and thus preventing association with the fluorophore. We modified the FLAPs with designed toehold hairpins that carry either an anti-FLAP or an anti-anti-FLAP sequence within the loop region. The addition of an input RNA molecule triggers a toehold-mediated strand invasion process that refolds the FLAP into an active or inactive configuration. Several of our designs display close-to-zero leak signals and correspondingly high ON/OFF fluorescence ratios. We also modified purine aptamers to sequester a partial anti-FLAP or an anti-anti-FLAP sequence to control the formation of the fluorogen-binding conformation, resulting in FLAPs whose fluorescence is activated or deactivated in the presence of guanine or adenine. We demonstrate that switching modules can be easily combined to generate FLAPs whose fluorescence depends on several inputs with different types of input logic.

Machine learning for metabolic pathway optimization: A review

Optimizing the metabolic pathways of microbial cell factories is essential for establishing viable biotechnological production processes. However, due to the limited understanding of the complex setup of cellular machinery, building efficient microbial cell factories remains tedious and time-consuming. Machine learning (ML), a powerful tool capable of identifying patterns within large datasets, has been used to analyze biological datasets generated using various high-throughput technologies to build data-driven models for complex bioprocesses. In addition, ML can also be integrated with Design-Build-Test-Learn to accelerate development. This review focuses on recent ML applications in genome-scale metabolic model construction, multistep pathway optimization, rate-limiting enzyme engineering, and gene regulatory element designing. In addition, we have discussed some limitations of these methods as well as potential solutions.

Tapping the potential of synthetic riboswitches: reviewing the versatility of the tetracycline aptamer

Synthetic riboswitches are a versatile class of regulatory elements that are becoming increasingly established in synthetic biology applications. They are characterized by their compact size and independence from auxiliary protein factors. While naturally occurring riboswitches were mostly discovered in bacteria, synthetic riboswitches have been designed for all domains of life. Published design strategies far exceed the number of riboswitches found in nature. A core element of any riboswitch is a binding domain, called an aptamer, which is characterized by high specificity and affinity for its ligand. Aptamers can be selected de novo, allowing the design of synthetic riboswitches against a broad spectrum of targets. The tetracycline aptamer has proven to be well suited for riboswitch engineering. Since its selection, it has been used in a variety of applications and is considered to be well established and characterized. Using the tetracycline aptamer as an example, we aim to discuss a large variety of design approaches for synthetic riboswitch engineering and their application. We aim to demonstrate the versatility of riboswitches in general and the high potential of synthetic RNA devices for creating new solutions in both the scientific and medical fields.

Nucleic acid strand displacement - from DNA nanotechnology to translational regulation

Nucleic acid strand displacement reactions involve the competition of two or more DNA or RNA strands of similar sequence for binding to a complementary strand, and facilitate the isothermal replacement of an incumbent strand by an invader. The process can be biased by augmenting the duplex comprising the incumbent with a single-stranded extension, which can act as a toehold for a complementary invader. The toehold gives the invader a thermodynamic advantage over the incumbent, and can be programmed as a unique label to activate a specific strand displacement process. Toehold-mediated strand displacement processes have been extensively utilized for the operation of DNA-based molecular machines and devices as well as for the design of DNA-based chemical reaction networks. More recently, principles developed initially in the context of DNA nanotechnology have been applied for the de novo design of gene regulatory switches that can operate inside living cells. The article specifically focuses on the design of RNA-based translational regulators termed toehold switches. Toehold switches utilize toehold-mediated strand invasion to either activate or repress translation of an mRNA in response to the binding of a trigger RNA molecule. The basic operation principles of toehold switches will be discussed as well as their applications in sensing and biocomputing. Finally, strategies for their optimization will be described as well as challenges for their operation in vivo.

Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications

Genetically encoded biosensors are the vital components of synthetic biology and metabolic engineering, as they are regarded as powerful devices for the dynamic control of genotype metabolism and evolution/screening of desirable phenotypes. This review summarized the recent advances in the construction and applications of different genetically encoded biosensors, including fluorescent protein-based biosensors, nucleic acid-based biosensors, allosteric transcription factor-based biosensors and two-component system-based biosensors. First, the construction frameworks of these biosensors were outlined. Then, the recent progress of biosensor applications in creating versatile microbial cell factories for the bioproduction of high-value chemicals was summarized. Finally, the challenges and prospects for constructing robust and sophisticated biosensors were discussed. This review provided theoretical guidance for constructing genetically encoded biosensors to create desirable microbial cell factories for sustainable bioproduction.

