Methods in Enzymology, Volume 465

Volume 467

By: Ludwig Brand, Michael L. Johnson

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Hardcover | 19 November 2009 | Edition Number 465

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712 Pages

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

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Non-Fiction Medicine Medicine in General Medical Equipment & Techniques Medical Research Science Biology, Life Sciences Molecular Biology Biochemistry Enzymology Public Health & Preventive Medicine Epidemiology & Medical Statistics

Contributors	p. xiii
Preface	p. xix
Volumes in Series	p. xxi
Correlation Analysis: A Tool for Comparing Relaxation-Type Models to Experimental Data	p. 1
Introduction	p. 2
Scatter Plots and Correlation Analysis	p. 3
Example 1: Relaxation Oscillations	p. 4
Example 2: Square Wave Bursting	p. 13
Example 3: Elliptic Bursting	p. 15
Example 4: Using Correlation Analysis on Experimental Data	p. 18
Summary	p. 19
Acknowledgments	p. 20
References	p. 20
Trait Variability of Cancer Cells Quantified by High-Content Automated Microscopy of Single Cells	p. 23
Introduction	p. 24
Background	p. 25
Experimental and Computational Workflow	p. 26
Application to Traits Relevant to Cancer Progression	p. 34
Conclusions	p. 54
Acknowledgments	p. 54
References	p. 54
Matrix Factorization for Recovery of Biological Processes from Microarray Data	p. 59
Introduction	p. 59
Overview of Methods	p. 59
Application to the Rosetta Compendium	p. 68
Results of Analyses	p. 70
Discussion	p. 74
References	p. 75
Modeling and Simulation of the Immune System as a Self-Regulating Network	p. 79
Introduction	p. 80
Mathematical Modeling of the Immune Network	p. 84
Two Examples of Models to Understand T Cell Regulation	p. 92
How to Implement Mathematical Models in Computer Simulations	p. 100
Concluding Remarks	p. 105
Acknowledgmentss	p. 106
References	p. 107
Entropy Demystified: The ôThermoö-dynamics of Stochastically Fluctuating Systems	p. 111
Introduction	p. 112
Energy	p. 113
Entropy and ôThermoö-dynamics of Markov Processes	p. 117
A Three-State Two-Cycle Motor Protein	p. 122
Phosphorylation-Dephosphorylation Cycle1 Kinetics	p. 125
Summary and Challenges	p. 131
References	p. 132
Effect of Kinetics on Sedimentation Velocity Profiles and the Role of Intermediates	p. 135
Introduction	p. 136
Methods	p. 138
ABCD Systems	p. 141
Monomer-Tetramer Model	p. 151
Summary	p. 158
Acknowledgmentss	p. 159
References	p. 159
Algebraic Models of Biochemical Networks	p. 163
Introduction	p. 164
Computational Systems Biology	p. 165
Network Inference	p. 176
Reverse-Engineering of Discrete Models: An Example	p. 181
Discussion	p. 190
References	p. 193
High-Through put Computing in the Sciences	p. 197
What is an HTC Application?	p. 199
HTC Technologies	p. 200
High-Throughput Computing Examples	p. 204
Advanced Topics	p. 218
Summary	p. 226
References	p. 226
Large Scale Transcriptome Data Integration Across Multiple Tissues to Decipher Stem Cell Signatures	p. 229
Introduction	p. 230
Systems and Data Sources	p. 231
Data Integration	p. 236
Artificial Neural Network Training and Validation	p. 238
Future Development and Enhancement Plans	p. 243
Acknowledgmentss	p. 244
References	p. 244
DynaFit-A Software Package for Enzymology	p. 247
Introduction	p. 248
Equilibrium Binding Studies	p. 250
Initial Rates of Enzyme Reactions	p. 255
Time Course of Enzyme Reactions	p. 260
General Methods and Algorithms	p. 262
Concluding Remarks	p. 275
Acknowledgmentss	p. 276
References	p. 276
Discrete Dynamic Modeling of Cellular Signaling Networks	p. 281
Introduction	p. 282
Cellular Signaling Networks	p. 284
Boolean Dynamic Modeling	p. 286
Variants of Boolean Network Models	p. 297
Application Examples	p. 301
Conclusion and Discussion	p. 303
Acknowledgmentss	p. 303
References	p. 303
The Basic Concepts of Molecular Modeling	p. 307
Introduction	p. 308
Homology Modeling	p. 308
Molecular Dynamics	p. 317
Molecular Docking	p. 324
References	p. 330
Deterministic and Stochastic Models of Genetic Regulatory Networks	p. 335
Introduction	p. 336
Boolean Networks	p. 337
Differential Equation Models	p. 343
Probabilistic Boolean Networks	p. 347
