Brand Computer Methods Part B

1. Auflage 2009
ISBN: 978-0-08-096280-1
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 712 Seiten

The combination of faster, more advanced computers and more quantitatively oriented biomedical researchers has recently 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 experiments are designed and performed. This volume, along with previous and forthcoming Computer Methods volumes for the Methods in Enzymology serial, aims to inform biomedical researchers about recent applications of modern data analysis and simulation methods as applied to biomedical research.
* Presents step-by-step computer methods and discusses the techniques in detail to enable their implementation in solving a wide range of problems * Informs biomedical researchers of the modern data analysis methods that have developed alongside computer hardware *Presents methods at the 'nuts and bolts' level to identify and resolve a problem and analyze what the results mean

Autoren/Hrsg.

Brand, Ludwig

Inhaltsverzeichnis

1;Front Cover;1
2;Methods in Enzymology;4
3;Copyright;5
4;Contents;6
5;Contributors;14
6;Preface;20
8;Chapter 1: Correlation Analysis: A Tool for Comparing Relaxation-Type Models to Experimental Data;50
8.1;1. Introduction;51
8.2;2. Scatter Plots and Correlation Analysis;52
8.3;3. Example 1: Relaxation Oscillations;53
8.4;4. Example 2: Square Wave Bursting;62
8.5;5. Example 3: Elliptic Bursting;64
8.6;6. Example 4: Using Correlation Analysis on Experimental Data;67
8.7;7. Summary;68
8.8;Appendix: Algorithm for the Determination of Phase Durations During Bursting;68
8.9;Acknowledgment;69
8.10;References;69
9;Chapter 2: Trait Variability of Cancer Cells Quantified by High-Content Automated Microscopy of Single Cells;72
9.1;1. Introduction;73
9.2;2. Background;74
9.3;3. Experimental and Computational Workflow;75
9.4;4. Application to Traits Relevant to Cancer Progression;83
9.5;5. Conclusions;103
9.6;Acknowledgments;103
9.7;References;103
10;Chapter 3: Matrix Factorization for Recovery of Biological Processes from Microarray Data;108
10.1;1. Introduction;108
10.2;2. Overview of Methods;112
10.3;3. Application to the Rosetta Compendium;117
10.4;4. Results of Analyses;119
10.5;5. Discussion;123
10.6;References;124
11;Chapter 4: Modeling and Simulation of the Immune System as a Self-Regulating Network;128
11.1;1. Introduction;129
11.2;2. Mathematical Modeling of the Immune Network;133
11.3;3. Two Examples of Models to Understand T Cell Regulation;141
11.4;4. How to Implement Mathematical Models in Computer Simulations;149
11.5;5. Concluding Remarks;154
11.6;Acknowledgments;155
11.7;References;156
12;Chapter 5: Entropy Demystified: The "Thermo"-dynamics of Stochastically Fluctuating Systems;160
12.1;1. Introduction;161
12.2;2. Energy;162
12.3;3. Entropy and "Thermo"-dynamics of Markov Processes;166
12.4;4. A Three-State Two-Cycle Motor Protein;171
12.5;5. Phosphorylation-Dephosphorylation Cycle Kinetics;174
12.6;6. Summary and Challenges;180
12.7;References;181
13;Chapter 6: Effect of Kinetics on Sedimentation Velocity Profiles and the Role of Intermediates;184
13.1;1. Introduction;185
13.2;2. Methods;187
13.3;3. ABCD Systems;190
13.4;4. Monomer-Tetramer Model;200
13.5;5. Summary;207
13.6;Acknowledgments;208
13.7;References;208
14;Chapter 7: Algebraic Models of Biochemical Networks;212
14.1;1. Introduction;213
14.2;2. Computational Systems Biology;214
14.3;3. Network Inference;225
14.4;4. Reverse-Engineering of Discrete Models: An Example;230
14.5;5. Discussion;239
14.6;References;242
15;Chapter 8: High-Throughput Computing in the Sciences;246
15.1;1. What is an HTC Application?;248
15.2;2. HTC Technologies;249
15.3;3. High-Throughput Computing Examples;253
15.4;4. Advanced Topics;267
15.5;5. Summary;275
15.6;References;275
16;Chapter 9: Large Scale Transcriptome Data Integration Across Multiple Tissues to Decipher Stem Cell Signatures;278
16.1;1. Introduction;279
16.2;2. Systems and Data Sources;280
16.3;3. Data Integration;285
16.4;4. Artificial Neural Network Training and Validation;287
16.5;5. Future Development and Enhancement Plans;292
16.6;Acknowledgments;293
16.7;References;293
17;Chapter 10: DynaFit-A Software Package for Enzymology;296
17.1;1. Introduction;297
17.2;2.Equilibrium Binding Studies;299
17.3;3. Initial Rates of Enzyme Reactions;304
17.4;4. Time Course of Enzyme Reactions;309
17.5;5. General Methods and Algorithms;311
17.6;6. Concluding Remarks;324
17.7;Acknowledgments;325
17.8;References;325
18;Chapter 11: Discrete Dynamic Modeling of Cellular Signaling Networks;330
18.1;1. Introduction;331
18.2;2. Cellular Signaling Networks;333
18.3;3. Boolean Dynamic Modeling;335
18.4;4. Variants of Boolean Network Models;346
18.5;5. Application Examples;350
18.6;6. Conclusion and Discussion;352
18.7;Acknowledgments;352
18.8;References;352