Recent advances in machine learning applications in metabolic engineering

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data

Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection (in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.

Deep Learning Concepts and Applications for Synthetic Biology

Synthetic biology has a natural synergy with deep learning. It can be used to generate large data sets to train models, for example by using DNA synthesis, and deep learning models can be used to inform design, such as by generating novel parts or suggesting optimal experiments to conduct. Recently, research at the interface of engineering biology and deep learning has highlighted this potential through successes including the design of novel biological parts, protein structure prediction, automated analysis of microscopy data, optimal experimental design, and biomolecular implementations of artificial neural networks. In this review, we present an overview of synthetic biology-relevant classes of data and deep learning architectures. We also highlight emerging studies in synthetic biology that capitalize on deep learning to enable novel understanding and design, and discuss challenges and future opportunities in this space.

Riboswitch-inspired toehold riboregulators for gene regulation in Escherichia coli

Regulatory RNA molecules have been widely investigated as components for synthetic gene circuits, complementing the use of protein-based transcription factors. Among the potential advantages of RNA-based gene regulators are their comparatively simple design, sequence-programmability, orthogonality, and their relatively low metabolic burden. In this work, we developed a set of riboswitch-inspired riboregulators in Escherichia coli that combine the concept of toehold-mediated strand displacement (TMSD) with the switching principles of naturally occurring transcriptional and translational riboswitches. Specifically, for translational activation and repression, we sequestered anti-anti-RBS or anti-RBS sequences, respectively, inside the loop of a stable hairpin domain, which is equipped with a single-stranded toehold region at its 5' end and is followed by regulated sequences on its 3' side. A trigger RNA binding to the toehold region can invade the hairpin, inducing a structural rearrangement that results in translational activation or deactivation. We also demonstrate that TMSD can be applied in the context of transcriptional regulation by switching RNA secondary structure involved in Rho-dependent termination. Our designs expand the repertoire of available synthetic riboregulators by a set of RNA switches with no sequence limitation, which should prove useful for the development of robust genetic sensors and circuits.

Attenuator LRR - a regulatory tool for modulating gene expression in Gram-positive bacteria

With the rapid development of synthetic biology in recent years, particular attention has been paid to RNA devices, especially riboswitches, because of their significant and diverse regulatory roles in prokaryotic and eukaryotic cells. Due to the limited performance and context-dependence of riboswitches, only a few of them (such as theophylline, tetracycline and ciprofloxacin riboswitches) have been utilized as regulatory tools in biotechnology. In the present study, we demonstrated that a ribosome-dependent ribo-regulator, LRR, discovered in our previous work, exhibits an attractive regulatory performance. Specifically, it offers a 60-fold change in expression in the presence of retapamulin and a low level of leaky expression of about 1-2% without antibiotics. Moreover, LRR can be combined with different promoters and performs well in Bacillus thuringiensis, B. cereus, B. amyloliquefaciens, and B. subtilis. Additionally, LRR also functions in the Gram-negative bacterium Escherichia coli. Furthermore, we demonstrate its ability to control melanin metabolism in B. thuringiensis BMB171. Our results show that LRR can be applied to regulate gene expression, construct genetic circuits and tune metabolic pathways, and has great potential for many applications in synthetic biology.

Improving the performance of machine learning models for biotechnology: The quest for deus ex machina

Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements and predictive tools in the field. Robust prediction of the behavior of microbial cell factories and production processes not only greatly increases our understanding of the function of such systems, but also provides significant savings of development time. However, many pitfalls when modeling biological data - bad fit, noisy data, model instability, low data quantity and imbalances in the data - cause models to suffer in their performance. Here we provide an accessible, in-depth analysis on the problems created by these pitfalls, as well as means of their detection and mediation, with a focus on supervised learning. Assessing the state of the art, we show that, currently, in-depth analyses of model performance are often absent and must be improved. This review provides a toolbox for the analysis of model robustness and performance, and simultaneously proposes a standard for the community to facilitate future work. It is further accompanied by an interactive online tutorial on the discussed issues.

