Computer Methods, Part C

1. Auflage 2010
ISBN: 978-0-12-381271-1
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band 487, 696 Seiten

Reihe: Methods in Enzymology

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 the 2 previous 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.

Inhaltsverzeichnis

1;Front Cover;1
2;Methods in Enzymology: Research on Nitrification and Related Processes, Part C;4
3;Copyright;5
4;Contents;6
5;Contributors;14
6;Preface;22
7;Methods in Enzymology;24
8;Chapter 1: Predicting Fluorescence Lifetimes and Spectra of Biopolymers;54
8.1;1. Introduction;55
8.2;2. Qualitative Concepts: Developing Intuition;60
8.3;3. Methods;67
8.4;4. Nonexponential Fluorescence Decay;78
8.5;5. Final Remarks;81
8.6;Appendix: Ab Initio Computation of Electron Transfer Coupling Matrix Elements;82
8.7;Acknowledgments;87
8.8;References;87
9;Chapter 2: Modeling of Regulatory Networks...;92
9.1;1. Introduction;93
9.2;2. Developmental History of the Drosophila Circadian Clock;95
9.3;3. Comparative Analysis of Three Network Regulatory Models;104
9.4;4. The CWO Anomaly and a New Network Regulatory Rule;116
9.5;5. Concluding Remarks;120
9.6;References;122
10;Chapter 3: Strategies for Articulated Multibody-Based Adaptive Coarse Grain Simulation of RNA;126
10.1;1. Introduction;127
10.2;2. Need for the Development of Adaptive Coarse-Graining Machinery;131
10.3;3. Metrics to Guide Transitions in Adaptive Modeling;136
10.4;4. Adaptive Modeling Framework in DCA Scheme;142
10.5;5. Conclusions;149
10.6;Acknowledgments;149
10.7;References;149
11;Chapter 4: Modeling Loop Entropy;152
11.1;1. Introduction;153
11.2;2. Computing Bounds on the Entropy of the Unfolded Ensemble;166
11.3;3. Approximating Entropy of the Loops in the Folded Ensemble;172
11.4;4. Examples;173
11.5;5. Conclusions;180
11.6;Acknowledgments;181
11.7;References;181
12;Chapter 5: Inferring Functional Relationships and Causal Network Structure from Gene Expression Profiles;186
12.1;1. Introduction;187
12.2;2. Methods;189
12.3;3. Results;194
12.4;4. Conclusions;197
12.5;References;198
13;Chapter 6: Numerical Solution of the Chemical Master Equation...;200
13.1;1. Introduction;201
13.2;2. The Chemical Master Equation;203
13.3;3. Irreducible Chemical Reaction Systems;205
13.4;4. Stability of the Chemical Master Equation Stationary Probability Distribution;206
13.5;5. Two Different Algorithms to Calculate Stationary Probability Distributions for the Chemical Master Equation...;210
13.6;6. Gene Expression with Negative Feedback Regulation;213
13.7;7. Concluding Remarks;220
13.8;References;220
14;Chapter 7: How Molecular Should Your Molecular Model Be?...;224
14.1;1. Introduction;225
14.2;2. Michaelis-Menten Kinetics Revisited;228
14.3;3. Use of the Hill Kinetics for Transcription Rate;238
14.4;4. Repressilator;243
14.5;5. Toggle Switch;251
14.6;6. Discussion;257
14.7;7. Conclusion;263
14.8;Acknowledgments;264
14.9;Appendix: The Gillespie Algorithm;264
14.10;References;264
15;Chapter 8: Computational Modeling of Biological Pathways by Executable Biology;270
15.1;1. Introduction;271
15.2;2. Executable Modeling Languages for Biology;273
15.3;3. Intuitive Representation of Formal Models;279
15.4;4. Case Studies;286
15.5;5. Conclusions and Perspectives;301
15.6;Acknowledgments;301
15.7;References;301
16;Chapter 9: Computing Molecular Fluctuations in Biochemical Reaction Systems Based on a Mechanistic, Statistical Theory...;306
16.1;1. Introduction;307
16.2;2. Theoretical Developments;309
16.3;3. Elementary Chemical Reactions;313
16.4;4. An Example of Chemical Reaction;315
16.5;5. Activation of Transcriptional Factors;319
16.6;6. Binding and Unbinding TF to E-boxes;322
16.7;7. Binding and Unbinding of Activated TF to E-Boxes;326
16.8;8. Conclusions;330
16.9;Acknowledgments;330
16.10;References;330
17;Chapter 10: Probing the Input-Output Behavior of Biochemical and Genetic Systems...;332
17.1;1. Introduction;333
17.2;2. System Identification Applied to a G-Protein Pathway;335
17.3;3. System Identification;344
17.4;4. Conclusion;364
17.5;Appendix;366
17.6;References;369
18;Chapter 11: Biochemical Pathway Modeling Tools for Drug Target Detection in Cancer and Other Complex Diseases;372
