Showing papers in "IEEE Transactions on Nanobioscience in 2008"

On Delayed Genetic Regulatory Networks With Polytopic Uncertainties: Robust Stability Analysis

Abstract: In this paper, we investigate the robust asymptotic stability problem of genetic regulatory networks with time-varying delays and polytopic parameter uncertainties. Both cases of differentiable and nondifferentiable time-delays are considered, and the convex polytopic description is utilized to characterize the genetic network model uncertainties. By using a Lyapunov functional approach and linear matrix inequality (LMI) techniques, the stability criteria for the uncertain delayed genetic networks are established in the form of LMIs, which can be readily verified by using standard numerical software. An important feature of the results reported here is that all the stability conditions are dependent on the upper and lower bounds of the delays, which is made possible by using up-to-date techniques for achieving delay dependence. Another feature of the results lies in that a novel Lyapunov functional dependent on the uncertain parameters is utilized, which renders the results to be potentially less conservative than those obtained via a fixed Lyapunov functional for the entire uncertainty domain. A genetic network example is employed to illustrate the applicability and usefulness of the developed theoretical results.

Development of Superparamagnetic Iron Oxide Nanoparticles (SPIONS) for Translation to Clinical Applications

Abstract: Superparamagnetic iron oxide nanoparticles (SPIONs) have attract a great deal of interest in biomedical research and clinical applications over the past decades. Taking advantage the fact that SPIONs only exhibit magnetic properties in the presence of an applied magnetic field, they have been used in both in vitro magnetic separation and in vivo applications such as hyperthermia (HT), magnetic drug targeting (MDT), magnetic resonance imaging (MRI), gene delivery (GD) and nanomedicine. Successful applications of SPIONs rely on precise control of the particle's shape, size, and size distribution and several synthetic routes for preparing SPIONs have been explored. Tailored surface properties specifically designed for cell targeting are often required, although the generic strategy involves creating biocompatible polymeric or non-polymeric coating and subsequent conjugation of bioactive molecules. In this review article, synthetic routes, surface modification and functionaliztion of SPIONs, as well as the major biomedical applications are summarized, with emphasis on in vivo applications.

Mechanical Modeling of Biological Cells in Microinjection

Abstract: Microinjection is an effective technique to introduce foreign materials into a biological cell. Although some semi-automatic and fully-automatic microinjection systems have been developed, a full understanding of the mechanical response of biological cells to injection operation remains deficient. In this paper, a new mechanical model based on membrane theory is proposed. This model establishes a relationship between the injection force and the deformation of biological cells with the quasi-static equilibrium equations, which are solved by the Runge-Kutta numerical method. Based on this model, other mechanical responses can also be inferred, such as the effect of the injector radius, the membrane stress and tension distribution, internal cell pressure, and the deformed cell shape. To verify the proposed model, experiments are performed on microinjection of zebrafish embryos at different developmental stages and medaka embryos at the blastula stage. It is demonstrated that the modeling results agree well with the experimental data, which shows that the proposed model can be used to estimate the mechanical properties of cell biomembranes. (In this paper, biomembrane refers to the membrane-like structures enveloping cells).

In Vitro Analysis of Cisplatin Functionalized Magnetic Nanoparticles in Combined Cancer Chemotherapy and Electromagnetic Hyperthermia

Abstract: A novel platform has been developed for combined cancer chemotherapy and hyperthermia based on iron oxide magnetic nanoparticles functionalized with cis-diamminedichloroplatinum(II) (cisplatin). The capabilities of this system for heating and controlled drug release were investigated, and the system was tested in vitro by the treatment of BP6 rat sarcoma cells, where we demonstrated a synergism between the effects of cisplatin-targetMAG nanoparticles and the application of electromagnetic field.