19;Chapter 12: The Basic Concepts of Molecular Modeling;356
19.1;1. Introduction;357
19.2;2. Homology Modeling;357
19.3;3. Molecular Dynamics;366
19.4;4. Molecular Docking;373
19.5;References;379
20;Chapter 13: Deterministic and Stochastic Models of Genetic Regulatory Networks;384
20.1;1. Introduction;385
20.2;2. Boolean Networks;386
20.3;3. Differential Equation Models;392
20.4;4. Probabilistic Boolean Networks;396
20.5;5. Stochastic Differential Equation Models;400
20.6;References;402
21;Chapter 14: Bayesian Probability Approach to ADHD Appraisal;406
21.1;1. Introduction;407
21.2;2. Bayesian Probability Algorithm;411
21.3;3. The Value of Bayesian Probability Approach as a Meta-Analysis Tool;418
21.4;4. Discussion and Future Directions;422
21.5;Acknowledgment;426
21.6;References;427
22;Chapter 15: Simple Stochastic Simulation;430
22.1;1. Introduction;431
22.2;2. Understanding Reaction Dynamics;434
22.3;3. Graphical Notation;435
22.4;4. Reactions;438
22.5;5. Reaction Kinetics;438
22.6;6. Transition Firing Rules;442
22.7;7. Summary;455
22.8;8. Notes;456
22.9;References;458
23;Chapter 16: Monte Carlo Simulation in Establishing Analytical Quality Requirements for Clinical Laboratory Tests: Meeting Clinical Needs;460
23.1;1. Introduction;461
23.2;2. Modeling Approach;463
23.3;3. Methods for Simulation Study;465
23.4;4. Results;466
23.5;5. Discussion;478
23.6;References;480
24;Chapter 17: Nonlinear Dynamical Analysis and Optimization for Biological/Biomedical Systems;484
24.1;1. Introduction;485
24.2;2. Hypothalamic-Pituitary-Adrenal Axis System;486
24.3;3. Development of a Clinically Relevant Performance-Assessment Tools;490
24.4;4. Dynamic Programming;501
24.5;5. Computation of Optimal Treatments for HPA Axis System;504
24.6;6. Conclusions;507
24.7;Acknowledgments;507
24.8;References;507
25;Chapter 18: Modeling of Growth Factor-Receptor Systems: From Molecular-Level Protein Interaction Networks to Whole-Body Compartment Models;510
25.1;1. Background;511
25.2;2. Molecular-Level Kinetics Models: Simulation of In Vitro Experiments;515
25.3;3. Mesoscale Single-Tissue 3D Models: Simulation of In Vivo Tissue Regions;523
25.4;4. Single-Tissue Compartmental Models: Simulation of In Vivo Tissue;531
25.5;5. Multitissue Compartmental Models: Simulation of Whole Body;534
25.6;6. Conclusions;542
25.7;Acknowledgments;543
25.8;References;543
26;Chapter 19: The Least-Squares Analysis of Data from Binding and Enzyme Kinetics Studies: Weights, Bias, and Confidence Intervals in Usual and Unusual Situations;548
26.1;1. Introduction;549
26.2;2. Least Squares Review;552
26.3;3. Statistics of Reciprocals;555
26.4;4. Weights When y is a True Dependent Variable;560
26.5;5. Unusual Weighting: When x is the Dependent Variable;570
26.6;6. Assessing Data Uncertainty: Variance Function Estimation;573
26.7;7. Conclusion;575
26.8;References;576
27;Chapter 20: Nonparametric Entropy Estimation Using Kernel Densities;580
27.1;1. Introduction;581
27.2;2. Motivating Application: Classifying Cardiac Rhythms;582
27.3;3. Renyi Entropy and the Friedman-Tukey Index;584
27.4;4. Kernel Density Estimation;585
27.5;5. Mean-Integrated Square Error;587
27.6;6. Estimating the FT Index;589
27.7;7. Connection Between Template Matches and Kernel Densities;593
27.8;8. Summary and Future Work;594
27.9;Acknowledgments;594
27.10;References;595
28;Chapter 21: Pancreatic Network Control of Glucagon Secretion and Counterregulation;596
28.1;1. Introduction;597
28.2;2. Mechanisms of Glucagon Counterregulation (GCR) Dysregulation in Diabetes;599
28.3;3. Interdisciplinary Approach to Investigating the Defects in the GCR;600
28.4;4. Initial Qualitative Analysis of the GCR Control Axis;602
28.5;5. Mathematical Models of the GCR Control Mechanisms in STZ-Treated Rats;605
28.6;6. Approximation of the Normal Endocrine Pancreas by a Minimal Control Network (MCN) and Analysis of the GCR Abnormalities in the Insulin Deficient Pancreas;609
28.7;7. Advantages and Limitations of the Interdisciplinary Approach;620
28.8;8. Conclusions;624
28.9;Acknowledgment;624
28.10;References;624
29;Chapter 22: Enzyme Kinetics and Computational Modeling for Systems Biology;632
29.1;1. Introduction;633
29.2;2. Computational Modeling and Enzyme Kinetics;635
29.3;3. Yeast Triosephosphate Isomerase (EC 5.3.1.1);637
29.4;4. Initial Rate Analysis;639
29.5;5. Progress Curve Analysis;643
29.6;6. Concluding Remarks;647
29.7;Acknowledgments;647
29.8;References;647
30;Chapter 23: Fitting Enzyme Kinetic Data with KinTek Global Kinetic Explorer;650
30.1;1. Background;651
30.2;2. Challenges of Fitting by Simulation;652
30.3;3. Methods;654
30.4;4. Progress Curve Kinetics;659
30.5;5 Fitting Full Progress Curves;662
30.6;6. Slow Onset Inhibition Kinetics;669
30.7;7. Summary;673
30.8;Acknowledgments;674
30.9;References;674
31;Author Index;676
32;Subject Index;686
33;Color Plates;696