Engineered Nucleotide Chemicapacitive Microsensor Array Augmented with Physics-Guided Machine Learning for High-Throughput Screening of Cannabidiol.

The recent legalization of cannabidiol (CBD) to treat neurological conditions such as epilepsy has sparked rising interest across global pharmaceuticals and synthetic biology industries to engineer microbes for sustainable synthetic production of medicinal CBD. Since the process involves screening large amounts of samples, the main challenge is often associated with the conventional screening platform that is time consuming, and laborious with high operating costs. Here, a portable, high-throughput Aptamer-based BioSenSing System (ABS3 ) is introduced for label-free, low-cost, fully automated, and highly accurate CBD concentrations' classification in a complex biological environment. The ABS3 comprises an array of interdigitated microelectrode sensors, each functionalized with different engineered aptamers. To further empower the functionality of the ABS3 , unique electrochemical features from each sensor are synergized using physics-guided multidimensional analysis. The capabilities of this ABS3 are demonstrated by achieving excellent CBD concentrations' classification with a high prediction accuracy of 99.98% and a fast testing time of 22 µs per testing sample using the optimized random forest (RF) model. It is foreseen that this approach will be the key to the realistic transformation from fundamental research to system miniaturization for diagnostics of disease biomarkers and drug development in the field of chemical/bioanalytics.

A model-driven approach towards rational microbial bioprocess optimization.

Due to sustainability concerns, bio-based production capitalizing on microbes as cell factories is in demand to synthesize valuable products. Nevertheless, the nonhomogenous variations of the extracellular environment in bioprocesses often challenge the biomass growth and the bioproduction yield. To enable a more rational bioprocess optimization, we have established a model-driven approach that systematically integrates experiments with modeling, executed from flask to bioreactor scale, and using ferulic acid to vanillin bioconversion as a case study. The impacts of mass transfer and aeration on the biomass growth and bioproduction performances were examined using minimal small-scale experiments. An integrated model coupling the cell factory kinetics with the three-dimensional computational hydrodynamics of bioreactor was developed to better capture the spatiotemporal distributions of bioproduction. Full-factorial predictions were then performed to identify the desired operating conditions. A bioconversion yield of 94% was achieved, which is one of the highest for recombinant Escherichia coli using ferulic acid as the precursor.

Blue light-mediated transcriptional activation and repression of gene expression in bacteria.

Light-regulated modules offer unprecedented new ways to control cellular behavior in precise spatial and temporal resolution. The availability of such tools may dramatically accelerate the progression of synthetic biology applications. Nonetheless, current optogenetic toolbox of prokaryotes has potential issues such as lack of rapid and switchable control, less portable, low dynamic expression and limited parts. To address these shortcomings, we have engineered a novel bidirectional promoter system for Escherichia coli that can be induced or repressed rapidly and reversibly using the blue light dependent DNA-binding protein EL222. We demonstrated that by modulating the dosage of light pulses or intensity we could control the level of gene expression precisely. We show that both light-inducible and repressible system can function in parallel with high spatial precision in a single cell and can be switched stably between ON- and OFF-states by repetitive pulses of blue light. In addition, the light-inducible and repressible expression kinetics were quantitatively analysed using a mathematical model. We further apply the system, for the first time, to optogenetically synchronize two receiver cells performing different logic behaviors over time using blue light as a molecular clock signal. Overall, our modular approach layers a transformative platform for next-generation light-controllable synthetic biology systems in prokaryotes.

Layering genetic circuits to build a single cell, bacterial half adder.

