| Contributors |
| Preface |
| Volumes in Series |
| Correlation Analysis: A Tool for Comparing Relaxation-Type Models to Experimental Data / Maurizio Tomaiuolo ; Joel Tabak ; Richard Bertram1： |
| Introduction |
| Scatter Plots and Correlation Analysis / 2： |
| Example 1: Relaxation Oscillations / 3： |
| Example 2: Square Wave Bursting / 4： |
| Example 3: Elliptic Bursting / 5： |
| Example 4: Using Correlation Analysis on Experimental Data / 6： |
| Summary / 7： |
| Acknowledgments |
| References |
| Trait Variability of Cancer Cells Quantified by High-Content Automated Microscopy of Single Cells / Vito Quaranta ; Darren R. Tyson ; Shawn P. Garbett ; Brandy Weidow ; Mark P. Harris ; Walter Georgescu |
| Background |
| Experimental and Computational Workflow |
| Application to Traits Relevant to Cancer Progression |
| Conclusions |
| Matrix Factorization for Recovery of Biological Processes from Microarray Data / Andrew V. Kossenkov ; Michael F. Ochs |
| Overview of Methods |
| Application to the Rosetta Compendium |
| Results of Analyses |
| Discussion |
| Modeling and Simulation of the Immune System as a Self-Regulating Network / Peter S. Kim ; Doron Levy ; Peter P. Lee |
| Mathematical Modeling of the Immune Network |
| Two Examples of Models to Understand T Cell Regulation |
| How to Implement Mathematical Models in Computer Simulations |
| Concluding Remarks |
| Acknowledgmentss |
| Entropy Demystified: The ôThermoö-dynamics of Stochastically Fluctuating Systems / Hong Qian |
| Energy |
| Entropy and ôThermoö-dynamics of Markov Processes |
| A Three-State Two-Cycle Motor Protein |
| Phosphorylation-Dephosphorylation Cycle1 Kinetics |
| Summary and Challenges |
| Effect of Kinetics on Sedimentation Velocity Profiles and the Role of Intermediates / John J. Correia ; P. Holland Alday ; Peter Sherwood ; Walter F. Stafford |
| Methods |
| ABCD Systems |
| Monomer-Tetramer Model |
| Algebraic Models of Biochemical Networks / Reinhard Laubenbacher ; Abdul Salam Jarrah |
| Computational Systems Biology |
| Network Inference |
| Reverse-Engineering of Discrete Models: An Example |
| High-Through put Computing in the Sciences / Mark Morgan ; Andrew Grimshaw8： |
| What is an HTC Application? |
| HTC Technologies |
| High-Throughput Computing Examples |
| Advanced Topics |
| Large Scale Transcriptome Data Integration Across Multiple Tissues to Decipher Stem Cell Signatures / Ghislain Bidaut ; Christian J. Stoeckert9： |
| Systems and Data Sources |
| Data Integration |
| Artificial Neural Network Training and Validation |
| Future Development and Enhancement Plans |
| DynaFit-A Software Package for Enzymology / Petr Kuzmiĉ10： |
| Equilibrium Binding Studies |
| Initial Rates of Enzyme Reactions |
| Time Course of Enzyme Reactions |
| General Methods and Algorithms |
| Discrete Dynamic Modeling of Cellular Signaling Networks / Réka Albert ; Rui-Sheng Wang11： |
| Cellular Signaling Networks |
| Boolean Dynamic Modeling |
| Variants of Boolean Network Models |
| Application Examples |
| Conclusion and Discussion |
| The Basic Concepts of Molecular Modeling / Akansha Saxena ; Diana Wong ; Karthikeyan Diraviyam ; David Sept12： |
| Homology Modeling |
| Molecular Dynamics |
| Molecular Docking |
| Deterministic and Stochastic Models of Genetic Regulatory Networks / Ilya Shmulevich ; John D. Aitchison13： |
| Boolean Networks |
| Differential Equation Models |
| Probabilistic Boolean Networks |
