Kinetics of binding and geometry of cells on molecular biochips

We examine how the shape of cells and the geometry of experiment affect the reaction-diffusion kinetics at the binding between target and probe molecules on molecular biochips. In particular, we compare the binding kinetics for the probes immobilized on surface of the semispherical and flat circular cells, the limit of thin slab of analyte solution over probe cell as well as hemispherical gel pads and cells printed in gel slab over a substrate. It is shown that hemispherical geometry provides significantly faster binding kinetics and ensures more spatially homogeneous distribution of local (from a pixel) signals over a cell in the transient regime. The advantage of using thin slabs with small volume of analyte solution may be hampered by the much longer binding kinetics needing the auxiliary mixing devices. Our analysis proves that the shape of cells and the geometry of experiment should be included to the list of essential factors at biochip designing.Comment: 10 pages, 1 figur

Use of Complex DNA and Antibody Microarrays as Tools in Functional Analyses

While the deciphering of basic sequence information on a genomic scale is yielding complete genomic sequences in ever-shorter intervals, experimental procedures for elucidating the cellular effects and consequences of the DNA-encoded information become critical for further analyses. In recent years, DNA microarray technology has emerged as a prime candidate for the performance of many such functional assays. Technically, array technology has come a long way since its conception some 15 years ago, initially designed as a means for large-scale mapping and sequencing

Fast image processing with constraints by solving linear PDEs

We present a general framework that allows image filtering by minimization of a functional using a linear and positive definite partial differential equation (PDE) while also permitting to control the weight of each pixel individually. Linearity and positive definiteness allow to use fast algorithms to calculate the solution. Pixel weighting allows to enforce the preservation of edge information without the need for nonlinear diffusion by making use of information coming from an external source. The proof of existence and uniqueness of the solution is outlined and based on that a numerical scheme for finding the solution is introduced. Using this framework we developed two applications. The first is simple and fast denoising, which incorporates an edge detection algorithm. In this case the functional is designed to enhance the weight of the approximation term over the smoothing term at those places where an edge is detected. The second application is a background suppression algorithm that is robust against noise, shadows thrown by the object, and on the background and varying illumination. The results are qualitatively not quite as good as the ones obtained with nonlinear PDEs, but this disadvantage is compensated by the processing speed, which allows analysis of a 320×240 color frame in about 0.3s on a standard PC

Die Osterweiterung der EU: Chancen und Gefahren für die Kaliningrader Oblast der Russischen Föderation

Bei dem vorliegenden Text handelt es sich um einen Vortrag des Autors im Rahmen des SCHIFF-Kolloquiums "Kooperation und Konflikt in der Ostseeregion". Der Autor ist seit 1995 als Vertreter des Außenministeriums der Russischen Föderation im Gebiet Kaliningrad tätig und skizziert in seinem Vortrag seine persönlichen Eindrücke und Erfahrungen im Kaliningrader Gebiet, das er als die "Oblast einer Stadt" bezeichnet. Er beschreibt den regionalen "acquis communautaire" und diskutiert die Chancen und Gefahren der Osterweiterung der EU für Kaliningrad. Um zu verdeutlichen, dass die Kaliningrader Bevölkerung die Entwicklung der Beziehungen zu ihren Nachbarn befürwortet, stellt er ferner einige Ergebnisse von Umfragen aus den Jahren 1996 und 2000 vor. (ICI

Miniaturized Protein Microarray with Internal Calibration as Point-of-Care Device for Diagnosis of Neonatal Sepsis

Neonatal sepsis is still a leading cause of death among newborns. Therefore a protein-microarray for point-of-care testing that simultaneously quantifies the sepsis associated serum proteins IL-6, IL-8, IL-10, TNF alpha, S-100, PCT, E-Selectin, CRP and Neopterin has been developed. The chip works with only a 4 μL patient serum sample and hence minimizes excessive blood withdrawal from newborns. The 4 μL patient samples are diluted with 36 μL assay buffer and distributed to four slides for repetitive measurements. Streptavidin coated magnetic particles that act as distinct stirring detection components are added, not only to stir the sample, but also to detect antibody antigen binding events. We demonstrate that the test is complete within 2.5 h using a single step assay. S-100 conjugated to BSA is spotted in increasing concentrations to create an internal calibration. The presented low volume protein-chip fulfills the requirements of point-of-care testing for accurate and repeatable (CV < 14%) quantification of serum proteins for the diagnosis of neonatal sepsis

Assessment and optimisation of normalisation methods for dual-colour antibody microarrays

<p>Abstract</p> <p>Background</p> <p>Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expression microarrays. Thus, the established normalisation methods for gene expression microarrays, e.g. loess regression, can in principle be applied to protein microarrays. However, the typical assumptions of such normalisation methods might be violated due to a bias in the selection of the proteins to be measured. Due to high costs and limited availability of high quality antibodies, the current arrays usually focus on a high proportion of regulated targets. Housekeeping features could be used to circumvent this problem, but they are typically underrepresented on protein arrays. Therefore, it might be beneficial to select invariant features among the features already represented on available arrays for normalisation by a dedicated selection algorithm.</p> <p>Results</p> <p>We compare the performance of several normalisation methods that have been established for dual-colour gene expression microarrays. The focus is on an invariant selection algorithm, for which effective improvements are proposed. In a simulation study the performances of the different normalisation methods are compared with respect to their impact on the ability to correctly detect differentially expressed features. Furthermore, we apply the different normalisation methods to a pancreatic cancer data set to assess the impact on the classification power.</p> <p>Conclusions</p> <p>The simulation study and the data application demonstrate the superior performance of the improved invariant selection algorithms in comparison to other normalisation methods, especially in situations where the assumptions of the usual global loess normalisation are violated.</p

A semi-nonparametric mixture model for selecting functionally consistent proteins

Background High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins. Results We present a novel semi-nonparametric mixture model for classifying proteins as consistent or inconsistent while controlling the false discovery rate and the false non-discovery rate. The performance of the proposed approach is compared to current methods via simulation under a variety of experimental conditions. Conclusions We provide a statistical method for selecting functionally consistent proteins in the context of protein microarray experiments, but the proposed semi-nonparametric mixture model method can certainly be generalized to solve other mixture data problems. The main advantage of this approach is that it provides the posterior probability of consistency for each protein