Bioinformational trends in grape and wine biotechnology

The creative destruction caused by the coronavirus pandemic is yielding immense opportunity for collaborative innovation networks. The confluence of biosciences, information sciences, and the engineering of biology, is unveiling promising bioinformational futures for a vibrant and sustainable bioeconomy. Bioinformational engineering, underpinned by DNA reading, writing, and editing technologies, has become a beacon of opportunity in a world paralysed by uncertainty. This article draws on lessons from the current pandemic and previous agricultural blights, and explores bioinformational research directions aimed at future-proofing the grape and wine industry against biological shocks from global blights and climate change.

Sequence-independent RNA sensing and DNA targeting by a split domain CRISPR-Cas12a gRNA switch

CRISPR technologies increasingly require spatiotemporal and dosage control of nuclease activity. One promising strategy involves linking nuclease activity to a cell's transcriptional state by engineering guide RNAs (gRNAs) to function only after complexing with a ‘trigger’ RNA. However, standard gRNA switch designs do not allow independent selection of trigger and guide sequences, limiting gRNA switch application. Here, we demonstrate the modular design of Cas12a gRNA switches that decouples selection of these sequences. The 5′ end of the Cas12a gRNA is fused to two distinct and non-overlapping domains: one base pairs with the gRNA repeat, blocking formation of a hairpin required for Cas12a recognition; the other hybridizes to the RNA trigger, stimulating refolding of the gRNA repeat and subsequent gRNA-dependent Cas12a activity. Using a cell-free transcription-translation system and Escherichia coli, we show that designed gRNA switches can respond to different triggers and target different DNA sequences. Modulating the length and composition of the sensory domain altered gRNA switch performance. Finally, gRNA switches could be designed to sense endogenous RNAs expressed only under specific growth conditions, rendering Cas12a targeting activity dependent on cellular metabolism and stress. Our design framework thus further enables tethering of CRISPR activities to cellular states.

Aptamers, Riboswitches, and Ribozymes in S. cerevisiae Synthetic Biology

Among noncoding RNA sequences, riboswitches and ribozymes have attracted the attention of the synthetic biology community as circuit components for translation regulation. When fused to aptamer sequences, ribozymes and riboswitches are enabled to interact with chemicals. Therefore, protein synthesis can be controlled at the mRNA level without the need for transcription factors. Potentially, the use of chemical-responsive ribozymes/riboswitches would drastically simplify the design of genetic circuits. In this review, we describe synthetic RNA structures that have been used so far in the yeast Saccharomyces cerevisiae. We present their interaction mode with different chemicals (e.g., theophylline and antibiotics) or proteins (such as the RNase III) and their recent employment into clustered regularly interspaced short palindromic repeats–CRISPR-associated protein 9 (CRISPR-Cas) systems. Particular attention is paid, throughout the whole paper, to their usage and performance into synthetic gene circuits.

Inducible nuclear import by TetR aptamer-controlled 3' splice site selection

Correct cellular localization is essential for the function of many eukaryotic proteins and hence cell physiology. Here, we present a synthetic genetic device that allows the control of nuclear and cytosolic localization based on controlled alternative splicing in human cells. The device is based on the fact that an alternative 3' splice site is located within a TetR aptamer that in turn is positioned between the branch point and the canonical splice site. The novel splice site is only recognized when the TetR repressor is bound. Addition of doxycycline prevents TetR aptamer binding and leads to recognition of the canonical 3' splice site. It is thus possible to produce two independent splice isoforms. Since the terminal loop of the aptamer may be replaced with any sequence of choice, one of the two isoforms may be extended by the respective sequence of choice depending on the presence of doxycycline. In a proof-of-concept study, we fused a nuclear localization sequence to a cytosolic target protein, thus directing the protein into the nucleus. However, the system is not limited to the control of nuclear localization. In principle, any target sequence can be integrated into the aptamer, allowing not only the production of a variety of different isoforms on demand, but also to study the function of mislocalized proteins. Moreover, it also provides a valuable tool for investigating the mechanism of alternative splicing in human cells.

