Computer methods, Part 2

Correlation Analysis: A Tool for Comparing Relaxation-Type Models to Experimental Data
......Page 46
Introduction......Page 47
Scatter Plots and Correlation Analysis......Page 48
Example 1: Relaxation Oscillations......Page 49
Example 2: Square Wave Bursting......Page 58
Example 3: Elliptic Bursting......Page 60
Example 4: Using Correlation Analysis on Experimental Data
......Page 63
Appendix: Algorithm for the Determination of Phase Durations During Bursting
......Page 64
References......Page 65
Trait Variability of Cancer Cells Quantified by High-Content Automated Microscopy of Single Cells......Page 68
Introduction......Page 69
Background......Page 70
Experimental and Computational Workflow......Page 71
Time-lapse image acquisition......Page 73
Image processing......Page 75
Statistical analysis......Page 76
Statistical subpopulations......Page 77
Cell motility......Page 79
Single-cell motility analysis: image acquisition and validation......Page 80
Single-cell motility: Image processing......Page 81
Classic single-cell and population metrics......Page 82
Novel single-cell and population metrics......Page 87
Cell proliferation......Page 90
Single-cell proliferation rates: Image processing and parameter extraction......Page 91
Single-cell IMT and generation rate......Page 93
Progeny tree (clonal subpopulation) generation rates......Page 94
Analysis of sibling pairs......Page 96
Quality control......Page 98
References......Page 99
Introduction......Page 103
Clustering techniques......Page 107
Traditional statistical approaches......Page 108
Matrix factorization techniques......Page 109
Application to the Rosetta Compendium......Page 112
Results of Analyses......Page 114
Discussion......Page 118
References......Page 119
Modeling and Simulation of the Immune System as a Self-Regulating Network......Page 122
Introduction......Page 123
Complexity of immune regulation......Page 124
Self/nonself discrimination as a regulatory phenomenon......Page 126
Mathematical Modeling of the Immune Network......Page 127
Ordinary differential equations......Page 128
Delay differential equations......Page 130
Partial differential equations......Page 131
Agent-based models......Page 132
Stochastic differential equations......Page 133
Which modeling approach is appropriate?......Page 134
Two Examples of Models to Understand T Cell Regulation......Page 135
Intracellular regulation: The T cell program......Page 136
Intercellular regulation: iTreg-based negative feedback......Page 140
Simulation of the T cell program......Page 143
Simulation of the iTreg model......Page 146
Concluding Remarks......Page 148
Acknowledgments......Page 149
References......Page 150
Entropy Demystified: The ``Thermo´´-dynamics of Stochastically Fluctuating Systems......Page 153
Introduction......Page 154
Equilibrium and nonequilibrium steady state......Page 155
Cycle kinetics, thermodynamic box and detailed balance......Page 157
Entropy and ``Thermo´´-dynamics of Markov Processes......Page 159
Entropy and entropy balance equation......Page 160
``Equilibrium´´ and time reversibility......Page 161
``Free energy´´ and relative entropy......Page 163
A Three-State Two-Cycle Motor Protein......Page 164
PdPC signaling switch and phosphorylation energy......Page 167
PdPC with Michaelis-Menten kinetics......Page 170
Substrate specificity amplification......Page 172
A little historical reflection......Page 173
References......Page 174
Effect of Kinetics on Sedimentation Velocity Profiles and the Role of Intermediates......Page 177
Introduction......Page 178
Methods......Page 180
ABCD Systems......Page 183
Monomer-Tetramer Model......Page 193
Summary......Page 200
References......Page 201
Algebraic Models of Biochemical Networks......Page 204
Introduction......Page 205
Computational Systems Biology......Page 206
Network Inference......Page 217
Boolean networks: Deterministic and stochastic......Page 222
Inferring stochastic Boolean networks......Page 225
Polynome: Parameter estimation for Boolean models of biological networks......Page 226
Example: Inferring the lac operon......Page 230
Discussion......Page 231
References......Page 234
High-Throughput Computing in the Sciences......Page 238
What is an HTC Application?......Page 240
Scripting languages......Page 241
Batch queuing systems......Page 242
Portable batch system......Page 243
Data transformation......Page 245
Parameter space studies......Page 249
Monte Carlo simulations......Page 252
Problem decomposition......Page 256
Iterative refinement......Page 258
Resource restrictions......Page 259
Checkpointing......Page 260
File staging......Page 263
Grid systems......Page 264
References......Page 267
Large Scale Transcriptome Data Integration Across Multiple Tissues to Decipher Stem Cell Signatures......Page 269
Introduction......Page 270
Computing environment......Page 271
Normalization......Page 272
Databases......Page 274
Stem cells generalized hierarchy......Page 275
Integrating data to a final compendium indexed by common gene identifier......Page 276
Variation filtering......Page 277
Leave-one-out validation-generation of 31 ANN models......Page 278
Independence testing......Page 280