The Effects of Cell Sizes, Environmental Conditions, and Growth Phases on the Strength of Individual W303 Yeast Cells Inside ESEM

Abstract: We performed in situ measurements of mechanical properties of individual W303 wild-type yeast cells by using an integrated environmental scanning electron microscope (ESEM)-nanomanipulator system. Compression experiments to penetrate the cell walls of single cells of different cell sizes (about 3-6 mu m diameter), environmental conditions (600 Pa and 3 mPa), and growth phases (early log, mid log, late log and saturation) were conducted. The compression experiments were performed inside ESEM, embedded with a 7 DOF nanomanipulator with a sharp pyramidal end effector and a cooling stage, i.e., a temperature controller. ESEM itself can control the chamber pressure. Data clearly show an increment in penetration force, i.e., 96plusmn2, 124 plusmn10, 163plusmn1, and 234plusmn14 nN at 3, 4, 5, and 6 mu m cell diameters, respectively. Whereas, 20-fold increase in penetration forces was recorded at different environmental conditions for 5 mu m cell diameter, i.e., 163plusmn1 nN and 2.95plusmn0.23 mu N at 600 Pa (ESEM mode) and 3 mPa (HV mode), respectively. This was further confirmed from quantitative estimation of average cell rigidity through the Hertz model, i.e., ESEM mode (3.31plusmn0.11 MPa) and HV mode (26.02plusmn3.66 MPa) for 5 mu m cell diameter. Finally, the penetration forces at different cell growth phases also show the increment pattern from log (early, mid, and late) to saturation phases, i.e., 161plusmn 25, 216plusmn15, 255 plusmn21, and 408plusmn41 nN, respectively.

Stochastic Dynamic Modeling of Short Gene Expression Time-Series Data

Abstract: In this paper, the expectation maximization (EM) algorithm is applied for modeling the gene regulatory network from gene time-series data. The gene regulatory network is viewed as a stochastic dynamic model, which consists of the noisy gene measurement from microarray and the gene regulation first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, both the model parameters and the actual values of the gene expression levels can be identified simultaneously. Moreover, the algorithm can deal with the sparse parameter identification and the noisy data in an efficient way. It is also shown that the EM algorithm can handle the microarray gene expression data with large number of variables but a small number of observations. The gene expression stochastic dynamic models for four real-world gene expression data sets are constructed to demonstrate the advantages of the introduced algorithm. Several indices are proposed to evaluate the models of inferred gene regulatory networks, and the relevant biological properties are discussed.

Laser-Based Gene Transfection and Gene Therapy

Abstract: The plasma membrane of mammalian cells can be transiently permeablized by optical means and exogenous materials or genes can be introduced into the cytoplasm of living cells. Until now, few mechanisms were exploited for the manipulation: laser is directly and tightly focused on the cells for optoinjection, laser-induced stress waves, photochemical internalization, and irradiation of selective cell targeting with light-absorbing particles. During the past few years, extensive progress and numerous breakthroughs have been made in this area of research. This review covers four different laser-assisted transfection techniques and their advantages and disadvantages. Universality towards various cell lines is possibly the main advantage of laser-assisted optoporation in comparison with presently existing methods of cell transfection.

A Combination of Rough-Based Feature Selection and RBF Neural Network for Classification Using Gene Expression Data

Abstract: This paper presents a novel rough-based feature selection method for gene expression data analysis. It can find the relevant features without requiring the number of clusters to be known a priori and identify the centers that approximate to the correct ones. In this paper, we attempt to introduce a prediction scheme that combines the rough-based feature selection method with radial basis function neural network. For further consider the effect of different feature selection methods and classifiers on this prediction process, we use the Naive Bayes and linear support vector machine as classifiers, and compare the performance with other feature selection methods, including information gain and principle component analysis. We demonstrate the performance by several published datasets and the results show that our proposed method can achieve high classification accuracy rate.

Carbon Nanotube Fibers Are Compatible With Mammalian Cells and Neurons

Abstract: We demonstrate the biocompatibility of carbon nanotube fibers (CNFs) fabricated from single-wall carbon nanotubes. Produced by a particle-coagulation spinning process, CNFs are "hair-like" conductive microwires, which uniquely combine properties of porous nanostructured scaffolds, high-area electrodes, and permeable microfluidic conduits. We report that CNFs are nontoxic and support the attachment, spreading, and growth of mammalian cells and the extension of processes from neurons in vitro. Our findings suggest that CNF may be employed for an electrical interfacing of nerve cells and external devices.

