Computer Methods Part B (eBook)

Ludwig Brand, Michael L. Johnson (Herausgeber)

eBook Download: EPUB

2009 | 1. Auflage
712 Seiten
Elsevier Science (Verlag)
9780080962801 (ISBN)

Infos

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

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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 "e;nuts and bolts"e; level to identify and resolve a problem and analyze what the results mean

Inhalt

Front Cover 1
Methods in Enzymology 4
Copyright 5
Contents 6
Contributors 14
Preface 20
Chapter 1: Correlation Analysis: A Tool for Comparing Relaxation-Type Models to Experimental Data 50
1. Introduction 51
2. Scatter Plots and Correlation Analysis 52
3. Example 1: Relaxation Oscillations 53
4. Example 2: Square Wave Bursting 62
5. Example 3: Elliptic Bursting 64
6. Example 4: Using Correlation Analysis on Experimental Data 67
7. Summary 68
Appendix: Algorithm for the Determination of Phase Durations During Bursting 68
Acknowledgment 69
References 69
Chapter 2: Trait Variability of Cancer Cells Quantified by High-Content Automated Microscopy of Single Cells 72
1. Introduction 73
2. Background 74
3. Experimental and Computational Workflow 75
4. Application to Traits Relevant to Cancer Progression 83
5. Conclusions 103
Acknowledgments 103
References 103
Chapter 3: Matrix Factorization for Recovery of Biological Processes from Microarray Data 108
1. Introduction 108
2. Overview of Methods 112
3. Application to the Rosetta Compendium 117
4. Results of Analyses 119
5. Discussion 123
References 124
Chapter 4: Modeling and Simulation of the Immune System as a Self-Regulating Network 128
1. Introduction 129
2. Mathematical Modeling of the Immune Network 133
3. Two Examples of Models to Understand T Cell Regulation 141
4. How to Implement Mathematical Models in Computer Simulations 149
5. Concluding Remarks 154
Acknowledgments 155
References 156
Chapter 5: Entropy Demystified: The "Thermo"-dynamics of Stochastically Fluctuating Systems 160
1. Introduction 161
2. Energy 162
3. Entropy and "Thermo"-dynamics of Markov Processes 166
4. A Three-State Two-Cycle Motor Protein 171
5. Phosphorylation-Dephosphorylation Cycle Kinetics 174
6. Summary and Challenges 180
References 181
Chapter 6: Effect of Kinetics on Sedimentation Velocity Profiles and the Role of Intermediates 184
1. Introduction 185
2. Methods 187
3. ABCD Systems 190
4. Monomer-Tetramer Model 200
5. Summary 207
Acknowledgments 208
References 208
Chapter 7: Algebraic Models of Biochemical Networks 212
1. Introduction 213
2. Computational Systems Biology 214
3. Network Inference 225
4. Reverse-Engineering of Discrete Models: An Example 230
5. Discussion 239
References 242
Chapter 8: High-Throughput Computing in the Sciences 246
1. What is an HTC Application? 248
2. HTC Technologies 249
3. High-Throughput Computing Examples 253
4. Advanced Topics 267
5. Summary 275
References 275
Chapter 9: Large Scale Transcriptome Data Integration Across Multiple Tissues to Decipher Stem Cell Signatures 278
1. Introduction 279
2. Systems and Data Sources 280
3. Data Integration 285
4. Artificial Neural Network Training and Validation 287
5. Future Development and Enhancement Plans 292
Acknowledgments 293
References 293
Chapter 10: DynaFit-A Software Package for Enzymology 296
1. Introduction 297
2.Equilibrium Binding Studies 299
3. Initial Rates of Enzyme Reactions 304
4. Time Course of Enzyme Reactions 309
5. General Methods and Algorithms 311
6. Concluding Remarks 324
Acknowledgments 325
References 325
Chapter 11: Discrete Dynamic Modeling of Cellular Signaling Networks 330
1. Introduction 331
2. Cellular Signaling Networks 333
3. Boolean Dynamic Modeling 335
4. Variants of Boolean Network Models 346
5. Application Examples 350
6. Conclusion and Discussion 352
Acknowledgments 352
References 352
Chapter 12: The Basic Concepts of Molecular Modeling 356
1. Introduction 357
2. Homology Modeling 357
3. Molecular Dynamics 366
4. Molecular Docking 373
References 379
Chapter 13: Deterministic and Stochastic Models of Genetic Regulatory Networks 384
1. Introduction 385