Applying the whole algorithm......Page 281
Future Development and Enhancement Plans......Page 283
References......Page 284
DynaFit-A Software Package for Enzymology......Page 286
Introduction......Page 287
Experiments involving intensive physical quantities......Page 289
NMR study of protein-protein interactions......Page 290
Independent binding sites and statistical factors......Page 291
Interacting versus independent sites on a trimeric enzyme......Page 292
Thermodynamic cycles in initial rate models......Page 294
Steady-state initial rate equation for DHFR......Page 295
Time Course of Enzyme Reactions......Page 299
SPR on-chip enzyme kinetics......Page 300
General Methods and Algorithms......Page 301
Systematic parameter scan......Page 302
Global minimization by differential evolution......Page 303
Uncertainty of model parameters......Page 308
``Shuffle´´ and ``shift´´ Monte-Carlo methods......Page 309
Two-dimensional histograms......Page 310
Model-discrimination analysis......Page 312
Model discrimination analysis......Page 314
References......Page 315
Discrete Dynamic Modeling of Cellular Signaling Networks......Page 320
Introduction......Page 321
Cellular Signaling Networks......Page 323
Boolean Dynamic Modeling......Page 325
Constructing the network backbone......Page 327
Determining transfer functions......Page 328
Selecting models for state transitions......Page 330
Analyzing steady states of the system......Page 332
Making biological implications and predictions......Page 334
Threshold Boolean networks......Page 336
Piecewise linear systems......Page 337
From Boolean switches to dose-response curves......Page 338
Abscisic acid-induced stomatal closure......Page 340
T-LGL survival signaling network......Page 341
References......Page 342
The Basic Concepts of Molecular Modeling......Page 346
Homology Modeling......Page 347
Sequence analysis......Page 348
Tertiary structure prediction......Page 352
Homology modeling......Page 353
Ab initio modeling......Page 354
Structure validation......Page 355
Molecular Dynamics......Page 356
Molecular mechanics......Page 357
Equilibration......Page 359
RMSD fluctuations......Page 360
Principal component analysis......Page 361
Basic components......Page 363
Choosing the correct tool......Page 364
Macromolecule......Page 365
Protein ligands......Page 366
Iterative docking and analysis......Page 367
Virtual screening......Page 368
References......Page 369
Deterministic and Stochastic Models of Genetic Regulatory Networks......Page 374
Introduction......Page 375
Boolean Networks......Page 376
Attractors as cell types and cellular functional states......Page 380
Differential Equation Models......Page 382
Accurate description of cellular growth and division and prediction of mutant phenotypes......Page 385
Probabilistic Boolean Networks......Page 386
Steady-state analysis and stability under stochastic fluctuations......Page 389
Stochastic Differential Equation Models......Page 390
The influence of noise on system behavior......Page 391
References......Page 392
Bayesian Probability Approach to ADHD Appraisal......Page 396
Introduction......Page 397
Prevalence......Page 398
Etiology of ADHD......Page 399
Summary of problem......Page 400
Methods......Page 401
Standardizing the scores for different tests......Page 402
Bayesian probability approach......Page 403
Subjects: Bayesian probability approach......Page 404
Procedure......Page 405
Results......Page 406
Methods......Page 408
Study II......Page 409
Procedure......Page 410
Statistical analyses......Page 411
Discussion and Future Directions......Page 412
Acknowledgments......Page 416
References......Page 417
Simple Stochastic Simulation......Page 420
Introduction......Page 421
Understanding Reaction Dynamics......Page 424
Graphical Notation......Page 425
Reaction Kinetics......Page 428
Second-order reactions......Page 429
Pseudo-first-order reactions......Page 431
Transition Firing Rules......Page 432
Ground rules......Page 433
First-order reactions......Page 434
Multiple options......Page 440
Pseudo-first-order and second-order reactions......Page 443
Summary......Page 445
Notes......Page 446
References......Page 448
Monte Carlo Simulation in Establishing Analytical Quality Requirements for Clinical Laboratory Tests: Meeting Clinical Needs
......Page 449
Introduction......Page 450
Simulation of assay imprecision and inaccuracy......Page 452
Modeling physiologic response to changing conditions......Page 453
Methods for Simulation Study......Page 454
Yale regimen......Page 455
University of Washington regimen......Page 465
Discussion......Page 467
References......Page 469
Nonlinear Dynamical Analysis and Optimization for Biological/Biomedical Systems......Page 472
Introduction......Page 473
Background......Page 474
System model......Page 475
Steady-state analysis......Page 476
Construction of an invariant manifold......Page 478
Evaluation of treatment options......Page 482
Development of an appropriate optimal control objective......Page 485
DP for deterministic systems......Page 489
Minimizing worst-case cost under uncertainty using DP......Page 491
Deterministic optimization......Page 492
Worst-case optimization......Page 493
