Electrohydrodynamic‐Induced SERS Immunoassay for Extensive Multiplexed Biomarker Sensing

Cancer diagnosis and patient monitoring require sensitive and simultaneous measurement of multiple cancer biomarkers considering that single biomarker analysis present inadequate information on the underlying biological transformations. Thus, development of sensitive and selective assays for multiple biomarker detection might improve clinical diagnosis and expedite the treatment process. Herein, a microfluidic platform for the rapid, sensitive, and parallel detection of multiple cancer‐specific protein biomarkers from complex biological samples is presented. This approach utilizes alternating current electrohydrodynamic‐induced surface shear forces that provide exquisite control over fluid flow thereby enhancing target–sensor interactions and minimizing non‐specific binding. Further, the use of surface‐enhanced Raman scattering‐based spectral encoding with individual barcodes for different targets enables specific and simultaneous detection of captured protein biomarkers. Using this approach, the specific and sensitive detection of clinically relevant biomarkers including human epidermal growth factor receptor 2 (HER2); Mucin 1, cell surface associated (MUC1); epidermal growth factor receptor; and Mucin 16, cell surface associated (MUC16) at concentrations as low as 10 fg mL^?1 in patient serum is demonstrated. Successful target detection from patient samples further demonstrates the potential of this current approach for the clinical diagnosis, which envisages a clinical translation for a rapid and sensitive appraisal of clinical samples in cancer diagnostics.     

Protein-Precoated Surface of Metal-Organic Framework Nanoparticles for Targeted Delivery

Metal-organic framework (MOF) nanoparticles have recently emerged as a promising vehicle for drug delivery with high porosity and feasibility. However, employing a MOF-based drug delivery system remains a challenge due to the difficulty in controlling interfaces of particles in a biological environment. In this paper, protein corona-blocked Zr₆-based MOF (PCN-224) nanoparticles are presented for targeted cancer therapy with high efficiency. The unmodified PCN-224 surface is precoated with glutathione transferase (GST)-fused targetable affibody (GST-Afb) proteins via simple mixing conjugations instead of chemical modifications that can induce the impairment of proteins. GST-Afb proteins are shown to stably protect the surface of PCN-224 particles in a specific orientation with GST adsorbed onto the porous surface and the GST-linked Afb posed outward, minimizing the unwanted interfacial interactions of particles with external biological proteins. The Afb-directed cell-specific targeting ability of particles and consequent induction of cell death is demonstrated both in vitro and in vivo by using two kinds of Afb, which targets the surface membrane receptor, human epidermal growth factor receptor 2 (HER2) or epidermal growth factor receptor (EGFR). This study provides insight into the way of regulating the protein-adhesive surface of MOF nanoparticles and designing a more effective MOF-hosted targeted delivery system.     

Deconstructing the core dynamics from a complex time-lagged regulatory biological circuit

Complex regulatory dynamics is ubiquitous in molecular networks composed of genes and proteins. Recent progress in computational biology and its application to molecular data generate a growing number of complex networks. Yet, it has been difficult to understand the governing principles of these networks beyond graphical analysis or extensive numerical simulations. Here the authors exploit several simplifying biological circumstances which thereby enable to directly detect the underlying dynamical regularities driving periodic oscillations in a dynamical nonlinear computational model of a protein?protein network. System analysis is performed using the cell cycle, a mathematically well-described complex regulatory circuit driven by external signals. By introducing an explicit time delay and using a `tearing-and-zooming? approach the authors reduce the system to a piecewise linear system with two variables that capture the dynamics of this complex network. A key step in the analysis is the identification of functional subsystems by identifying the relations between statevariables within the model. These functional subsystems are referred to as dynamical modules operating as sensitive switches in the original complex model. By using reduced mathematical representations of the subsystems the authors derive explicit conditions on how the cell cycle dynamics depends on system parameters, and can, for the first time, analyse and prove global conditions for system stability. The approach which includes utilising biological simplifying conditions, identification of dynamical modules and mathematical reduction of the model complexity may be applicable to other well-characterised biological regulatory circuits.     

New integrated model to estimate the manufacturing cost and production system performance at the conceptual design stage of helicopter blade assembly

This paper describes a new integrated model development to estimate the manufacturing cost and production system performance at the conceptual design stage. A fully automated conceptual framework for design for manufacturing (DFM) has been developed. An integrated product process design concept using activity based costing is applied in this paper. The new integrated model consists of four sub-modules: the geometric parameters generation module, processing time estimation module, activity based costing (ABC) module and production system performance module. All of the input-output data flows of developed modules are fully integrated for automated manufacturing cost analysis and production system performance. A developed integrated model is very useful for designers or integrated product development team to make a decision for evaluating the design alternatives and trade-offs between design and manufacturing phases at the conceptual design stage. A case study for a composite helicopter rotor blade is included.     