2. Boolean Networks 386
3. Differential Equation Models 392
4. Probabilistic Boolean Networks 396
5. Stochastic Differential Equation Models 400
References 402
Chapter 14: Bayesian Probability Approach to ADHD Appraisal 406
1. Introduction 407
2. Bayesian Probability Algorithm 411
3. The Value of Bayesian Probability Approach as a Meta-Analysis Tool 418
4. Discussion and Future Directions 422
Acknowledgment 426
References 427
Chapter 15: Simple Stochastic Simulation 430
1. Introduction 431
2. Understanding Reaction Dynamics 434
3. Graphical Notation 435
4. Reactions 438
5. Reaction Kinetics 438
6. Transition Firing Rules 442
7. Summary 455
8. Notes 456
References 458
Chapter 16: Monte Carlo Simulation in Establishing Analytical Quality Requirements for Clinical Laboratory Tests: Meeting Clinical Needs 460
1. Introduction 461
2. Modeling Approach 463
3. Methods for Simulation Study 465
4. Results 466
5. Discussion 478
References 480
Chapter 17: Nonlinear Dynamical Analysis and Optimization for Biological/Biomedical Systems 484
1. Introduction 485
2. Hypothalamic-Pituitary-Adrenal Axis System 486
3. Development of a Clinically Relevant Performance-Assessment Tools 490
4. Dynamic Programming 501
5. Computation of Optimal Treatments for HPA Axis System 504
6. Conclusions 507
Acknowledgments 507
References 507
Chapter 18: Modeling of Growth Factor-Receptor Systems: From Molecular-Level Protein Interaction Networks to Whole-Body Compartment Models 510
1. Background 511
2. Molecular-Level Kinetics Models: Simulation of In Vitro Experiments 515
3. Mesoscale Single-Tissue 3D Models: Simulation of In Vivo Tissue Regions 523
4. Single-Tissue Compartmental Models: Simulation of In Vivo Tissue 531
5. Multitissue Compartmental Models: Simulation of Whole Body 534
6. Conclusions 542
Acknowledgments 543
References 543
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
1. Introduction 549
2. Least Squares Review 552
3. Statistics of Reciprocals 555
4. Weights When y is a True Dependent Variable 560
5. Unusual Weighting: When x is the Dependent Variable 570
6. Assessing Data Uncertainty: Variance Function Estimation 573
7. Conclusion 575
References 576
Chapter 20: Nonparametric Entropy Estimation Using Kernel Densities 580
1. Introduction 581
2. Motivating Application: Classifying Cardiac Rhythms 582
3. Renyi Entropy and the Friedman-Tukey Index 584
4. Kernel Density Estimation 585
5. Mean-Integrated Square Error 587
6. Estimating the FT Index 589
7. Connection Between Template Matches and Kernel Densities 593
8. Summary and Future Work 594
Acknowledgments 594
References 595
Chapter 21: Pancreatic Network Control of Glucagon Secretion and Counterregulation 596
1. Introduction 597
2. Mechanisms of Glucagon Counterregulation (GCR) Dysregulation in Diabetes 599
3. Interdisciplinary Approach to Investigating the Defects in the GCR 600
4. Initial Qualitative Analysis of the GCR Control Axis 602
5. Mathematical Models of the GCR Control Mechanisms in STZ-Treated Rats 605
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
7. Advantages and Limitations of the Interdisciplinary Approach 620
8. Conclusions 624
Acknowledgment 624
References 624
Chapter 22: Enzyme Kinetics and Computational Modeling for Systems Biology 632
1. Introduction 633
2. Computational Modeling and Enzyme Kinetics 635
3. Yeast Triosephosphate Isomerase (EC 5.3.1.1) 637
4. Initial Rate Analysis 639
5. Progress Curve Analysis 643
6. Concluding Remarks 647
Acknowledgments 647
References 647
Chapter 23: Fitting Enzyme Kinetic Data with KinTek Global Kinetic Explorer 650
1. Background 651
2. Challenges of Fitting by Simulation 652
3. Methods 654
4. Progress Curve Kinetics 659
5 Fitting Full Progress Curves 662
6. Slow Onset Inhibition Kinetics 669
7. Summary 673
Acknowledgments 674
References 674
Author Index 676
Subject Index 686
Color Plates 696

Produkt-Details

• Erscheint lt. Verlag: 5.11.2009
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Themenwelt: Medizin / Pharmazie
• Themenwelt: Naturwissenschaften ► Biologie
• Themenwelt: Technik
• ISBN-13: 9780080962801 / 9780080962801