References......Page 495
Modeling of Growth Factor-Receptor
Systems: From Molecular-Level
Protein Interaction Networks to
Whole-Body Compartment Models
......Page 497
Growth factor systems in angiogenesis......Page 498
Systems biology of VEGF: Interaction networks and molecular cross talk......Page 499
Multiscale biology of VEGF: Transport and signaling range......Page 500
Molecular-Level Kinetics Models: Simulation of In Vitro Experiments......Page 502
Case study: Mechanism of PlGF synergy-Shifting VEGF to VEGFR2 versus PlGF-VEGFR1 signaling......Page 504
Case study: Mechanism of NRP1-VEGFR2 coupling via VEGF165-Effect on VEGF isoform-specific receptor binding
......Page 508
2D and 3D tissue geometry based on microanatomy......Page 510
Volumes: Diffusion and consumption of oxygen......Page 513
Surfaces: Receptor-ligand interactions......Page 514
What is not included in these models?......Page 515
Case study: Proangiogenic VEGF cell-based therapy for muscle ischemia......Page 516
Case study: Proangiogenic exercise therapy for muscle ischemia......Page 517
Mathematical framework for tissue porosity and available volume fractions......Page 518
Case study: Pharmacodynamic mechanism and tumor microenvironment affect efficacy of anti-NRP1 therapy in cancer
......Page 519
Multitissue Compartmental Models: Simulation of Whole Body......Page 521
Macromolecular vascular permeability......Page 522
Lymphatic drainage......Page 523
Case study: Pharmacokinetics of anti-VEGF therapy in cancer......Page 524
Case study: Mechanism of sVEGFR1 as a ligand trap......Page 527
Conclusions......Page 529
References......Page 530
The Least-Squares Analysis of
Data from Binding and Enzyme
Kinetics Studies: Weights, Bias,
and Confidence Intervals in
Usual and Unusual Situations
......Page 534
Introduction......Page 535
Standard linear and nonlinear least squares......Page 538
Uncertainty in functions of uncertain quantities: Error propagation......Page 540
A simple Monte Carlo experiment......Page 541
Implications-The 10% rule of thumb......Page 544
Application to binding and kinetics data......Page 545
Constant sigmay
......Page 546
Illustrations for perfectly fitting data......Page 547
Real data example......Page 550
Monte Carlo simulations......Page 552
Effective variance treatment......Page 556
Checking the results with exactly fitting data......Page 557
Assessing Data Uncertainty: Variance Function Estimation......Page 559
Conclusion......Page 561
References......Page 562
Nonparametric Entropy Estimation Using Kernel Densities......Page 565
Introduction......Page 566
Motivating Application: Classifying Cardiac Rhythms......Page 567
Renyi Entropy and the Friedman-Tukey Index......Page 569
Kernel Density Estimation......Page 570
Mean-Integrated Square Error......Page 572
Estimating the FT Index......Page 574
Connection Between Template Matches and Kernel Densities......Page 578
Acknowledgments......Page 579
References......Page 580
Pancreatic Network Control of Glucagon Secretion and Counterregulation......Page 581
Introduction......Page 582
Mechanisms of Glucagon Counterregulation (GCR) Dysregulation in Diabetes......Page 584
Interdisciplinary Approach to Investigating the Defects in the GCR......Page 585
beta-Cell inhibition of alpha-cells
......Page 587
alpha-Cell stimulation of delta-cells
......Page 588
Glucose inhibition of alpha-cells
......Page 589
Mathematical Models of the GCR Control Mechanisms in STZ-Treated Rats......Page 590
Approximation of the Normal Endocrine Pancreas by a Minimal Control Network (MCN) and Analysis of the GCR Abnormalities in the Insulin Deficient Pancreas
......Page 594
Dynamic network approximation of the MCN......Page 595
Determination of the model parameters......Page 596
In silico experiments......Page 598
In silico experiments with simulated complete insulin deficiency......Page 599
Defective GCR response to hypoglycemia with the absence of a switch-off signal in the insulin deficient model
......Page 600
Reduction of the GCR response by high glucose conditions during the switch-off or by failure to terminate the intrapancreatic signal
......Page 601
Simulated transition from a normal physiology to an insulinopenic state......Page 603
Advantages and Limitations of the Interdisciplinary Approach......Page 605
References......Page 609
Enzyme Kinetics and Computational Modeling for Systems Biology......Page 616
Introduction......Page 617
Standards in computational systems biology......Page 619
COPASI: A biochemical modeling and simulation package......Page 620
Yeast Triosephosphate Isomerase (EC 5.3.1.1)......Page 621
Initial Rate Analysis......Page 623
Progress Curve Analysis......Page 627
References......Page 631
Fitting Enzyme Kinetic Data with KinTek Global Kinetic Explorer......Page 633
Background......Page 634
Challenges of Fitting by Simulation......Page 635
Defining the model......Page 637
Defining each experiment......Page 639
A note on units......Page 640
A note on statistics......Page 641
Progress Curve Kinetics......Page 642
Fitting Full Progress Curves......Page 645
Error analysis......Page 649
Slow Onset Inhibition Kinetics......Page 652
Summary......Page 656
References......Page 657