Energy Absorption of Gold Nanoshells in Hyperthermia Therapy

Abstract: The unique optical characteristics of a gold nanoshell motivate the application of nanoshell-based hyperthermia in drug delivery and cancer treatment. However, most of our understanding on energy absorption and heat transfer is still focused on individual particles, which may not be accurate for nanoshell aggregates in a real application due to the strong optical interaction of nanoshells. This paper investigates the relationship between the optical interaction and the interparticle distance in the visible and near-infrared regions by means of a finite-difference time-domain (FDTD) method. The objective is to explore the energy transportation mechanism, which is critical for hyperthermia therapy. From the numerical simulation results of different forms of nanoshell aggregates, including individual nanoshells, 1-D chains, 2-D arrays, and 3-D clusters, it was found that the interparticle distance plays a crucial role from the maximal absorption point of view. The interparticle distance affects both field enhancement and surface plasmon resonance position. The accurate prediction of energy absorption also helps the way nanoshells are populated in the tumor cell so as to prevent heat damage to healthy tissues in clinic applications. In the case of 3-D clusters, the laser energy decays exponentially along the wave propagation, and the penetration depth greatly depends on the interparticle distance. The closer the nanoshells are placed, the shorter the penetration depth is. The maximal total length for the laser penetration through the shell of gold nanoparticles is about a few hundred to several nanometers. The actual penetration depth primarily depends not only on the interparticle distance, but also on the size of the nanoshells as well as other factors. Since the absorption energy is concentrated on the surface clusters of nanoparticles, heat transfer mechanisms in metal-nanoparticles-based hyperthermia will differ from that in other hyperthermia. The information obtained from this paper will serve as a basis for further study of heat transfer in metal-nanoparticles-based hyperthermia.

Numerical Study on the Multi-Region Bio-Heat Equation to Model Magnetic Fluid Hyperthermia (MFH) Using Low Curie Temperature Nanoparticles

Abstract: This study develops and solves two-dimensional convective-conductive coupled partial differential equations based on Pennes' bio-heat transfer model using low Curie temperature nanoparticles (LCTNPs) to illustrate thermal behavior quantitatively within tumor-normal composite tissue by establishing a multi-region finite difference algorithm. The model combines Neel relaxation and temperature-variant saturation magnetization derived from Brillouin Equation and Curie-Weiss Law. The numerical results indicate that different deposition patterns of LCTNP and boundary conditions directly effect the steady state temperature distribution. Compared with high Curie temperature nanoparticles (HCTNPs), optimized distributions of LCTNPs within tumorous tissue can be used to control the temperature increase in tumors for hyperthermia treatment using an external magnetic field while healthy tissue surrounding a tumor can be kept closer to normal body tissue, reducing the side effects observed in whole body and regional hyperthermia therapy.

Investigation of Self-Organizing Oscillator Networks for Use in Clustering Microarray Data

Abstract: The self-organizing oscillator network (SOON) is a comparatively new clustering algorithm that does not require the knowledge of the number of clusters. The SOON is distance based, and its clustering behavior is different to density-based algorithms in a number of ways. This paper examines the effect of adjusting the control parameters of the SOON with four different datasets; the first is a (communications) modulation dataset representing one modulation scheme under a variety of noise conditions. This allows the assessment of the behavior of the algorithm with data varying between highly separable and nonseparable cases. The main thrust of this paper is to evaluate its efficacy in biological datasets. The second is taken from microarray experiments on the cell cycle of yeast, while the third and the fourth represent two microarray cancer datasets, i.e., the lymphoma and the liver cancer datasets. The paper demonstrates that the SOON is a viable tool to analyze these problems, and can add many useful insights to the biological data that may not always be available using other clustering methods.

