Numerical Computer Methods: Volume 210: Numerical Computer Methods

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Overview

The aim of this volume is to brief researchers of the importance of data analysis in enzymology, of the modern methods that have developed concomitantly with computer hardware, and of the need to validate their computer programs with real and synthetic data to ascertain that the results produced are what they expected.

Audience: Biochemists, biophysicists, physical chemists, and molecular and cell biologists.

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Editorial Reviews

From the Publisher
Praise for the Series
"The Methods in Enzymology series represents the gold-standard."
--NEUROSCIENCE
"Incomparably useful."
--ANALYTICAL BIOCHEMISTRY
"It is a true 'methods' series, including almost every detail from basic theory to sources of equipment and reagents, with timely documentation provided on each page."
--BIO/TECHNOLOGY
"The series has been following the growing, changing and creation of new areas of science. It should be on the shelves of all libraries in the world as a whole collection."
--CHEMISTRY IN INDUSTRY
"The appearance of another volume in that excellent series, Methods in Enzymology, is always a cause for appreciation for those who wish to successfully carry out a particular technique or prepare an enzyme or metabolic intermediate without the tiresome prospect of searching through unfamiliar literature and perhaps selecting an unproven method which is not easily reproduced."
--AMERICAN SOCIETY OF MICROBIOLOGY NEWS
"If we had some way to find the work most often consulted in the laboratory, it could well be the multi-volume series Methods in Enzymology...a great work."
--ENZYMOLOGIA
"A series that has established itself as a definitive reference for biochemists."
--JOURNAL OF CHROMATOGRAPHY
Booknews
Biomedical researchers must understand the capabilities and limitations of computer data analysis--how today's high speed computers eliminate the need to analyze transformed data as was done in the past, and how computer analysis rests on assumptions about the nature of the data that can be erroneous. Basic methods are described and illustrated; and new methods under development, such as neural networks and fractals, are introduced. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780121821111
  • Publisher: Elsevier Science
  • Publication date: 5/12/1992
  • Series: Methods in Enzymology Series
  • Pages: 718
  • Product dimensions: 9.21 (w) x 6.14 (h) x 1.63 (d)

Table of Contents

Parameter Estimation by Least-Squares Methods. Global Analysis of Biochemical and Biophysical Data. Pad*aae*b1Laplace Algorithm for Sums of Exponentials: Selecting Appropriate Exponential Model and Initial Estimates for Exponential Fitting. Use of Weighting Functions in Data Fitting. Analysis of Residuals: Criteria for Determining Goodness-of-Fit. Analysis of Ligand-Binding Data with Experimental Uncertainties in Independent Variables. Monte Carlo Method for Determining Complete Confidence Probability
Distributions of Estimated Model Parameters. Singular Value Decomposition: Application to Analysis of Experimental Data. Fourier Resolution Enhancement of Infrared Spectral Data. Maximum Likelihood Analysis of Fluorescence Data. Method of Moments and Treatment of Nonrandom Error. Laplace Deconvolution of Fluroescence Decay Surfaces. Interpolation Methods. Compartmental Analysis of Fluorescence Decay Surfaces of Excited-State Processes. Analysis of Discrete, Time-Sampled Data Using Fourier Series Method. Alternatives to Consider in
Fluorescence Decay Analysis. Practical Aspects of Kinetic Analysis. Compartmental Analysis of Enzyme-Catalyzed Reactions. Analysis of Site-Specific Interaction Parameters in Protein*b1DNA Complexes. Analysis of Circular Dichroism Spectra. Fluorescence Quenching Studies: Analysis of Nonlinear Stern*b1Volmer Data. Simultaneous Analysis for Testing of Models and Parameter Estimation. Numerical Analysis of Binding Data: Advantages, Practical Aspects, and Implications. Deconvolution Analysis for Pulsed-Laser Photoacoustics. Parameter Estimation in Binary Mixtures of Phospholipids.
Deconvolution Analysis of Hormone Data. Dynamic Programming Algorithms for Biological Sequence Comparison. Programs for Symbolic Mathematics in Biochemistry. Artificial Neural Networks. Fractal Applications in Biology: Scaling Time in Biochemical Networks. Author Index. Subject Index.
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