Computer methods, Part 2

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 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 Pt. A. Phase response curves: elucidatind the dynamics of coupled oscillators / A. Granada ... [et al.] Multiple ion binding equilibria, reaction kinetics, and thermodynamics in dynamic models of biochemical pathways / Kalyan C. Vinnakota ... [et al.] Analytical methods for the retrieval and interpretation of continuous glucose monitoring data in diabetes / Boris Kovatchev, Marc Breton, William Clarke Analysis of heterogeneity in molecular weight and shape by analytical ultracentrifugation using parallel distributed computing / Borries Demeler, Emre Brookes, Luitgard Nagel-Steger Discrete stochastic simulation methods for chemically reacting systems / Yang Cao, David C. Samuels Analyses for physiological and behavioral rhythmicity / Harold B. Dowse Computational approach for the rational design of stable proteins and enzymes; optimization of surface charge charge interactions / Katrina L. Schweiker, George I. Makhatadze ^ Efficient computation of confidence intervals for Bayesian model predictions based on multidimensional parameter space / Amber D. Smith ... [et al.] Analyzing enzymatic pH activity profiles and protein titration curves using structure-based pKa calculations and titration curve fitting / Jens Erik Nielsen Least squares in calibration: weights, nonlinearity, and other nuisances / Joel Tellinghuisen Evaluation and comparison of computational models / Jay I. Myung, Yun Tang, Mark A. Pitt Desegregating undergraduate mathematics and biology interdisciplinary instruction with emphasis on ongoing biomedical research / Raina Robeva Mathematical algorithms for high-resolution DNA melting analysis / Robert Palais, Carl T. Wittwer Biomathematical modeling of pulsatile hormone secretion: a historical perspective / William S. Evans, Leon S. Farhy, Michael L. Johnson ^ ^^ AutoDecon: a robust numerical method for the quantification of pulsatile events / Michael L. Johnson ... [et al.] Modeling fatigue over sleep deprivation, circadian rhythm, and caffeine with a minimal performance inhibitor model / Patrick L. Benitez ... [et al.] ^^