18.1;1. Introduction and Overview;373
18.2;2. Biomedical Knowledge and Data Retrieval: Constructing a Conceptual Map of a Biochemical Network;378
18.3;3. Mathematical Modeling of Biochemical Networks: Translating Knowledge into Mathematical Equations;380
18.4;4. Model Calibration: Matching the Mathematical Model to Quantitative Experimental Data;390
18.5;5. Predictive Model Simulations as a Tool for Drug Discovery;394
18.6;6. Model Sensitivity Analysis as a Tool for Detecting Critical Processes in Biochemical Networks;398
18.7;7. Drug Target Detection Through Model Optimization;403
18.8;8. One Step Further: Combining Mathematical Modeling with Drug Screening via Protein Docking-Based Techniques;409
18.9;9. Final Remarks;412
18.10;Appendix;414
18.11;Acknowledgments;420
18.12;References;420
19;Chapter 12: Deterministic and Stochastic Simulation and Analysis of Biochemical Reaction Networks...;424
19.1;1. Introduction;425
19.2;2. Mathematical Modeling of Biochemical Reaction Networks and Law of Mass Action;425
19.3;3. Stochastic Simulations;434
19.4;4. An Example: Lactose Operon in E. coli;439
19.5;5. Conclusions and Discussion;446
19.6;Acknowledgments;448
19.7;References;448
20;Chapter 13: Multivariate Neighborhood Sample Entropy: A Method for Data Reduction and Prediction of Complex Data;450
20.1;1. Introduction;451
20.2;2. Current Methods and Limitations;451
20.3;3. k-Nearest Neighbors;452
20.4;4. Sample Entropy;453
20.5;5. Multivariate Neighborhood Sample Entropy: MN-SampEn;454
20.6;6. Relationship Between kNN and MN-SampEn;455
20.7;7. Relationship Between SampEn and MN-SampEn;455
20.8;8. Applying MN-SampEn to Proteomics Data;456
20.9;9. Algorithmic Implementation and Optimizing Tolerances;456
20.10;10. Results;458
20.11;11. Discussion;460
20.12;12. Limitations and Future Directions;461
20.13;References;461
21;Chapter 14: Scaling Differences of Heartbeat Excursions Between Wake and Sleep Periods;462
21.1;1. Introduction;463
21.2;2. Methods;464
21.3;3. Data Analysis;467
21.4;4. Conclusions;480
21.5;Acknowledgments;481
21.6;References;481
22;Chapter 15: Changepoint Analysis for Single-Molecule Polarized Total Internal Reflection Fluorescence Microscopy Experiments.;484
22.1;1. Overview;486
22.2;2. Multiple Channels;492
22.3;3. Detailed Analysis;495
22.4;4. Simulation Results;503
22.5;5. Discussion;510
22.6;6. Conclusion;514
22.7;Acknowledgments;515
22.8;References;515
23;Chapter 16: Inferring Mechanisms from Dose-Response Curves;518
23.1;1. Introduction;519
23.2;2. General Theory;520
23.3;3. Application of Model to Data;525
23.4;4. Discussion;532
23.5;Appendix Model Fit to Data and Parameter Estimates for Ubc9 and Glucocorticoid Receptor;534
23.6;Acknowledgments;535
23.7;References;535
24;Chapter 17: Spatial Aspects in Biological System Simulations;538
24.1;1. Introduction;539
24.2;2. Methods and Frameworks;542
24.3;3. Summary and Future Prospects;561
24.4;Acknowledgments;562
24.5;References;562
25;Chapter 18: Computational Approaches to Modeling Viral Structure and Assembly;566
25.1;1. Introduction;567
25.2;2. Double-Stranded DNA (dsDNA) Bacteriophage;567
25.3;3. Single-Stranded RNA Viruses;579
25.4;Acknowledgments;593
25.5;References;593
26;Chapter 19: Rosetta3:...;598
26.1;1. Introduction;599
26.2;2. Requirements;601
26.3;3. Design Decisions;603
26.4;4. Architecture;607
26.5;5. Conclusion;624
26.6;Acknowledgments;625
26.7;References;625
27;Chapter 20: Computational Design of Intermolecular Stability and Specificity in Protein Self-Assembly;628
27.1;1. Introduction;629
27.2;2. Similarities and Differences Between Unimolecular Folding and Self-assembly;630
27.3;3. Computational Approaches to Optimizing Stability and Specificity;632
27.4;4. Collagen Self-assembly;637
27.5;5. Considerations in Computational Design of Collagen Heteromers;640
27.6;6. Conclusions;644
27.7;References;644
28;Chapter 21: Differential Analysis of 2D Gel Images;648
28.1;1. Introduction;649
28.2;2. Differential Analysis of 2D Gel Images;650
28.3;3. Analyzing 2D Gel Images Using RegStatGel;652
28.4;4. Illustration of an Exploratory Analysis Using RegStatGel;659
28.5;5. Concluding Remarks;661
28.6;References;662
29;Author Index;664
30;Subject Index;680
31;Colour plate;692