Build a Sustainable Vaccines Industry with Synthetic Biology.

The vaccines industry has not changed appreciably in decades regarding technology, and has struggled to remain viable, with large companies withdrawing from production. Meanwhile, there has been no let-up in outbreaks of viral disease, at a time when the biopharmaceuticals industry is discussing downsizing. The distributed manufacturing model aligns well with this, and the advent of synthetic biology promises much in terms of vaccine design. Biofoundries separate design from manufacturing, a hallmark of modern engineering. Once designed in a biofoundry, digital code can be transferred to a small-scale manufacturing facility close to the point of care, rather than physically transferring cold-chain-dependent vaccine. Thus, biofoundries and distributed manufacturing have the potential to open up a new era of biomanufacturing, one based on digital biology and information systems. This seems a better model for tackling future outbreaks and pandemics.

Building the UK's industrial base in engineering biology.

The paper describes the strategy and components that have been put in place to build the UK's research and industrial base in Engineering Biology. The initial section of the paper provides a brief historical overview of the development of the field in the United Kingdom. This comprised, principally, a major report by the Royal Academy of Engineering and a strategic roadmap for synthetic biology, together with the establishment of six new synthetic biology research centres, a national centre for the industrial translation of synthetic biology and five biofoundries. The next section of the paper describes the UK government's policy for the field. Important elements of the implementation of the policy comprises people, Infrastructure, Business Environment and place. In this context, a number of important areas are addressed-including industrial translation; building an expert workforce and nucleating, incubating and accelerating a new engineering biology industry in the United Kingdom. The final portion of the paper addresses the author's view of the way forward. This comprises placing the development of the field, both nationally and internationally, in the context of the development of the Bioeconomy and Climate Change. The final section of the text addresses a specific strategic approach and the implications for the United Kingdom in relation to the development of its industrial base in Engineering Biology.

Author Correction: Building a global alliance of biofoundries.

The original version of this Comment contained errors in the legend of Figure 2, in which the locations of the fifteenth and sixteenth GBA members were incorrectly given as '(15) Australian Genome Foundry, Macquarie University; (16) Australian Foundry for Advanced Biomanufacturing, University of Queensland.'. The correct version replaces this with '(15) Australian Foundry for Advanced Biomanufacturing (AusFAB), University of Queensland and (16) Australian Genome Foundry, Macquarie University'. This has been corrected in both the PDF and HTML versions of the Comment.

Miniaturisation of high-throughput plasmid DNA library preparation for next-generation sequencing using multifactorial optimisation.

High-throughput preparation of plasmid DNA libraries for next-generation sequencing (NGS) is an important capability for molecular biology laboratories. In particular, it is an essential quality control (QC) check when large numbers of plasmid variants are being generated. Here, we describe the use of the Design of Experiments (DOE) methodology to optimise the miniaturised preparation of plasmid DNA libraries for NGS, using the Illumina® Nextera XT technology and the Labcyte Echo® acoustic liquid dispensing system. Furthermore, we describe methods which can be implemented as a QC check for identifying the presence of genomic DNA (gDNA) in plasmid DNA samples and the subsequent shearing of the gDNA, which otherwise prevents the acoustic transfer of plasmid DNA. This workflow enables the preparation of plasmid DNA libraries which yield high-quality sequencing data.

Toward the First Data Acquisition Standard in Synthetic Biology.

This paper describes the development of a new data acquisition standard for synthetic biology. This comprises the creation of a methodology that is designed to capture all the data, metadata, and protocol information associated with biopart characterization experiments. The new standard, called DICOM-SB, is based on the highly successful Digital Imaging and Communications in Medicine (DICOM) standard in medicine. A data model is described which has been specifically developed for synthetic biology. The model is a modular, extensible data model for the experimental process, which can optimize data storage for large amounts of data. DICOM-SB also includes services orientated toward the automatic exchange of data and information between modalities and repositories. DICOM-SB has been developed in the context of systematic design in synthetic biology, which is based on the engineering principles of modularity, standardization, and characterization. The systematic design approach utilizes the design, build, test, and learn design cycle paradigm. DICOM-SB has been designed to be compatible with and complementary to other standards in synthetic biology, including SBOL. In this regard, the software provides effective interoperability. The new standard has been tested by experiments and data exchange between Nanyang Technological University in Singapore and Imperial College London.