Integrated analysis of the gene neighbouring impact on bacterial metabolic networks

Different levels of abstraction are needed to represent a living system. Unfortunately information of different nature is not superposable in an obvious way, but requires a dedicated framework. Because biological abstractions, i.e., genomic or metabolic information, can be easily represented as graphs, it is intuitive to integrate them into a unique graph, in which one can perform graph analysis for investigating a given biological assumption. This study follows such a philosophy and completes a genome and metabolome combination. In a such integrated framework and as illustration, we applied a graph analysis that automatically investigates impacts of the gene adjacency to predict functional relationships between genes and reactions. Our approach, called SIPPER, creates a weighted graph, in which the weights rely on the given relationship between genes, and computes (alternative) chains of reactions catalysed by genes. This method, as a generalisation of methods already published, can be easily adapted to several biological assumptions, properties or measures. This paper evaluates SIPPER on Escherichia coli. We automatically extract subgraphs, called k-SIPs, and quantify their interest in both genomic and metabolic contexts by showing functional compounds like operons or functional modules.     

Rapid Quantitative Profiling of N-Glycan by the Glycan-Labeling Method Using 3-Aminoquinoline/α-Cyano-4-hydroxycinnamic Acid

Protein glycosylation is a crucial phenomenon for understanding protein functions, since its patterns and degree are associated with many biological processes, such as intercellular signaling and immune response. We previously reported a novel glycan-labeling method using a 3-ainoquinoline/α-cyano-4-hydroxycinnamic acid (3-AQ/CHCA) liquid matrix for highly sensitive detection by matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry (MS). In the present study, we examined the practicality of this method for qualitative and quantitative glycan profile analysis. We first investigated the reproducibility of the data for 16 N-glycans prepared from human epidermal growth factor receptor type 2 (HER2). All of the data obtained in intra-assays and interassays were highly correlated with statistical significance (R(2) > 0.9, p < 0.05). In addition, the HER2 glycosylation pattern differed significantly between different breast cancer cell lines SK-BR-3 and BT474 in a comparative analysis of profile data. Finally, the quantitative capability of this method was examined by using PA-labeled monosialylated N-glycan as an internal standard (IS). Using IS for AQ-labeled neutral and sialylated standard glycans, the ion peak intensity was highly linear (R(2) > 0.9) from 0.5 to 5000 fmol. Furthermore, using IS for HER2 N-glycans, all of the N-glycans were highly linear with their dilution factors (R(2) > 0.9). These results suggest that our developed AQ labeling method enabled rapid qualitative and quantitative analyses of glycans. This glycan analysis method should contribute to the field of biomarker discovery and biomedicine in applications such as quality control of biotechnology-based drugs.     

Modular simulation tool for modelling JIT manufacturing

Since quantitative information is very helpful in implementing JIT production techniques, computer simulation can be a valuable tool in designing, implementing or changing JIT practices in a production system. Nowadays, existing simulation software incorporates modular programming and enhanced graphic systems for output representation. It enables users to generate modules that represent partial aspects of a JIT system. These modules, adequately modified and integrated, give researchers and practitioners the possibility to create complex models that can be applied to a variety of JIT systems or JIT production environments. A modular simulation tool, based on the modular capabilities of Witness, is introduced in this paper. As a module example, the feeder double-kanban line module is presented, which represents one of the core aspects of a JIT manufacturing system. Finally, module integration is illustrated by modelling a U-Shaped line. The experimentation and evaluation of the U-line allow one to appreciate how modular simulation can be a powerful tool in decision making, by enabling users to analyse systems configurations and operation rules before implementing them.     

Smart nanoprobes for ultrasensitive detection of breast cancer via magnetic resonance imaging

Antibody-conjugated hydrophilic magnetic nanocrystals for use as smart nanoprobes were developed for ultrasensitive detection of breast cancer via magnetic resonance (MR) imaging. MnFe(2)O(4) nanocrystals (MNCs) for use as MR imaging contrast agents were synthesized by thermal decomposition to take advantage of their MR signal enhancement effect. The MNC surfaces were then modified with amphiphilic tri-block copolymers (dicarboxy poly(ethylene glycol)-block-poly(propylene glycol)-block-poly(ethylene glycol)), not only allowing the MNCs to transfer from the organic to the aqueous phase, but also increasing the colloidal stability of the MNCs by masking poly(ethylene glycol). The physicochemical properties of the synthesized hydrophilic magnetic nanocrystals (HMNCs) were fully investigated. Trastuzumab (TZ), a monoclonal antibody against human epidermal growth factor receptor (HER2/neu), was further conjugated on the surface of HMNCs to specifically target HER2/neu over-expressed breast cancer cells. MR imaging analysis of target cells treated with TZ-conjugated HMNCs (TZ-HMNCs) clearly demonstrated their potential as high-performance nanoprobes for selective imaging.     