Machine learning for metabolic engineering: A review

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

Sequence-to-function deep learning frameworks for engineered riboregulators

While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules. Toehold switches, which are programmable nucleic acid sensors, face an analogous design bottleneck; our limited understanding of how sequence impacts functionality often necessitates expensive, time-consuming screens to identify effective switches. Here, we introduce Sequence-based Toehold Optimization and Redesign Model (STORM) and Nucleic-Acid Speech (NuSpeak), two orthogonal and synergistic deep learning architectures to characterize and optimize toeholds. Applying techniques from computer vision and natural language processing, we 'un-box' our models using convolutional filters, attention maps, and in silico mutagenesis. Through transfer-learning, we redesign sub-optimal toehold sensors, even with sparse training data, experimentally validating their improved performance. This work provides sequence-to-function deep learning frameworks for toehold selection and design, augmenting our ability to construct potent biological circuit components and precision diagnostics.

A deep learning approach to programmable RNA switches

Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R2 = 0.43-0.70) previous state-of-the-art thermodynamic and kinetic models (R2 = 0.04-0.15) and allow for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This work shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology.

Metabolic pathway engineering: Perspectives and applications

BACKGROUND

Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION

In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Biosystems Design by Machine Learning

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

Riboflow: Using Deep Learning to Classify Riboswitches With ∼99% Accuracy

Riboswitches are cis-regulatory genetic elements that use an aptamer to control gene expression. Specificity to cognate ligand and diversity of such ligands have expanded the functional repertoire of riboswitches to mediate mounting apt responses to sudden metabolic demands and signal changes in environmental conditions. Given their critical role in microbial life, riboswitch characterisation remains a challenging computational problem. Here we have addressed the issue with advanced deep learning frameworks, namely convolutional neural networks (CNN), and bidirectional recurrent neural networks (RNN) with Long Short-Term Memory (LSTM). Using a comprehensive dataset of 32 ligand classes and a stratified train-validate-test approach, we demonstrated the accurate performance of both the deep learning models (CNN and RNN) relative to conventional hyperparameter-optimized machine learning classifiers on all key performance metrics, including the ROC curve analysis. In particular, the bidirectional LSTM RNN emerged as the best-performing learning method for identifying the ligand-specificity of riboswitches with an accuracy >0.99 and macro-averaged F-score of 0.96. An additional attraction is that the deep learning models do not require prior feature engineering. A dynamic update functionality is built into the models to factor for the constant discovery of new riboswitches, and extend the predictive modeling to new classes. Our work would enable the design of genetic circuits with custom-tuned riboswitch aptamers that would effect precise translational control in synthetic biology. The associated software is available as an open-source Python package and standalone resource for use in genome annotation, synthetic biology, and biotechnology workflows.

A Small-Molecule-Responsive Riboswitch Enables Conditional Induction of Viral Vector-Mediated Gene Expression in Mice

Adeno-associated viral (AAV) vector-mediated gene therapy holds great potential for future medical applications. However, to facilitate safer and broader applicability and to enable patient-centric care, therapeutic protein expression should be controllable, ideally by an orally administered drug. The use of protein-based systems is considered rather undesirable, due to potential immunogenicity and the limited coding space of AAV. Ligand-dependent riboswitches, in contrast, are small and characterized by an attractive mode-of-action based on mRNA-self-cleavage, independent of coexpressed foreign protein. While a promising approach, switches available to date have only shown moderate potency in animals. In particular, ON-switches that induce transgene expression upon ligand administration so far have achieved rather disappointing results. Here we present the utilization of the previously described tetracycline-dependent ribozyme K19 for controlling AAV-mediated transgene expression in mice. Using this tool switch, we provide first proof for the feasibility of clinically desired key features, including multiorgan functionality, potent regulation (up to 15-fold induction), reversibility, and the possibility to fine-tune and repeatedly induce expression. The systematic assessment of ligand and reporter protein plasma levels further enabled the characterization of pharmacokinetic-pharmacodynamic relationships. Thus, our results strongly support future efforts to develop engineered riboswitches for applications in clinical gene therapy.

Contemporary Tools for Regulating Gene Expression in Bacteria

Insights from novel mechanistic paradigms in gene expression control have led to the development of new gene expression systems for bioproduction, control, and sensing applications. Coupled with a greater understanding of synthetic burden and modern creative biodesign approaches, contemporary bacterial gene expression tools and systems are emerging that permit fine-tuning of expression, enabling greater predictability and maximisation of specific productivity, while minimising deleterious effects upon cell viability. These advances have been achieved by using a plethora of regulatory tools, operating at all levels of the so-called 'central dogma' of molecular biology. In this review, we discuss these gene regulation tools in the context of their design, prototyping, integration into expression systems, and biotechnological application.