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.

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.

The effect of sample age and prediction resolution on myocardial infarction risk prediction.

Myocardial infarction (MI) is one of the leading causes of death in many developed countries. Hence, early detection of MI events is critical for effective preventative therapies, potentially reducing avoidable mortality. One approach for early disease prediction is the use of risk prediction models developed using machine learning techniques. One important component of these models is to provide clinicians with the flexibility to customize (e.g., the prediction range) and use the risk prediction model that they deemed most beneficial for their patients. Therefore, in this paper, we develop MI prediction models and investigate the effect of sample age and prediction resolution on the performance of MI risk prediction models. The cardiovascular health study dataset was used in this study. Results indicate that the prediction model developed using SVM algorithm is capable of achieving high sensitivity, specificity, and balanced accuracy of 95.3%, 84.8%, and 90.1%, respectively, over a time span of 6 years. Both sample age and prediction resolution were found not to have a significant impact on the performance of MI risk prediction models developed using subjects aged 65 and above. This implies that risk prediction models developed using different sample age and prediction resolution is a feasible approach. These models can be integrated into a computer aided screening tool which clinicians can use to interpret and predict the MI risk status of the individual patients after performing the necessary clinical assessments (e.g., cognitive function, physical function, electrocardiography, general changes to health/lifestyle, and medications) required by the models. This could offer a means for clinicians to screen the patients at risk of having MI in the near future and prescribe early medical intervention to reduce the risk.

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.

Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology.

Modular and orthogonal genetic logic gates are essential for building robust biologically based digital devices to customize cell signalling in synthetic biology. Here we constructed an orthogonal AND gate in Escherichia coli using a novel hetero-regulation module from Pseudomonas syringae. The device comprises two co-activating genes hrpR and hrpS controlled by separate promoter inputs, and a σ(54)-dependent hrpL promoter driving the output. The hrpL promoter is activated only when both genes are expressed, generating digital-like AND integration behaviour. The AND gate is demonstrated to be modular by applying new regulated promoters to the inputs, and connecting the output to a NOT gate module to produce a combinatorial NAND gate. The circuits were assembled using a parts-based engineering approach of quantitative characterization, modelling, followed by construction and testing. The results show that new genetic logic devices can be engineered predictably from novel native orthogonal biological control elements using quantitatively in-context characterized parts.

Computational design approaches and tools for synthetic biology.

A proliferation of new computational methods and software tools for synthetic biology design has emerged in recent years but the field has not yet reached the stage where the design and construction of novel synthetic biology systems has become routine. To a large degree this is due to the inherent complexity of biological systems. However, advances in biotechnology and our scientific understanding have already enabled a number of significant achievements in this area. A key concept in engineering is the ability to assemble simpler standardised modules into systems of increasing complexity but it has yet to be adequately addressed how this approach can be applied to biological systems. In particular, the use of computer aided design tools is common in other engineering disciplines and it should eventually become centrally important to the field of synthetic biology if the challenge of dealing with the stochasticity and complexity of biological systems can be overcome.

Opportunities for microfluidic technologies in synthetic biology.

We introduce microfluidics technologies as a key foundational technology for synthetic biology experimentation. Recent advances in the field of microfluidics are reviewed and the potential of such a technological platform to support the rapid development of synthetic biology solutions is discussed.

Web-based multilayer viewing interface for knee cartilage.

Many adults suffer from osteoarthritis (OA) with the majority of people over 65 showing radiographic evidence of the disease. To carry out effective diagnosis and treatment, it is necessary to understand the progression of cartilage loss and study the effectiveness of therapeutic interventions. Hence, it is important to have accurate, fast diagnosis of the disease. In this paper, we describe a Web-based user interface that enables the direct viewing of 2-D and 3-D image data from the visceral and tissue levels of the biological continuum (i.e., the continuum comprising systems, viscera, tissue, cells, proteins, and genes)--while preserving geometric integrity. This is achieved despite the fact that the data are from different modalities (i.e., magnetic resonance (MR) and light microscopy). The user interface was tested using image data acquired from a study of articular cartilage thickness in the porcine knee. The interface allows the clinician to view both MR and light microscopy images in an integrated manner-with the information linked geometrically.

Comparison and evaluation of methods for liver segmentation from CT datasets.

