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The speed of laboratory computers doubles every year or two. As a consequence, complex and time-consuming data analysis methods that were prohibitively slow a few years ago can now be routinely employed. Examples of such methods within this volume include wavelets, transfer functions, inverse convolutions, robust fitting, moment analysis, maximum-entropy, and singular value decomposition. There are also many new and exciting approaches for modeling and prediction of biologically relevant molecules such as proteins, lipid bilayers, and ion channels.There is also an interesting trend in the educational background of new biomedical researchers over the last few years. For example, three of the authors in this volume are Ph.D. mathematicians who have faculty appointments in the School of Medicine at the University of Virginia. The combination of faster computers and more quantitatively oriented biomedical researchers has yielded new and more precise methods for the analysis of biomedical data. These better analyses have enhanced the conclusions that can be drawn from biomedical data and they have changed the way that the experiments are designed and performed. This is our fifth ‘‘Numerical Computer Methods’’ volume for Methods in Enzymology. The aim of volumes 210, 240, 321, 383, and the present volume is to inform biomedical researchers about some of these recent applications of modern data analysis and simulation methods as applied to biomedical research.

Ed. Michael L. Johnson and Ludwig Brand

numerical-computer-methods-part-e.pdf

Table of Contents

• Editors-In-Chief (Page ii)
  
• Contributors to Volume 384 (pp.ix-x )
  
• Preface (Page xi)
  
• Methods In Enzymology (pp.xiii-xxxiv )
  

1. A Practical Approach to Interpretation of Singular Value Decomposition Results (pp1-8)
   R. J. DeSa and I. B. C. Matheson
2. Large Reduction in Singular Value Calculation Time Using Savitzsky–Golay Data Precompression (pp9-17)
   I. B. C. Matheson
3. Efficient Integration of Kinetic Differential Equation Sets Using Matrix Exponentiation (pp18-39)
   I. B. C. Matheson, L. J. Parkhurst and R. J. DeSa
4. Deconvolution Analysis as a Hormone Pulse-Detection Algorithm (pp40-54)
   Michael L. Johnson, Amelia Virostko, Johannes D. Veldhuis and William S. Evans
5. Modeling of Oscillations in Endocrine Networks with Feedback (pp54-81)
   Leon S. Farhy
6. Measuring the Coupling of Hormone Concentration Time Series Using Polynomial Transfer Functions (pp82-94)
   Christopher R. Fox, Leon S. Farhy, William S. Evans and Michael L. Johnson
7. Numerical Estimation of HbA1c from Routine Self-Monitoring Data in People with Type 1 and Type 2 Diabetes Mellitus (pp94-106)
   Boris P. Kovatchev and Daniel J. Cox
8. Wavelet Modeling and Processing of Nasal Airflow Traces (pp106-130)
   Mario Peruggia, Junfeng Sun, Michael Mullins and Paul Suratt
9. Quantifying Asynchronous Breathing (pp130-138 )
   Michael L. Johnson and Paul Suratt
10. Mixed-Model Regression Analysis and Dealing with Interindividual Differences (pp139-171)
    Hans P. A. Van Dongen, Erik Olofsen, David F. Dinges and Greg Maislin
11. Sample Entropy (pp172-184)
    Joshua S. Richman, Douglas E. Lake and J. Randall Moorman
12. Calculating Sedimentation Coefficient Distributions by Direct Modeling of Sedimentation Velocity Concentration Profiles (pp185-212)
    Julie Dam and Peter Schuck
13. Analysis of Heterogeneous Interactions (pp212-232)
    James L. Cole
14. Estimation of Weights for Various Methods of the Fitting of Equilibrium Data from the Analytical Ultracentrifuge (pp232-242)
    Marc S. Lewis and Michael M. Reily
15. Applications of NMR Spin Relaxation Methods for Measuring Biological Motions (pp243-264)
    Guruvasuthevan R. Thuduppathy and R. Blake Hill

• Author Index (pp265-273 )
  
• Subject Index (pp275-278)

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