scholarly journals Temporal and structural analysis of biological networks in combination with microarray data

2008 ◽  
Author(s):  
Chang Hun You ◽  
Lawrence B. Holder ◽  
Diane J. Cook
Keyword(s):  
Structural Analysis ◽  
Microarray Data ◽  
Biological Networks

scholarly journals Microarray Data Analysis: From Hypotheses to Conclusions Using Gene Expression Data

2004 ◽  
Vol 26 (5-6) ◽  
pp. 279-290
Author(s):  
Nicola J. Armstrong ◽  
Mark A. van de Wiel
Keyword(s):  
Microarray Data ◽  
Biological Networks ◽  
Multiple Testing ◽  
Gene Selection ◽  
Statistical Testing ◽  
Microarray Data Analysis ◽  
Gene Sets ◽  
New Development ◽  
Statistical Issues ◽  
Critical Interpretation

We review several commonly used methods for the design and analysis of microarray data. To begin with, some experimental design issues are addressed. Several approaches for pre‐processing the data (filtering and normalization) before the statistical analysis stage are then discussed. A common first step in this type of analysis is gene selection based on statistical testing. Two approaches, permutation and model‐based methods are explained and we emphasize the need to correct for multiple testing. Moreover, powerful approaches based on gene sets are mentioned. Clustering of either genes or samples is frequently performed when analyzing microarray data. We summarize the basics of both supervised and unsupervised clustering (classification). The latter may be of use for creating diagnostic arrays, for example. Construction of biological networks, such as pathways, is a statistically challenging but complex task that is a relatively new development and hence mentioned only briefly. We finish with some remarks on literature and software. The emphasis in this paper is on the philosophy behind several statistical issues and on a critical interpretation of microarray related analysis methods.

Biological networks to the analysis of microarray data

2006 ◽  
Vol 16 (12) ◽  
pp. 1242-1251 ◽  
Author(s):  
Fang Zhou ◽  
Luo Qingming ◽  
Zhang Guoqing ◽  
Li Ixue
Keyword(s):  
Microarray Data ◽  
Biological Networks

Structural analysis of the surface-layer protein of spirillum serpens by high-resolution electron microscopy

1983 ◽  
Vol 41 ◽  
pp. 738-739
Author(s):  
W. H. Wu ◽  
R. M. Glaeser
Keyword(s):  
Electron Microscopy ◽  
Surface Layer ◽  
Structural Analysis ◽  
High Resolution ◽  
Low Dose ◽  
High Resolution Electron Microscopy ◽  
Optical Diffraction ◽  
Surface Layer Protein ◽  
Morphological Unit ◽  
Resolution Electron

Spirillum serpens possesses a surface layer protein which exhibits a regular hexagonal packing of the morphological subunits. A morphological model of the structure of the protein has been proposed at a resolution of about 25 Å, in which the morphological unit might be described as having the appearance of a flared-out, hollow cylinder with six ÅspokesÅ at the flared end. In order to understand the detailed association of the macromolecules, it is necessary to do a high resolution structural analysis. Large, single layered arrays of the surface layer protein have been obtained for this purpose by means of extensive heating in high CaCl2, a procedure derived from that of Buckmire and Murray. Low dose, low temperature electron microscopy has been applied to the large arrays.As a first step, the samples were negatively stained with neutralized phosphotungstic acid, and the specimens were imaged at 40,000 magnification by use of a high resolution cold stage on a JE0L 100B. Low dose images were recorded with exposures of 7-9 electrons/Å2. The micrographs obtained (Fig. 1) were examined by use of optical diffraction (Fig. 2) to tell what areas were especially well ordered.

RNA polymerase: Preparation and structural analysis of helical polymers

1984 ◽  
Vol 42 ◽  
pp. 152-153
Author(s):  
E. Loren Buhle ◽  
Pamela Rew ◽  
Ueli Aebi
Keyword(s):  
Structural Analysis ◽  
Rna Polymerase ◽  
Molecular Level ◽  
X Ray Diffraction ◽  
Helical Polymers ◽  
X Ray ◽  
E Coli ◽  
The Past ◽  
Ordered Arrays ◽  
Low Ionic Strength

While DNA-dependent RNA polymerase represents one of the key enzymes involved in transcription and ultimately in gene expression in procaryotic and eucaryotic cells, little progress has been made towards elucidation of its 3-D structure at the molecular level over the past few years. This is mainly because to date no 3-D crystals suitable for X-ray diffraction analysis have been obtained with this rather large (MW ~500 kd) multi-subunit (α2ββ'ζ). As an alternative, we have been trying to form ordered arrays of RNA polymerase from E. coli suitable for structural analysis in the electron microscope combined with image processing. Here we report about helical polymers induced from holoenzyme (α2ββ'ζ) at low ionic strength with 5-7 mM MnCl2 (see Fig. 1a). The presence of the ζ-subunit (MW 86 kd) is required to form these polymers, since the core enzyme (α2ββ') does fail to assemble into such structures under these conditions.

Interpreting HVEM of muscle-cell impulse networks

1992 ◽  
Vol 50 (1) ◽  
pp. 126-127
Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn
Keyword(s):  
Structural Analysis ◽  
Muscle Cell ◽  
Electron Micrographs ◽  
Relative Size ◽  
Automatic Classification ◽  
Nerve Fibers ◽  
Analysis Algorithm ◽  
Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

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