Stochastic Differential Equation Models	p. 351
References	p. 353
Bayesian Probability Approach to ADHD Appraisal	p. 357
Introduction	p. 358
Bayesian Probability Algorithm	p. 362
The Value of Bayesian Probability Approach as a Meta-Analysis Tool	p. 369
Discussion and Future Directions	p. 373
Acknowledgments	p. 377
References	p. 378
Simple Stochastic Simulation	p. 381
Introduction	p. 382
Understanding Reaction Dynamics	p. 385
Graphical Notation	p. 386
Reactions	p. 389
Reaction Kinetics	p. 389
Transition Firing Rules	p. 393
Summary	p. 406
Notes	p. 407
References	p. 409
Monte Carlo Simulation in Establishing Analytical Quality Requirements for Clinical Laboratory Tests: Meeting Clinical Needs	p. 411
Introduction	p. 412
Modeling Approach	p. 414
Methods for Simulation Study	p. 416
Results	p. 417
Discussion	p. 429
References	p. 431
Nonlinear Dynamical Analysis and Optimization for Biological/Biomedical Systems	p. 435
Introduction	p. 436
Hypothalamic-Pituitary-Adrenal Axis System	p. 437
Development of a Clinically Relevant Performance-Assessment Tools	p. 441
Dynamic Programming	p. 452
Computation of Optimal Treatments for HPA Axis System	p. 455
Conclusions	p. 458
Acknowledgmentss	p. 458
References	p. 458
Modeling of Growth Factor-Receptor Systems: From Molecular-Level Protein Interaction Networks to Whole-Body Compartment Models	p. 461
Background	p. 462
Molecular-Level Kinetics Models: Simulation of In Vitro Experiments	p. 466
Mesoscale Single-Tissue 3D Models: Simulation of In Vivo Tissue Regions	p. 474
Single-Tissue Compartmental Models: Simulation at In Vivo Tissue	p. 482
Multitissue Compartmental Models: Simulation of Whole Body	p. 485
Conclusions	p. 493
Acknowledgmentss	p. 494
References	p. 494
The Least-Squares Analysis of Data from Binding and Enzyme Kinetics Studies: Weights, Bias, and Confidence Intervals in Usual and Unusual Situations	p. 499
Introduction	p. 500
Least Squares Review	p. 503
Statistics of Reciprocals	p. 506
Weights When y is a True Dependent Variable	p. 511
Unusual Weighting: When x is the Dependent Variable	p. 521
Assessing Data Uncertainty: Variance Function Estimation	p. 524
Conclusion	p. 526
References	p. 527
Nonparametric Entropy Estimation Using Kernel Densities	p. 531
Introduction	p. 532
Motivating Application: Classifying Cardiac Rhythms	p. 533
Renyi Entropy and the Friedman-Tukey Index	p. 535
Kernel Density Estimation	p. 536
Mean-Integrated Square Error	p. 538
Estimating the FT Index	p. 540
Connection Between Template Matches and Kernel Densities	p. 544
Summary and Future Work	p. 545
Acknowledgmentss	p. 545
References	p. 546
Pancreatic Network Control of Glucagon Secretion and Counterregulation	p. 547
Introduction	p. 548
Mechanisms of Glucagon Counterregulation (GCR) Dysregulation in Diabetes	p. 550
Interdisciplinary Approach to Investigating the Defects in the GCR	p. 551
Initial Qualitative Analysis of the GCR Control Axis	p. 553
Mathematical Models of the GCR Control Mechanisms in STZ-Treated Rats	p. 556
Approximation of the Normal Endocrine Pancreas by a Minimal Control Network (MCN) and Analysis of the GCR Abnormalities in the Insulin Deficient Pancreas	p. 560
Advantages and Limitations of the Interdisciplinary Approach	p. 571
Conclusions	p. 575
Acknowledgments	p. 575
References	p. 575
Enzyme Kinetics and Computational Modeling for Systems Biology	p. 583
Introduction	p. 584
Computational Modeling and Enzyme Kinetics	p. 586
Yeast Triosephosphate Isomerase (EC 5.3.1.1)	p. 588
Initial Rate Analysis	p. 590
Progress Curve Analysis	p. 594
Concluding Remarks	p. 598
Acknowledgments	p. 598
References	p. 598
Fitting Enzyme Kinetic Data with KinTek Global Kinetic Explorer	p. 601
Background	p. 602
Challenges of Fitting by Simulation	p. 603
Methods	p. 605
Progress Curve Kinetics	p. 610
Fitting Full Progress Curves	p. 613
Slow Onset Inhibition Kinetics	p. 620
Summary	p. 624
Acknowledgments	p. 625
References	p. 625
Author Index	p. 627
Subject Index	p. 637
Table of Contents provided by Ingram. All Rights Reserved.

Methods in Enzymology, Volume 465

Volume 467

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

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