Bioelectronic Imaging Array Based on Bacteriorhodopsin Film

Abstract: A photoreceptor array that exploits the light sensitive bacteriorhodopsin (bR) films has been manufactured on a flexible indium-tin-oxide (ITO) coated plastic film using electrophoretic sedimentation technique (EPS). The effective sensing area of each photoreceptor is 2 × 2 mm2, separated by 1 mm and arranged in a 4 × 4 array. A switched integrator with gain on the order of 1010 is used to amplify the signal to a suitable level. When exposed to light, the differential response characteristic is attributed to charge displacement and recombination within bR molecules, as well as loading effects of the attached amplifier. The peak spectral response occurs at 568 nm and is linear over the tested light power range of 200 ? W to 12 mW. The response remains linear at other tested wavelengths, but with reduced amplitude. Initial tests have indicated that responsivity among all photoreceptors is greater than 71% of the average value, 465.25 mV/mW. The differential nature of the signal generated by bR makes it a suitable sensing material for vision applications such as motion detection. The prototype array demonstrates this property by employing Reichardt's delay-and-correlate algorithm. Furthermore, fabricating sensor arrays on flexible substrates introduces a new design approach that enables non-planar imaging surfaces.

DomNet: Protein Domain Boundary Prediction Using Enhanced General Regression Network and New Profiles

Abstract: The accurate and stable prediction of protein domain boundaries is an important avenue for the prediction of protein structure, function, evolution, and design. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques. In this paper, we propose a new machine learning based domain predictor namely, DomNet that can show a more accurate and stable predictive performance than the existing state-of-the-art models. The DomNet is trained using a novel compact domain profile, secondary structure, solvent accessibility information, and interdomain linker index to detect possible domain boundaries for a target sequence. The performance of the proposed model was compared to nine different machine learning models on the Benchmark_2 dataset in terms of accuracy, sensitivity, specificity, and correlation coefficient. The DomNet achieved the best performance with 71% accuracy for domain boundary identification in multidomains proteins. With the CASP7 benchmark dataset, it again demonstrated superior performance to contemporary domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut, and DomainDiscovery.

Magnetically Targeted Viral Envelopes: A PET Investigation of Initial Biodistribution

Abstract: Gene and drug therapy for organ-specific diseases in part depends on the efficient delivery to a particular region of the body. We examined the biodistribution of a viral envelope commonly used as a nanoscale gene delivery vehicle using positron emission tomography (PET) and investigated the magnetic alteration of its biodistribution. Iron oxide nanoparticles and 18F-fluoride were encapsulated by hemagglutinating virus of Japan envelopes (HVJ-Es). HVJ-Es were then injected intravenously in the rat and imaged dynamically using high-resolution PET. Control subjects received injections of encapsulated materials alone. For magnetic targeting, permanent magnets were fixed on the head during the scan. Based on the quantitative analysis of PET images, HVJ-Es accumulated in the liver and spleen and activity remained higher than control subjects for 2 h. Histological sections of the liver confirmed imaging findings. Pixel-wise activity patterns on coregistered PET images of the head showed a significantly different pattern for the subjects receiving magnetic targeting as compared to all control groups. Imaging demonstrated the initial biodistribution of a viral envelope within the rodent by providing quantitative behavior over time and in specific anatomical regions. Magnetic force altered the biodistribution of the viral envelope to a target structure, and could enable region-specific delivery of therapeutic vehicles noninvasively.