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

Single-trial EEG source reconstruction for brain-computer interface.

A new way to improve the classification rate of an EEG-based brain-computer interface (BCI) could be to reconstruct the brain sources of EEG and to apply BCI methods to these derived sources instead of raw measured electrode potentials. EEG source reconstruction methods are based on electrophysiological information that could improve the discrimination between BCI tasks. In this paper, we present an EEG source reconstruction method for BCI. The results are compared with results from raw electrode potentials to enable direct evaluation of the method. Features are based on frequency power change and Bereitschaft potential. The features are ranked with mutual information before being fed to a proximal support vector machine. The dataset IV of the BCI competition II and data from four subjects serve as test data. Results show that the EEG inverse solution improves the classification rate and can lead to results comparable to the best currently known methods.

Wavelet-based de-noising algorithm for images acquired with parallel magnetic resonance imaging (MRI).

Wavelet-based de-noising has been shown to improve image signal-to-noise ratio in magnetic resonance imaging (MRI) while maintaining spatial resolution. Wavelet-based de-noising techniques typically implemented in MRI require that noise displays uniform spatial distribution. However, images acquired with parallel MRI have spatially varying noise levels. In this work, a new algorithm for filtering images with parallel MRI is presented. The proposed algorithm extracts the edges from the original image and then generates a noise map from the wavelet coefficients at finer scales. The noise map is zeroed at locations where edges have been detected and directional analysis is also used to calculate noise in regions of low-contrast edges that may not have been detected. The new methodology was applied on phantom and brain images and compared with other applicable de-noising techniques. The performance of the proposed algorithm was shown to be comparable with other techniques in central areas of the images, where noise levels are high. In addition, finer details and edges were maintained in peripheral areas, where noise levels are low. The proposed methodology is fully automated and can be applied on final reconstructed images without requiring sensitivity profiles or noise matrices of the receiver coils, therefore making it suitable for implementation in a clinical MRI setting.

Addressing the future of clinical information systems--Web-based multilayer visualization.

This paper addresses some key issues relating to the development of new technology for clinical information systems (CIS) in relation to imaging and visualizing data. With the increasing importance of molecular and cellular biology, a new type of medicine, molecular based medicine, is now developing. This will significantly alter the way in which medicine is practiced. The view is presented that CIS will need to operate seamlessly across the Biological Continuum, i.e., the hierarchy of the human organism comprising systems, viscera, tissue, cells, proteins, and genes. We propose a multilayered visualization interface, which operates across the Biological Continuum, based on Web-based technology. A visualization interface package for two-dimensional and three-dimensional image data at the visceral and cellular levels is described. Two application examples are presented: 1) MR knee images, at the visceral level and 2) endothelial nuclei images, acquired from confocal laser microscopy, at the cellular level.

Automated techniques for visualization and mapping of articular cartilage in MR images of the osteoarthritic knee: a base technique for the assessment of microdamage and submicro damage.

The purpose of this paper is to describe automated techniques for the visualization and mapping of articular cartilage in magnetic resonance (MR) images of the osteoarthritic knee. The MR sequences and analysis software which will be described allow the assessment of cartilage damage using a range of standard scanners. With high field strength systems it would be possible, using these techniques, to assess micro-damage. The specific aim of the paper is to develop and validate software for automated segmentation and thickness mapping of articular cartilage from three-dimensional (3-D) gradient-echo MR images of the knee. The method can also be used for MR-based assessment of tissue engineered grafts. Typical values of cartilage thickness over seven defined regions can be obtained in patients with osteoarthritis (OA) and control subjects without OA. Three groups of patients were studied. The first group comprised patients with moderate OA in the age range 45-73 years. The second group comprised asymptomatic volunteers of 50-65 years; the third group, younger volunteers selected by clinical interview, history and X-ray. In this paper, sagittal 3-D spoiled-gradient steady-state acquisition images were obtained using a 1.5-T GE whole-body scanner with a specialist knee coil. For validation bovine and porcine cadaveric knees were given artificial cartilage lesions and then imaged. The animal validations showed close agreement between direct lesion measurements and those obtained from the MR images. The feasibility of semi-automated segmentation is demonstrated. Regional cartilage thickness values are seen as having practical application for fully automated detection of OA lesions even down to the submicrometer level.