BACKGROUND: Gene regulation in biological systems is impacted by the cellular and genetic context-dependent effects of the biological parts which comprise the circuit. Here, we have sought to elucidate the limitations of engineering biology from an architectural point of view, with the aim of compiling a set of engineering solutions for overcoming failure modes during the development of complex, synthetic genetic circuits. RESULTS: Using a synthetic biology approach that is supported by computational modelling and rigorous characterisation, AND, OR and NOT biological logic gates were layered in both parallel and serial arrangements to generate a repertoire of Boolean operations that include NIMPLY, XOR, half adder and half subtractor logics in a single cell. Subsequent evaluation of these near-digital biological systems revealed critical design pitfalls that triggered genetic context-dependent effects, including 5' UTR interferences and uncontrolled switch-on behaviour of the supercoiled σ54 promoter. In particular, the presence of seven consecutive hairpins immediately downstream of the promoter transcription start site severely impeded gene expression. CONCLUSIONS: As synthetic biology moves forward with greater focus on scaling the complexity of engineered genetic circuits, studies which thoroughly evaluate failure modes and engineering solutions will serve as important references for future design and development of synthetic biological systems. This work describes a representative case study for the debugging of genetic context-dependent effects through principles elucidated herein, thereby providing a rational design framework to integrate multiple genetic circuits in a single prokaryotic cell.

Apelin attenuates oxidative stress in human adipocytes.

It has been recently recognized that the increased oxidative stress (ROS overproduction) in obese condition is a key contributor to the pathogenesis of obesity-associated metabolic diseases. Apelin is an adipocytokine secreted by adipocytes, and known for its anti-obesity and anti-diabetic properties. In obesity, both oxidative stress and plasma level of apelin are increased. However, the regulatory roles of apelin on oxidative stress in adipocytes remain unknown. In the present study, we provide evidence that apelin, through its interaction with apelin receptor (APJ), suppresses production and release of reactive oxygen species (ROS) in adipocytes. This is further supported by the observations that apelin promotes the expression of anti-oxidant enzymes via MAPK kinase/ERK and AMPK pathways, and suppresses the expression of pro-oxidant enzyme via AMPK pathway. We further demonstrate that apelin is able to relieve oxidative stress-induced dysregulations of the expression of anti- and pro-oxidant enzymes, mitochondrial biogenesis and function, as well as release of pro- and anti-inflammatory adipocytokines. This study, for the first time, reveals the antioxidant properties of apelin in adipocytes, and suggests its potential as a novel therapeutic target for metabolic diseases.

Isotropic reconstruction of a 4-D MRI thoracic sequence using super-resolution.

PURPOSE: Four-dimensional (4D) thoracic magnetic resonance imaging (MRI) sequences have been shown to successfully monitor both tumor and lungs anatomy. However, a high temporal resolution is required to avoid motion artifacts, which leads to volumes with poor spatial resolution. This article proposes to reconstruct an isotropic 4D MRI thoracic sequence with minimum modifications to the acquisition protocols. This could be an important step toward the use of 4D MRI for thoracic radiotherapy applications. METHODS: In a postacquisition step, three orthogonal 4D anisotropic acquisitions are combined using super-resolution to reconstruct a series of isotropic volumes. A new phantom that simulates lung tumor motion is developed to evaluate the performance of the algorithm. The proposed framework is also applied to real data of a lung cancer patient. RESULTS: Subjective and objective evaluations show clear resolution enhancement and partial volume effect diminution. The isotropic reconstruction of patient data significantly improves both the visualization and segmentation of thoracic structures. CONCLUSIONS: The results presented here are encouraging and suggest that super-resolution can be regarded as an efficient method to improve the resolution of 4D MRI sequences. It produces an isotropic 4D sequence that would be impossible to acquire in practice. Further investigations will be required to evaluate its reproducibility in various clinical applications.

A novel neural-inspired learning algorithm with application to clinical risk prediction.

Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials.

Optical methods for blood perfusion measurement--theoretical comparison among four different modalities.

Blood perfusion in human tissue can be measured in vivo by means of various optical methods, which seem to be very different from one another. The most prominent examples of them are laser Doppler flowmetry, laser speckle contrast imaging, diffuse correlation spectroscopy, and the most recently developed diffuse speckle contrast analysis. In this paper, we claim that these four seemingly different modalities are examining different aspects of the same entity-the temporal autocorrelation function of scattered photons. We will show how the observables in each modality can be theoretically derived from the temporal autocorrelation function, and will discuss the merits and drawbacks of each modality in its practical use.

A predictor for predicting Escherichia coli transcriptome and the effects of gene perturbations.