Functionalized Solid Lipid Nanoparticles for Transendothelial Delivery

Abstract: The objectives of this study were to synthesize and characterize functionalized solid lipid nanoparticles (fSLN) to investigate their interaction with endothelial cell monolayers and to evaluate their transendothelial transport capabilities. fSLN bearing tetramethylrhodamine-isothiocyanate-labeled bovine serum albumin (TRITC-BSA) and Coumarin 6 were prepared using a single-step phase-inversion process that afforded concurrent surface modification with a variety of macromolecules such as polystyrene sulfonate (PSS), poly-L-lysine (PLL), heparin (Hep), polyacrylic acid (PAA), polyvinyl alcohol, and polyethylene glygol (PEG). TRITC-BSA/Coumarin 6 encapsulated in fSLN with composite surface functionality (PSS-PLL and PSS-PLL-Hep) were also investigated. Size and surface charge of fSLN were analyzed using dynamic light scattering and transmission electron microscopy. Transport across bovine aortic endothelial cell (BAEC) monolayers was assessed spectrophotometrically using a transwell assay, and fSLN localization at the level of the cell and permeable support was analyzed using fluorescence microscopy. fSLN with tunable size and surface functionality were successfully produced, and had significant effects on cell localization and transport. Specifically, fSLN with PSS-PLL-Hep composite surface functionalization was capable of translocating 53.2 plusmn 8.7 mug of TRITC-BSA within 4 h, with fSLN-PEG, fSLN-PAA, and fSLN-PSS exhibiting near-complete apical, paracellular, and cytosolic localization, respectively. Coumarin 6 was released by fSLN as indicated by dye labeling of BAEC membranes. We have developed a rapid process for the production of fSLN bearing low- and high-molecular-weight payloads of varying physicochemical properties. These findings have implications for drug delivery and bioimaging applications, since due to tunable surface chemistry, fSLN internalization and/or translocation across intact endothelial cell monolayers is possible.

Molecular Delivery to Cells Facilitated by Corona Ion Deposition

Abstract: A novel method of inducing the delivery of nonpermeant molecules to the cytosol of cells is presented in this paper. Corona discharge in air was utilized to produce ions that in turn were deposited onto the liquid surface of media containing cultured cells. Murine B16 melanoma cells were used to demonstrate the molecular delivery of fluorescent dye calcein, the drug bleomycin, and a nucleic acid stain SYTOX-green. None of these molecules penetrate cells with intact membranes. Following the corona treatment, cells were observed to admit significant quantities of these molecules from the culture media, relative to control samples. Further, greater than 95% viability of treated cells was observed by Trypan Blue assay. This method may provide an attractive alternative to electroporation where a physical contact between electrodes and cells is needed to deliver molecules to the cytosol.

Neuroblastoma Cells Displacement by Magnetic Carbon Nanotubes

Abstract: In this paper, as-produced multiwall carbon nanotubes (MWNTs) have been analyzed by scanning electron microscopy and energy dispersive X-ray spectrometry, revealing the presence of Fe, Al, and Zn residuals and impurities. MWNTs have then been dispersed in Pluronic F127 aqueous solution and used to seed neuroblastoma cell lines (HN9.10e and SH-SY5Y) for three days. We found that MWNTs interact with cells and induce, under a permanent constant magnetic field, the cell displacement toward the magnetic source.

Quantum Algorithms for Biomolecular Solutions of the Satisfiability Problem on a Quantum Machine

Abstract: In this paper, we demonstrate that the logic computation performed by the DNA-based algorithm for solving general cases of the satisfiability problem can be implemented more efficiently by our proposed quantum algorithm on the quantum machine proposed by Deutsch. To test our theory, we carry out a three-quantum bit nuclear magnetic resonance experiment for solving the simplest satisfiability problem.

Distributed Sequence Alignment Applications for the Public Computing Architecture

Abstract: The public computer architecture shows promise as a platform for solving fundamental problems in bioinformatics such as global gene sequence alignment and data mining with tools such as the basic local alignment search tool (BLAST). Our implementation of these two problems on the Berkeley open infrastructure for network computing (BOINC) platform demonstrates a runtime reduction factor of 1.15 for sequence alignment and 16.76 for BLAST. While the runtime reduction factor of the global gene sequence alignment application is modest, this value is based on a theoretical sequential runtime extrapolated from the calculation of a smaller problem. Because this runtime is extrapolated from running the calculation in memory, the theoretical sequential runtime would require 37.3 GB of memory on a single system. With this in mind, the BOINC implementation not only offers the reduced runtime, but also the aggregation of the available memory of all participant nodes. If an actual sequential run of the problem were compared, a more drastic reduction in the runtime would be seen due to an additional secondary storage I/O overhead for a practical system. Despite the limitations of the public computer architecture, most notably in communication overhead, it represents a practical platform for grid- and cluster-scale bioinformatics computations today and shows great potential for future implementations.

