Final Technical Report--Quantitative analysis of metabolic regulation by integration of metabolomics, proteomics, and fluxomics

Quantitative analysis of experimental metabolic data is frequently challenged by non-intuitive, complex patterns which emerge from regulatory networks. Quantitative output of metabolic regulation can be summarised by metabolic functions which comprise information about dynamics of metabolite concentrations. They reflect the sum of biochemical reactions which affect a metabolite concentration. Derivatives of metabolic functions provide essential information about system dynamics. The Jacobian matrix of a reaction network summarises first-order partial derivatives of metabolic functions with respect to metabolite concentrations while Hessian matrices summarise second-order partial derivatives. Here, a simple model of invertase-driven sucrose hydrolysis is simulated and both Jacobian and Hessian matrices of metabolic functions are derived for quantitative analysis of kinetic regulation of sucrose metabolism. Based on previous experimental observations, metabolite dynamics are quantitatively explained in context of underlying metabolic functions. Their potential regulatory role during plant cold acclimation is derived from Jacobian and Hessian matrices.

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Calibration, standardization, and quantitative analysis of multidimensional fluorescence (MDF) measurements on complex mixtures (IUPAC Technical Report)

Pure and Applied Chemistry ◽

10.1515/pac-2017-0610 ◽

2017 ◽

Vol 89 (12) ◽

pp. 1849-1870 ◽

Cited By ~ 9

Author(s):

Alan G. Ryder ◽

Colin A. Stedmon ◽

Niels Harrit ◽

Rasmus Bro

Keyword(s):

Data Analysis ◽

Quantitative Analysis ◽

Complex Mixtures ◽

Spectroscopy Data ◽

Chemometric Methods ◽

Short Introduction ◽

Technical Report ◽

Calibration Sample ◽

Multidimensional Fluorescence ◽

Data Analysis Methods

AbstractThis IUPAC Technical Report describes and compares the currently applied methods for the calibration and standardization of multi-dimensional fluorescence (MDF) spectroscopy data as well as recommendations on the correct use of chemometric methods for MDF data analysis. The paper starts with a brief description of the measurement principles for the most important MDF techniques and a short introduction to the most important applications. Recommendations are provided for instrument calibration, sample preparation and handling, and data collection, as well as the proper use of chemometric data analysis methods.

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Quantitative analysis of PbO-SiO2 glass by electron microprobe (Technical Report)

Pure and Applied Chemistry ◽

10.1351/pac199668081657 ◽

1996 ◽

Vol 68 (8) ◽

pp. 1657-1663 ◽

Cited By ~ 1

Author(s):

J. Matousek ◽

V. Hulínsky ◽

R. Metselaar ◽

John Corish

Keyword(s):

Quantitative Analysis ◽

Electron Microprobe ◽

Sio2 Glass ◽

Technical Report

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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Quantification of Silica Uptake by Alveolar Macrophages - An Emperical Scanning Electron Microprobe Method

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100054297 ◽

1982 ◽

Vol 40 ◽

pp. 332-333

Author(s):

V. V. Damiano ◽

R. P. Daniele ◽

H. T. Tucker ◽

J. H. Dauber

Keyword(s):

Quantitative Analysis ◽

Alveolar Macrophages ◽

Bulk Sample ◽

Silica Particles ◽

Empirical Method ◽

Phagocytic Cells ◽

Calibration Standards ◽

X Ray ◽

Accurate Quantitative Analysis ◽

Scanning Electron

An important example of intracellular particles is encountered in silicosis where alveolar macrophages ingest inspired silica particles. The quantitation of the silica uptake by these cells may be a potentially useful method for monitoring silica exposure. Accurate quantitative analysis of ingested silica by phagocytic cells is difficult because the particles are frequently small, irregularly shaped and cannot be visualized within the cells. Semiquantitative methods which make use of particles of known size, shape and composition as calibration standards may be the most direct and simplest approach to undertake. The present paper describes an empirical method in which glass microspheres were used as a model to show how the ratio of the silicon Kα peak X-ray intensity from the microspheres to that of a bulk sample of the same composition correlated to the mass of the microsphere contained within the cell. Irregular shaped silica particles were also analyzed and a calibration curve was generated from these data.

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Lateral resolution of the electron microbeam analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100109859 ◽

1978 ◽

Vol 36 (1) ◽

pp. 542-543

Author(s):

H.J. Dudek

Keyword(s):

Quantitative Analysis ◽

Depth Resolution ◽

Lateral Resolution ◽

Cylindrical Particle ◽

Correction Procedure ◽

X Ray ◽

Element Concentrations ◽

Microbeam Analysis ◽

The Matrix

The chemical inhomogenities in modern materials such as fibers, phases and inclusions, often have diameters in the region of one micrometer. Using electron microbeam analysis for the determination of the element concentrations one has to know the smallest possible diameter of such regions for a given accuracy of the quantitative analysis.In th is paper the correction procedure for the quantitative electron microbeam analysis is extended to a spacial problem to determine the smallest possible measurements of a cylindrical particle P of high D (depth resolution) and diameter L (lateral resolution) embeded in a matrix M and which has to be analysed quantitative with the accuracy q. The mathematical accounts lead to the following form of the characteristic x-ray intens ity of the element i of a particle P embeded in the matrix M in relation to the intensity of a standard S

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An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010008794x ◽

1991 ◽

Vol 49 ◽

pp. 726-727

Author(s):

John A. Hunt

Keyword(s):

Quantitative Analysis ◽

Large Data ◽

Difference Spectrum ◽

Large Data Sets ◽

Foil Thickness ◽

Data Sets ◽

Analysis Techniques ◽

Spectrum Imaging ◽

Normal Spectrum ◽

Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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