BACKGROUND: A means to predict the effects of gene over-expression, knockouts, and environmental stimuli in silico is useful for system biologists to develop and test hypotheses. Several studies had predicted the expression of all Escherichia coli genes from sequences and reported a correlation of 0.301 between predicted and actual expression. However, these do not allow biologists to study the effects of gene perturbations on the native transcriptome. RESULTS: We developed a predictor to predict transcriptome-scale gene expression from a small number (n = 59) of known gene expressions using gene co-expression network, which can be used to predict the effects of over-expressions and knockdowns on E. coli transcriptome. In terms of transcriptome prediction, our results show that the correlation between predicted and actual expression value is 0.467, which is similar to the microarray intra-array variation (p-value = 0.348), suggesting that intra-array variation accounts for a substantial portion of the transcriptome prediction error. In terms of predicting the effects of gene perturbation(s), our results suggest that the expression of 83% of the genes affected by perturbation can be predicted within 40% of error and the correlation between predicted and actual expression values among the affected genes to be 0.698. With the ability to predict the effects of gene perturbations, we demonstrated that our predictor has the potential to estimate the effects of varying gene expression level on the native transcriptome. CONCLUSION: We present a potential means to predict an entire transcriptome and a tool to estimate the effects of gene perturbations for E. coli, which will aid biologists in hypothesis development. This study forms the baseline for future work in using gene co-expression network for gene expression prediction.

A biological continuum based approach for efficient clinical classification.

Clinical feature selection problem is the task of selecting and identifying a subset of informative clinical features that are useful for promoting accurate clinical diagnosis. This is a significant task of pragmatic value in the clinical settings as each clinical test is associated with a different financial cost, diagnostic value, and risk for obtaining the measurement. Moreover, with continual introduction of new clinical features, the need to repeat the feature selection task can be very time consuming. Therefore to address this issue, we propose a novel feature selection technique for diagnosis of myocardial infarction - one of the leading causes of morbidity and mortality in many high-income countries. This method adopts the conceptual framework of biological continuum, the optimization capability of genetic algorithm for performing feature selection and the classification ability of support vector machine. Together, a network of clinical risk factors, called the biological continuum based etiological network (BCEN), was constructed. Evaluation of the proposed methods was carried out using the cardiovascular heart study (CHS) dataset. Results demonstrate a significant speedup of 4.73-fold can be achieved for the development of MI classification model. The key advantage of this methodology is the provision of a reusable (feature subset) paradigm for efficient development of up-to-date and efficacious clinical classification models.

Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization.

To enable a more rational optimization approach to drive the transition from lab-scale to large industrial bioprocesses, a systematic framework coupling both experimental design and integrated modeling was established to guide the workflow executed from small flask scale to bioreactor scale. The integrated model relies on the coupling of biotic cell factory kinetics to the abiotic bioreactor hydrodynamics to offer a rational means for an in-depth understanding of two-way spatiotemporal interactions between cell behaviors and environmental variations. This model could serve as a promising tool to inform experimental work with reduced efforts via full-factorial in silico predictions. This chapter thus describes the general workflow involved in designing and applying this modeling approach to drive the experimental design towards rational bioprocess optimization.

A biological camera that captures and stores images directly into DNA.

The increasing integration between biological and digital interfaces has led to heightened interest in utilizing biological materials to store digital data, with the most promising one involving the storage of data within defined sequences of DNA that are created by de novo DNA synthesis. However, there is a lack of methods that can obviate the need for de novo DNA synthesis, which tends to be costly and inefficient. Here, in this work, we detail a method of capturing 2-dimensional light patterns into DNA, by utilizing optogenetic circuits to record light exposure into DNA, encoding spatial locations with barcoding, and retrieving stored images via high-throughput next-generation sequencing. We demonstrate the encoding of multiple images into DNA, totaling 1152 bits, selective image retrieval, as well as robustness to drying, heat and UV. We also demonstrate successful multiplexing using multiple wavelengths of light, capturing 2 different images simultaneously using red and blue light. This work thus establishes a 'living digital camera', paving the way towards integrating biological systems with digital devices.