FIB-Nanostructured Surfaces and Investigation of Bio/Nonbio Interactions at the Nanoscale

Abstract: A better understanding of the interactions between biological entities and nanostructures is of central importance for developing functionalized materials and systems such as active surfaces with adapted biocompatibility There is clear evidence in literature that cells and proteins generally interact with nanoscale-featured surfaces Despite this quantity of information, little is known about the functional relationship between surface properties (ie, roughness and nanostructuration) and biomolecules interaction The main obstacle in the achievement of this goal is a technological one Precise and straightforward control on surface modification at the nanometer level is required for understanding how nanostructuration influences interactions at bio/nonbio interface In this paper, the authors describe the advantages of the focused ion beam (FIB) for surface nanostructuration of any material The use of light transmitting substrates (especially glass) is often useful when studying the influence of surface morphology-in terms of shape and feature size-on bio/nonbio interactions by using traditional methods of biology and biotechnology A simple methodology enabling a very efficient patterning of glass surfaces is thus described and validated: the enhancement of proteins interaction on FIB-nanostructured glass surfaces is demonstrated via fluorescence assays and a relationship between the adsorbed protein concentration and the density of surface patterning is derived

Estimating Gene Signals From Noisy Microarray Images

Abstract: In oligonucleotide microarray experiments, noise is a challenging problem, as biologists now are studying their organisms not in isolation but in the context of a natural environment. In low photomultiplier tube (PMT) voltage images, weak gene signals and their interactions with the background fluorescence noise are most problematic. In addition, nonspecific sequences bind to array spots intermittently causing inaccurate measurements. Conventional techniques cannot precisely separate the foreground and the background signals. In this paper, we propose analytically based estimation technique. We assume a priori spot-shape information using a circular outer periphery with an elliptical center hole. We assume Gaussian statistics for modeling both the foreground and background signals. The mean of the foreground signal quantifies the weak gene signal corresponding to the spot, and the variance gives the measure of the undesired binding that causes fluctuation in the measurement. We propose a foreground-signal and shape-estimation algorithm using the Gibbs sampling method. We compare our developed algorithm with the existing Mann-Whitney (MW)- and expectation maximization (EM)/iterated conditional modes (ICM)-based methods. Our method outperforms the existing methods with considerably smaller mean-square error (MSE) for all signal-to-noise ratios (SNRs) in computer-generated images and gives better qualitative results in low-SNR real-data images. Our method is computationally relatively slow because of its inherent sampling operation and hence only applicable to very noisy-spot images. In a realistic example using our method, we show that the gene-signal fluctuations on the estimated foreground are better observed for the input noisy images with relatively higher undesired bindings.

Estimating Locations of Quantum-Dot-Encoded Microparticles From Ultra-High Density 3-D Microarrays

Abstract: We develop a maximum likelihood (ML)-based parametric image deconvolution technique to locate quantum-dot (q-dot) encoded microparticles from three-dimensional (3-D) images of an ultra-high density 3-D microarray. A potential application of the proposed microarray imaging is assay analysis of gene, protein, antigen, and antibody targets. This imaging is performed using a wide-field fluorescence microscope. We first describe our problem of interest and the pertinent measurement model by assuming additive Gaussian noise. We use a 3-D Gaussian point-spread-function (PSF) model to represent the blurring of the widefield microscope system. We employ parametric spheres to represent the light intensity profiles of the q-dot-encoded microparticles. We then develop the estimation algorithm for the single-sphere-object image assuming that the microscope PSF is totally unknown. The algorithm is tested numerically and compared with the analytical Cramer-Rao bounds (CRB). To apply our analysis to real data, we first segment a section of the blurred 3-D image of the multiple microparticles using a k-means clustering algorithm, obtaining 3-D images of single-sphere-objects. Then, we process each of these images using our proposed estimation technique. In the numerical examples, our method outperforms the blind deconvolution (BD) algorithms in high signal-to-noise ratio (SNR) images. For the case of real data, our method and the BD-based methods perform similarly for the well-separated microparticle images.