A Modular Fault-Diagnostic System for Analog Electronic Circuits Using Neural Networks With Wavelet Transform as a Preprocessor

We have developed a modular analog circuit fault- diagnostic system based on neural networks using wavelet decomposition, principal component analysis, and data normalization as preprocessors. Our proposed system has the ability to identify faulty components or modules in an analog circuit by analyzing its impulse response. In this approach, the circuit is divided into modules, which, in turn, are divided into smaller submodules successively. At each level, where a module is divided into submodules, a neural network is trained to identify the submodule that inherits the fault of interest from the parent module. This procedure finds the faulty component or module of any desirable size in an analog circuit by consecutive divisions of modules as many times as necessary. Our proposed approach has three advantages over the traditional neural-network-based diagnostic systems, which directly look for faulty components in the entire circuit. First, the performance of the modular systems is reliable and robust independent of the circuit size and can successfully classify similar fault classes with a significant overlap in the feature space where the traditional approach completely fails. Second, the modular approach requires significantly smaller neural network architectures, leading to much more efficient training. Third, for large real circuit boards, our diagnostic system proceeds to systematically reduce the size of the faulty modules until it is feasible to replace it.     

SMILE: a novel procedure for subcellular module identification with localisation expansion

Computational clustering methods help identify functional modules in protein–protein interaction (PPI) network, in which proteins participate in the same biological pathways or specific functions. Subcellular localisation is crucial for proteins to implement biological functions and each compartment accommodates specific portions of the protein interaction structure. However, the importance of protein subcellular localisation is often neglected in the studies of module identification. In this study, the authors propose a novel procedure, subcellular module identification with localisation expansion (SMILE), to identify super modules that consist of several subcellular modules performing specific biological functions among cell compartments. These super modules identified by SMILE are more functionally diverse and have been verified to be more associated with known protein complexes and biological pathways compared with the modules identified from the global PPI networks in both the compartmentalised PPI and InWeb_InBioMap datasets. The authors’ results reveal that subcellular localisation is a principal feature of functional modules and offers important guidance in detecting biologically meaningful results.Inspec keywords: cellular biophysics, proteins, molecular biophysicsOther keywords: subcellular module identification, localisation expansion, computational clustering methods, protein‐protein interaction network, biological functions, protein interaction structure, protein subcellular localisation, subcellular modules, InWeb‐InBioMap datasets, subcellular localisation     

Evaluation of the logistic model of the reconfigurable manufacturing system based on generalised stochastic Petri nets

To reveal the influence on system performance by the logistic model of reconfigurable manufacturing system (RMS), the generalised stochastic Petri nets (GSPN) modular modelling approach is presented in this paper. It is based upon the characteristics of a bottleneck service. According to this approach, the bottleneck service in the production process is found first. By corresponding different resources in the service to different modules of the GSPN, the module is reconfigured. The analysis of the model using the Markov chain is hereby presented, as is the average utilisation factor of RMS. Following this, the production capacity of different products and the average productivity of reconfigurable manufacturing cells (RMCs) are discussed.     

Immobilization of a human epidermal growth factor receptor 2 mimotope-derived synthetic peptide on Au and its potential application for detection of herceptin in human serum by quartz crystal microbalance

Therapeutic antibodies are antigenically similar to human antibodies and are difficult to detect in assays of human serum samples without the use of the therapeutic antibody's complementary antigen. Herein for the first time, we established a platform to detect Herceptin in solutions by using a small (<2.2 kDa), inexpensive, highly stable human epidermal growth factor receptor (HER2) mimotope-derived synthetic peptide immobilized on the surface of a Au quartz electrode. We used the HER2 mimotope as a substitute for the HER2 receptor protein in piezoimmunosensor or quartz crystal microbalance (QCM) assays to detect Herceptin in human serum. We demonstrated that assay sensitivity was dependent upon the amino acids used to tether and link the peptide to the sensor surface and the buffers used to carry out the assays. The detection limit of the piezoimmunosensor assay was 0.038 nM with a linear operating range of 0.038-0.859 nM. Little nonspecific binding to other therapeutic antibodies (Avastin and Rituxan) was observed. Levels of Herceptin in serum samples obtained from treated patients, as ascertained using the synthetic peptide-based QCM assay, were typical for those treated with Herceptin. The findings of this study are significant in that low-cost synthetic peptides could be used in a QCM assay, in lieu of native or recombinant antigens or capture antibodies, to rapidly detect a therapeutic antibody in human serum. The results suggested that a synthetic peptide bearing a particular functional sequence could be applied for developing a new generation of affinity-based immunosensors to detect a broad range of clinical biomarkers.     