Graphene oxide aptasensor droplet assay for detection of metabolites secreted by single cells applied to synthetic biology.

Synthetic biology harnesses the power of natural microbes by re-engineering metabolic pathways to manufacture desired compounds. Droplet technology has emerged as a high-throughput tool to screen single cells for synthetic biology, while the challenges in sensitive flexible single-cell secretion assay for bioproduction of high-value chemicals remained. Here, a novel droplet modifiable graphene oxide (GO) aptasensor was developed, enabling sensitive flexible detection of different target compounds secreted from single cells. Fluorophore-labeled aptamers were stably anchored on GO through π-π stacking interactions to minimize the non-specific interactions for low-background detection of target compounds with high signal-to-noise ratios. The assay's versatility was exhibited by adapting aptamer sequences to measure metabolic secretions like ATP and naringenin. To show the case, engineered E. coli were constructed for the bioproduction of naringenin. The high signal-to-noise ratio assay (∼2.72) was approached to precisely measure the naringenins secreted from single E. coli in the droplets. Consequently, secretory cells (Gib) were clearly distinguished from wild-type (WT) cells, with a low overlap in cell populations (∼0%) for bioproduction.

Engineering microbes to sense and eradicate Pseudomonas aeruginosa, a human pathogen.

Synthetic biology aims to systematically design and construct novel biological systems that address energy, environment, and health issues. Herein, we describe the development of a synthetic genetic system, which comprises quorum sensing, killing, and lysing devices, that enables Escherichia coli to sense and kill a pathogenic Pseudomonas aeruginosa strain through the production and release of pyocin. The sensing, killing, and lysing devices were characterized to elucidate their detection, antimicrobial and pyocin release functionalities, which subsequently aided in the construction of the final system and the verification of its designed behavior. We demonstrated that our engineered E. coli sensed and killed planktonic P. aeruginosa, evidenced by 99% reduction in the viable cells. Moreover, we showed that our engineered E. coli inhibited the formation of P. aeruginosa biofilm by close to 90%, leading to much sparser and thinner biofilm matrices. These results suggest that E. coli carrying our synthetic genetic system may provide a novel synthetic biology-driven antimicrobial strategy that could potentially be applied to fighting P. aeruginosa and other infectious pathogens.

An improved tamper detection and localization scheme for volumetric DICOM images.

The development of teleradiology brings the convenience of global medical record access along with the concerns over the security of medical images transmitted over the open network. With the prevailing adoption of three-dimensional (3-D) imaging modalities, it is vital to develop a security mechanism to provide large volumes of medical images with privacy and reliability. This paper presents the development of a new and improved method of implementing tamper detection and localization based on a fully reversible digital watermarking scheme for the protection of volumetric DICOM images. This tamper detection and localization method utilizes the 3-D property of volumetric data to achieve much faster processing time at both sender and receiver sides without compromising tamper localization accuracy. The performance of the proposed scheme was evaluated by using sample volumetric DICOM images. Results show that the scheme achieved on average about 65 % and 72 % reduction in watermarking and dewatermarking processing time, respectively. For cases where the images had been tampered, it is possible to detect and localize the tampered areas with improved localization resolution in the images using the scheme.

Group average difference: a termination criterion for active contour.

This paper presents a termination criterion for active contour that does not involve alteration of the energy functional. The criterion is based on the area difference of the contour during evolution. In this criterion, the evolution of the contour terminates when the area difference fluctuates around a constant. The termination criterion is tested using parametric gradient vector flow active contour with contour resampling and normal force selection. The usefulness of the criterion is shown through its trend, speed, accuracy, shape insensitivity, and insensitivity to contour resampling. The metric used in the proposed criterion demonstrated a steadily decreasing trend. For automatic implementation in which different shapes need to be segmented, the proposed criterion demonstrated almost 50% and 60% total time reduction while achieving similar accuracy as compared with the pixel movement-based method in the segmentation of synthetic and real medical images, respectively. Our results also show that the proposed termination criterion is insensitive to shape variation and contour resampling. The criterion also possesses potential to be used for other kinds of snakes.