Correlations Between the Experimental and Numerical Investigations on the Mechanical Properties of Erythrocyte by Laser Stretching

Abstract: The deformation and mechanical properties of the erythrocytes are studied experimentally and numerically. For the experimental part, an osmotic swollen spherical erythrocyte was attached with a pair of silica beads, and then stretched at two opposite ends by a laser trap. The purpose of this experiment is to find the empirical correlation between the stretching force and the cell deformation in terms of the transverse strain, which is a measure of the change of radius in a spherical cell along its equator. Experimental results show the cell shape become more oblate, elliptic as the stretching force increases. On the numerical front, a physical model from the original work by Pamplona and Calladine for the lipsomes was extended to simulate the deformation of the cell membrane. Numerical analyses were performed to solve the nondimensionalized governing equations with proper boundary conditions imposed to simulate the experimental conditions. The simulated results indicate that at high tensile stiffness, the cell can deform into a spindle shape with negative curvature close to the ends of stretch. Finally, the experimental data and the simulated results were correlated through optimization by minimizing their discrepancy at various values of the shear stiffness. The optimal value of shear stiffness was found in the range of 2.35 ~ 4.29 X 10-6 N-m-1, which is comparable with those values reported in the literature.

Preparation of $\hbox{F}_{0}\hbox{F}_{1}$ -ATPase Nanoarray by Dip-Pen Nanolithography and Its Application as Biosensors

Abstract: Dip-pen nanolithography (DPN) is a widely used technique to create nanoscopic patterns of many different materials. F0F1 -ATPase is a nanoscale rotary molecular motor, and could be used as a biosensor or an ideal motor device in a micro-/nanosystem. In this paper, the DPN technique was used to create nanoarrays of F0F1 -ATPase within chromatophore on a gold surface. The feature size of our F0F1 -ATPase patterns was an average of 130 nm, and mathematically, there were no more than ten F0F1 -ATPases in each dot. The biological activity of patterned F0F1 -ATPase was demonstrated by its adenosine triphosphate synthesis, which was indicated by the fluorescence change of labeled F1300. The patterned F0F1 -ATPase nanoarrays were further constructed as biosensors to detect H9 influenza A virus. The results showed that the biosensor arrays had a remarkable S/N ratio and excellent specificity. This type of biosensor arrays can be further used in high-throughput, high-sensitive detection in future. Meanwhile, the precise patterning of F0F1 -ATPase with desired size, position, and biological activity would accelerate its application in many fields.

Spatial Distribution of the Electric Potential From Photosystem I Reaction Centers in Lipid Vesicles

Abstract: Photosynthetic reaction centers are integral membrane complexes that produce a net transmembrane charge separation in response to light. The Photosystem I (PSI) complex is a thoroughly studied reaction center that has been proposed as a nanoscale photovoltaic structure in diverse applications, including activation of excitable cells by triggering of voltage-gated ion channels. An electrostatic model of a spherical lipid vesicle embedded with PSI and suspended in an aqueous medium is presented. The distribution of the electric potential is obtained by solving the nonlinear Poisson-Boltzmann equation with the finite-element method. The model predicts a maximum potential difference of 1.3 V between charges. This value depends mostly on the intrinsic dielectric constants of the reaction center and distance between charges. However, the potential distribution near the reaction center depends on the ionic strength of the aqueous medium. When the ionic strength is zero, the vesicle develops a transmembrane potential that increases linearly with the density of reaction centers. When the ionic strength increases, this potential difference approaches to zero. The main results of the simulations are consistent with previously reported experimental data. Based on the presented results, the potential application of PSI to light activation of voltage-gated ion channels is discussed.