| Stochastic Differential Equation Models |
| Bayesian Probability Approach to ADHD Appraisal / Raina Robeva ; Jennifer Kim Penberthy14： |
| Bayesian Probability Algorithm |
| The Value of Bayesian Probability Approach as a Meta-Analysis Tool |
| Discussion and Future Directions |
| Simple Stochastic Simulation / Maria J. Schilstra ; Stephen R. Martin15： |
| Understanding Reaction Dynamics |
| Graphical Notation |
| Reactions |
| Reaction Kinetics |
| Transition Firing Rules |
| Notes |
| Monte Carlo Simulation in Establishing Analytical Quality Requirements for Clinical Laboratory Tests: Meeting Clinical Needs / James C. Boyd ; David E. Bruns16： |
| Modeling Approach |
| Methods for Simulation Study |
| Results |
| Nonlinear Dynamical Analysis and Optimization for Biological/Biomedical Systems / Amos Ben-Zvi ; Jong Mm Lee17： |
| Hypothalamic-Pituitary-Adrenal Axis System |
| Development of a Clinically Relevant Performance-Assessment Tools |
| Dynamic Programming |
| Computation of Optimal Treatments for HPA Axis System |
| Modeling of Growth Factor-Receptor Systems: From Molecular-Level Protein Interaction Networks to Whole-Body Compartment Models / Florence T. H. Wu ; Marianne O. Stefanini ; Feilim Mac Gabhann ; Aleksander S. Popel18： |
| Molecular-Level Kinetics Models: Simulation of In Vitro Experiments |
| Mesoscale Single-Tissue 3D Models: Simulation of In Vivo Tissue Regions |
| Single-Tissue Compartmental Models: Simulation at In Vivo Tissue |
| Multitissue Compartmental Models: Simulation of Whole Body |
| The Least-Squares Analysis of Data from Binding and Enzyme Kinetics Studies: Weights, Bias, and Confidence Intervals in Usual and Unusual Situations / Joel Tellinghuisen19： |
| Least Squares Review |
| Statistics of Reciprocals |
| Weights When y is a True Dependent Variable |
| Unusual Weighting: When x is the Dependent Variable |
| Assessing Data Uncertainty: Variance Function Estimation |
| Conclusion |
| Nonparametric Entropy Estimation Using Kernel Densities / Douglas E. Lake20： |
| Motivating Application: Classifying Cardiac Rhythms |
| Renyi Entropy and the Friedman-Tukey Index |
| Kernel Density Estimation |
| Mean-Integrated Square Error |
| Estimating the FT Index |
| Connection Between Template Matches and Kernel Densities |
| Summary and Future Work |
| Pancreatic Network Control of Glucagon Secretion and Counterregulation / Leon S. Farhy ; Anthony L. McCall21： |
| Mechanisms of Glucagon Counterregulation (GCR) Dysregulation in Diabetes |
| Interdisciplinary Approach to Investigating the Defects in the GCR |
| Initial Qualitative Analysis of the GCR Control Axis |
| Mathematical Models of the GCR Control Mechanisms in STZ-Treated Rats |
| Approximation of the Normal Endocrine Pancreas by a Minimal Control Network (MCN) and Analysis of the GCR Abnormalities in the Insulin Deficient Pancreas |
| Advantages and Limitations of the Interdisciplinary Approach |
| Enzyme Kinetics and Computational Modeling for Systems Biology / Pedro Mendes ; Hanan Messiha ; Naglis Malys ; Stefan Hoops22： |
| Computational Modeling and Enzyme Kinetics |
| Yeast Triosephosphate Isomerase (EC 5.3.1.1) |
| Initial Rate Analysis |
| Progress Curve Analysis |
| Fitting Enzyme Kinetic Data with KinTek Global Kinetic Explorer / Kenneth A. Johnson23： |
| Challenges of Fitting by Simulation |
| Progress Curve Kinetics |
| Fitting Full Progress Curves |
| Slow Onset Inhibition Kinetics |
| Author Index |
| Subject Index |
