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Numerical Computer Methods, Part D
The aim of this volume is to brief researchers of the importance of data analysis in enzymology and of the modern methods that have developed concomitantly with computer hardware.
Ludwig Brand (Volume editor), Michael L. Johnson (Volume editor)
9780121827885, Elsevier Science
Hardback, published 25 May 2004
489 pages, Approx. 100 illustrations
22.9 x 15.1 x 3 cm, 0.86 kg
The aim of Numerical Computer Methods, Part D is to brief researchers of the importance of data analysis in enzymology, and of the modern methods that have developed concomitantly with computer hardware. It is also to validate researchers' computer programs with real and synthetic data to ascertain that the results produced are what they expected.
Editors-In-Chief Contributors to Volume 383 Preface Methods In Enzymology Prediction of Protein Structure Overview and Perspective Classifications of Protein Structure Concepts and Evaluations of Protein Predictions Process of Extracting Information about Protein Structure from Sequence Future Directions Modeling and Studying Proteins with Molecular Dynamics Introduction Sampling of CHARMM Capabilities Program Operation Basics Example Analysis Ab Initio Protein Folding Using LINUS Introduction Anatomy of LINUS Simulation Implementation Simulation Examples Conclusion Appendix I Protein Structure Prediction Using Rosetta Introduction Rosetta Strategy De Novo Structure Prediction with Rosetta Structure Prediction by Fragment Assembly Enhancements of Fragment Insertion Strategy Effectiveness of Conformation Modification Operators for Energy Function Optimization Conclusions Supplemental Materials Appendix I Appendix II Poisson–Boltzmann Methods for Biomolecular Electrostatics Introduction Numerical Solution of Poisson–Boltzmann Equation Applications to Biomedical Sciences Conclusions Atomic Simulations of Protein Folding, Using the Replica Exchange Algorithm Introduction Replica Exchange Molecular Dynamics Practical Issues Appendix DNA Microarray Time Series Analysis: Automated Statistical Assessment of Circadian Rhythms in Gene Expression Patterning Introduction Statistical Assessment of Daily Rhythms in Microarray Data Simulation Procedure Comparisons of Analytical Results Summary Molecular Simulations of Diffusion and Association in Multimacromolecular Systems Introduction Theoretical Aspects Practical Aspects Some Example Applications Conclusion Modeling Lipid–Sterol Bilayers: Applications to Structural Evolution, Lateral Diffusion, and Rafts Introduction Theoretical Models Simulation Methods Results Summary and Perspectives Idealization and Simulation of Single Ion Channel Data Introduction Noise Filtering Missed Events Subconductance Levels Models Analysis Methods Simulation Idealization Interpretation Performance Statistical Error in Isothermal Titration Calorimetry Introduction Variance–Covariance Matrix in Least Squares Monte Carlo Computational Methods Van't Hoff Analysis of K°(T): Least-Squares Demonstration Isothermal Titration Calorimetry Calorimetric Versus Van't Hoff Î?H° from ITC Conclusion Analysis of Circular Dichroism Data Introduction Summary of Methods to Obtain Secondary Structure of Proteins from Circular Dichroism Data Determination of Thermodynamics of Protein Folding/Unfolding from CD Data Determination of Binding Constants from CD Data Conclusion Appendix I Computation and Analysis of Protein Circular Dichroism Spectra Introduction Basic Definitions Computation of Protein CD Analysis of Protein CD Model Comparison Methods Introduction Statistical Foundations of Model Comparison Model Comparison Methods Model Comparison at Work Conclusion Practical Robust Fit of Enzyme Inhibition Data Introduction Theory Numerical Example Implementation Notes Conclusions Measuring Period of Human Biological Clock: Infill Asymptotic Analysis of Harmonic Regression Parameter Estimates Introduction Theory Proof Proof Proof Data Analysis Discussion Appendix I: Outline of Proof of Proposition 1 Appendix II: Proof of Lemma 1 Appendix III: Proof of Lemma 2 Bayesian Methods to Improve Sample Size Approximations Introduction Bayesian Inference Deriving Sample Size Formulas Choosing Prior Distributions Gain from Using Prior Information Examples Conclusion Distribution Functions from Moments and the Maximum-Entropy Method Introduction Ligand Binding: Moments Maximum-Entropy Distributions Ligand Binding: Distribution Functions Enthalpy Distributions Self-Association Distributions Author Index Subject Index
Subject Areas: Enzymology [PSBZ], Biochemistry [PSB], Biophysics [PHVN], Medical bioinformatics [MBF]