DNA Binding Site Characterization by Means of RÉnyi Entropy Measures on Nucleotide Transitions

Abstract: In this work, parametric information-theory measures for the characterization of binding sites in DNA are extended with the use of transitional probabilities on the sequence. We propose the use of parametric uncertainty measures such as Renyi entropies obtained from the transition probabilities for the study of the binding sites, in addition to nucleotide frequency-based Renyi measures. Results are reported in this work comparing transition frequencies (i.e., dinucleotides) and base frequencies for Shannon and parametric Renyi entropies for a number of binding sites found in E. Coli, lambda, and T7 organisms. We observe that the information provided by both approaches is not redundant. Furthermore, under the presence of noise in the binding site matrix we observe overall improved robustness of nucleotide transition-based algorithms when compared with nucleotide frequency-based method.

Construction and Evaluation of an Automated Light Directed Protein-Detecting Microarray Synthesizer

Abstract: We have designed, constructed, and evaluated an automated instrument that has produced high-density arrays with more than 30 000 peptide features within a 1.5 area of a glass slide surface. These arrays can be used for high throughput library screening for protein binding ligands, for potential drug candidate molecules, or for discovering biomarkers. The device consists of a novel fluidics system, a relay control electrical system, an optics system that implements Texas Instrumentspsila digital micromirror device (DMD), and a microwave source for accelerated synthesis of peptide arrays. The instrument implements two novel solid phase chemical synthesis strategies for producing peptide and peptoid arrays. Biotin-streptavidin and DNP anti-DNP (dinitrophenol) models of antibody small molecule interactions were used to demonstrate and evaluate the instrument's capability to produce high-density protein detecting arrays. Several screening assay and detection schemes were explored with various levels of efficiency and assays with sensitivity of 10 nM were also possible.

Error Tolerant DNA Self-Assembly Using ( $\hbox{2}k-\hbox{1}$ ) $\times$ ( $\hbox{2}k-\hbox{1}$ ) Snake Tile Sets

Abstract: DNA self-assembly has been advocated as a possible technique for bottom-up manufacturing of scaffolds for computing systems in the nanoscale region. However, self-assembly is affected by different types of errors (such as growth and facet roughening) that severely limit its applicability. Different methods for reducing the error rate of self-assembly using tiles as basic elements have been proposed. A particularly effective method relies on snake tile sets that utilize a square block of even size (i.e., 2k times 2k tiles, k = 2, 3,.. .). In this paper, an odd-sized square block [i.e., (2k -1) times (2k - 1)] is proposed as basis for the snake tile set. Compared with other tile sets, the proposed snake tile sets achieve a considerable reduction in error rate at a very modest reduction in growth rate. Growth and facet roughening errors are considered and analytical results are presented to prove the reduction in error rate compared with an even-sized snake tile set. Simulation results are provided.

Analysis of Punctures in DNA Self-Assembly Under Forward Growth

Abstract: This paper deals with the characterization and analysis of intentionally induced punctures on a DNA self-assembly. Based on forward growth, punctures are utilized to remove errors in DNA tiles from the self-assembly. Initially, a Markov model is proposed by considering different types of punctures under various bonding conditions in the tiles. For different values of on and off rates (as corresponding to the parameters G se and G mc ), it is shown that the proposed models can assess the types of puncture for removing mitsmatched tiles as errors. Subsequently, a novel model of puncturing is introduced to establish the condition by which a generic aggregate can utilize punctures for error resilience. It is proven that by using the correct puncture(s), errors as frozen mismatched tiles are moved toward the boundaries, thus ensuring the generation of the target assembly and ease in removal of the errors. As an example, the Sierpinski tile set is analyzed based on the proposed models to fully assess the appropriate type of puncture for this pattern. Simulation results are provided as evidence that the proposed models are effective.