XPC: an on-line expert system for statistical process control

The majority of available microcomputer packages for statistical process control (SPC) are off-line programs which present information regarding quality in the form of control charts. The user has to interpret the charts to infer process and product quality. This paper describes XPC, an on-line expert system for SPC. The system produces mean and range charts and interprets them automatically. XPC consists of five main modules. The first module ascertains process parameters and constructs the charts. The second module performs capability analysis to ensure that these control charts are compatible with the process specifications. The third module interprets on-line data, detects possible out-of-control situations and suggests corrective actions. The fourth module updates the charts to improve process capability. The last module produces periodical reports. XPC is based on Leonardo, an expert system shell with a hybrid knowledge representation facility enabling the use of rules, rulesets, frames, procedures and classes     

Validation and invalidation of systems biology models using robustness analysis

Robustness, the ability of a system to function correctly in the presence of both internal and external uncertainty, has emerged as a key organising principle in many biological systems. Biological robustness has thus become a major focus of research in Systems Biology, particularly on the engineering-biology interface, since the concept of robustness was first rigorously defined in the context of engineering control systems. This review focuses on one particularly important aspect of robustness in Systems Biology, that is, the use of robustness analysis methods for the validation or invalidation of models of biological systems. With the explosive growth in quantitative modelling brought about by Systems Biology, the problem of validating, invalidating and discriminating between competing models of a biological system has become an increasingly important one. In this review, the authors provide a comprehensive overview of the tools and methods that are available for this task, and illustrate the wide range of biological systems to which this approach has been successfully applied.     

Critical roles for EGFR and EGFR–HER2 clusters in EGF binding of SW620 human carcinoma cells

Epidermal growth factor (EGF) signalling regulates normal epithelial and other cell growth, with EGF receptor (EGFR) overexpression reported in many cancers. However, the role of EGFR clusters in cancer and their dependence on EGF binding is unclear. We present novel single-molecule total internal reflection fluorescence microscopy of (i) EGF and EGFR in living cancer cells, (ii) the action of anti-cancer drugs that separately target EGFR and human EGFR2 (HER2) on these cells and (iii) EGFR–HER2 interactions. We selected human epithelial SW620 carcinoma cells for their low level of native EGFR expression, for stable transfection with fluorescent protein labelled EGFR, and imaged these using single-molecule localization microscopy to quantify receptor architectures and dynamics upon EGF binding. Prior to EGF binding, we observe pre-formed EGFR clusters. Unexpectedly, clusters likely contain both EGFR and HER2, consistent with co-diffusion of EGFR and HER2 observed in a different model CHO-K1 cell line, whose stoichiometry increases following EGF binding. We observe a mean EGFR : EGF stoichiometry of approximately 4 : 1 for plasma membrane-colocalized EGFR–EGF that we can explain using novel time-dependent kinetics modelling, indicating preferential ligand binding to monomers. Our results may inform future cancer drug developments.     

Optical sizing of immunolabel clusters through multispectral plasmon coupling microscopy

The wavelength dependent scattering cross sections of self-assembled silver nanoparticle clusters of known size (n) were measured on five different wavelength channels between 427 and 510 nm through correlation of multispectral imaging and scanning electron microscopy. A multivariate statistical analysis of the spectral response of this training set provided a correlation between spectral response and cluster size and enabled a classification of new measurements into four distinct nanoparticle association levels (I1-I4) whose compositions were dominated by monomers (I1), dimers (I2), trimers and tetramers (I3), and larger clusters (I4), respectively. One potential application of the optical sizing approach is to map association levels of silver immunolabels on cellular surfaces. We demonstrate the feasibility of this approach using silver immunolabels targeted at the epidermal growth factor receptor on A431 cells in a proof of principle experiment. The ability to measure immunolabel association levels on subcellular length scales in an optical microscope provides new opportunities for experimentally assessing receptor density distributions on living cells in solution.