Highly Reversible Tunable Thermal-Repressible Split-T7 RNA Polymerases (Thermal-T7RNAPs) for Dynamic Gene Regulation.

Temperature is a physical cue that is easy to apply, allowing cellular behaviors to be controlled in a contactless and dynamic manner via heat-inducible/repressible systems. However, existing heat-repressible systems are limited in number, rely on thermal sensitive mRNA or transcription factors that function at low temperatures, lack tunability, suffer delays, and are overly complex. To provide an alternative mode of thermal regulation, we developed a library of compact, reversible, and tunable thermal-repressible split-T7 RNA polymerase systems (Thermal-T7RNAPs), which fused temperature-sensitive domains of Tlpa protein with split-T7RNAP to enable direct thermal control of the T7RNAP activity between 30 and 42 °C. We generated a large mutant library with varying thermal performances via an automated screening framework to extend temperature tunability. Lastly, using the mutants, novel thermal logic circuitry was implemented to regulate cell growth and achieve active thermal control of the cell proportions within co-cultures. Overall, this technology expanded avenues for thermal control in biotechnology applications.

Thermogenetics: Applications come of age.

Temperature is a ubiquitous physical cue that is non-invasive, penetrative and easy to apply. In the growing field of thermogenetics, through beneficial repurposing of natural thermosensing mechanisms, synthetic biology is bringing new opportunities to design and build robust temperature-sensitive (TS) sensors which forms a thermogenetic toolbox of well characterised biological parts. Recent advancements in technological platforms available have expedited the discovery of novel or de novo thermosensors which are increasingly deployed in many practical temperature-dependent biomedical, industrial and biosafety applications. In all, the review aims to convey both the exhilarating recent technological developments underlying the advancement of thermosensors and the exciting opportunities the nascent thermogenetic field holds for biomedical and biotechnology applications.

Single 3'-exonuclease-based multifragment DNA assembly method (SENAX).

DNA assembly is a vital process in biotechnology and synthetic biology research, during which DNA plasmids are designed and constructed using bioparts to engineer microorganisms for a wide range of applications. Here, we present an enzymatic homology-based DNA assembly method, SENAX (Stellar ExoNuclease Assembly miX), that can efficiently assemble multiple DNA fragments at ambient temperature from 30 to 37 °C and requires homology overlap as short as 12-18 base pairs. SENAX relies only on a 3'-5' exonuclease, XthA (ExoIII), followed by Escherichia coli transformation, enabling easy scaling up and optimization. Importantly, SENAX can efficiently assemble short fragments down to 70 bp into a vector, overcoming a key shortcoming of existing commonly used homology-based technologies. To the best of our knowledge, this has not been reported elsewhere using homology-based methods. This advantage leads us to develop a framework to perform DNA assembly in a more modular manner using reusable promoter-RBS short fragments, simplifying the construction process and reducing the cost of DNA synthesis. This approach enables commonly used short bioparts (e.g., promoter, RBS, insulator, terminator) to be reused by the direct assembly of these parts into intermediate constructs. SENAX represents a novel accurate, highly efficient, and automation-friendly DNA assembly method.

BMSS2: A Unified Database-Driven Modeling Tool for Systematic Biomodel Selection.

Modeling in synthetic biology constitutes a powerful means in our continuous search for improved performance with a rational Design-Build-Test-Learn approach. Particularly, kinetic models unravel system dynamics and enable system analysis for guiding experimental design. However, a systematic yet modular pipeline that allows one to identify the appropriate model and guide the experimental designs while tracing the entire model development and analysis is still lacking. Here, we develop BMSS2, a unified tool that streamlines and automates model selection by combining information criterion ranking with upstream and parallel analysis algorithms. These include Bayesian parameter inference, a priori and a posteriori identifiability analysis, and global sensitivity analysis. In addition, the database-driven design supports interactive model storage/retrieval to encourage reusability and facilitate automated model selection. This allows ease of model manipulation and deposition for the selection and analysis, thus enabling better utilization of models in guiding experimental design.