Models and Algorithms for Biomolecules and Molecular Networks

ISBN-13: 9780470601938 / Angielski / Twarda / 2016 / 274 str.

Jie Liang;Bhaskar DasGupta;¬Abar

Opis

By providing expositions to modeling principles, theories, computational solutions, and open problems, this reference presents a full scope on relevant biological phenomena, modeling frameworks, technical challenges, and algorithms.

• Up-to-date developments of structures of biomolecules, systems biology, advanced models, and algorithms
• Sampling techniques for estimating evolutionary rates and generating molecular structures
• Accurate computation of probability landscape of stochastic networks, solving discrete chemical master equations
• End-of-chapter exercises

Spis treści

List of Figures xiii

List of Tables xix

Foreword xxi

Acknowledgments xxiii

1 Geometric Models of Protein Structure and Function Prediction 1

1.1 Introduction, 1

1.2 Theory and Model, 2

1.2.1 Idealized Ball Model, 2

1.2.2 Surface Models of Proteins, 3

1.2.3 Geometric Constructs, 4

1.2.4 Topological Structures, 6

1.2.5 Metric Measurements, 9

1.3 Algorithm and Computation, 13

1.4 Applications, 15

1.4.1 Protein Packing, 15

1.4.2 Predicting Protein Functions from Structures, 17

1.5 Discussion and Summary, 20

References, 22

Exercises, 25

2 Scoring Functions for Predicting Structure and Binding of Proteins 29

2.1 Introduction, 29

2.2 General Framework of Scoring Function and Potential Function, 31

2.2.1 Protein Representation and Descriptors, 31

2.2.2 Functional Form, 32

2.2.3 Deriving Parameters of Potential Functions, 32

2.3 Statistical Method, 32

2.3.1 Background, 32

2.3.2 Theoretical Model, 33

2.3.3 Miyazawa––Jernigan Contact Potential, 34

2.3.4 Distance–Dependent Potential Function, 41

2.3.5 Geometric Potential Functions, 45

2.4 Optimization Method, 49

2.4.1 Geometric Nature of Discrimination, 50

2.4.2 Optimal Linear Potential Function, 52

2.4.3 Optimal Nonlinear Potential Function, 53

2.4.4 Deriving Optimal Nonlinear Scoring Function, 55

2.4.5 Optimization Techniques, 55

2.5 Applications, 55

2.5.1 Protein Structure Prediction, 56

2.5.2 Protein––Protein Docking Prediction, 56

2.5.3 Protein Design, 58

2.5.4 Protein Stability and Binding Affinity, 59

2.6 Discussion and Summary, 60

2.6.1 Knowledge–Based Statistical Potential Functions, 60

2.6.2 Relationship of Knowledge–Based Energy Functions and Further Development, 64

2.6.3 Optimized Potential Function, 65

2.6.4 Data Dependency of Knowledge–Based Potentials, 66

References, 67

Exercises, 75

3 Sampling Techniques: Estimating Evolutionary Rates and Generating Molecular Structures 79

3.1 Introduction, 79

3.2 Principles of Monte Carlo Sampling, 81

3.2.1 Estimation Through Sampling from Target Distribution, 81

3.2.2 Rejection Sampling, 82

3.3 Markov Chains and Metropolis Monte Carlo Sampling, 83

3.3.1 Properties of Markov Chains, 83

3.3.2 Markov Chain Monte Carlo Sampling, 85

3.4 Sequential Monte Carlo Sampling, 87

3.4.1 Importance Sampling, 87

3.4.2 Sequential Importance Sampling, 87

3.4.3 Resampling, 91

3.5 Applications, 92

3.5.1 Markov Chain Monte Carlo for Evolutionary Rate Estimation, 92

3.5.2 Sequentail Chain Growth Monte Carlo for Estimating Conformational Entropy of RNA Loops, 95

3.6 Discussion and Summary, 96

References, 97

Exercises, 99

4 Stochastic Molecular Networks 103

4.1 Introduction, 103

4.2 Reaction System and Discrete Chemical Master Equation, 104

4.3 Direct Solution of Chemical Master Equation, 106

4.3.1 State Enumeration with Finite Buffer, 106

4.3.2 Generalization and Multi–Buffer dCME Method, 108

4.3.3 Calculation of Steady–State Probability Landscape, 108

4.3.4 Calculation of Dynamically Evolving Probability Landscape, 108

4.3.5 Methods for State Space Truncation for Simplification, 109

4.4 Quantifying and Controlling Errors from State Space Truncation, 111

4.5 Approximating Discrete Chemical Master Equation, 114

4.5.1 Continuous Chemical Master Equation, 114

4.5.2 Stochastic Differential Equation: Fokker––Planck Approach, 114

4.5.3 Stochastic Differential Equation: Langevin Approach, 116

4.5.4 Other Approximations, 117

4.6 Stochastic Simulation, 118

4.6.1 Reaction Probability, 118

4.6.2 Reaction Trajectory, 118

4.6.3 Probability of Reaction Trajectory, 119

4.6.4 Stochastic Simulation Algorithm, 119

4.7 Applications, 121

4.7.1 Probability Landscape of a Stochastic Toggle Switch, 121

4.7.2 Epigenetic Decision Network of Cellular Fate in Phage Lambda, 123

4.8 Discussions and Summary, 127

References, 128

Exercises, 131

5 Cellular Interaction Networks 135

5.1 Basic Definitions and Graph–Theoretic Notions, 136

5.1.1 Topological Representation, 136

5.1.2 Dynamical Representation, 138

5.1.3 Topological Representation of Dynamical Models, 139

5.2 Boolean Interaction Networks, 139

5.3 Signal Transduction Networks, 141

5.3.1 Synthesizing Signal Transduction Networks, 142

5.3.2 Collecting Data for Network Synthesis, 146

5.3.3 Transitive Reduction and Pseudo–node Collapse, 147

5.3.4 Redundancy and Degeneracy of Networks, 153

5.3.5 Random InteractionNetworks and Statistical Evaluations, 157

5.4 Reverse Engineering of Biological Networks, 159

5.4.1 Modular Response Analysis Approach, 160

5.4.2 Parsimonious Combinatorial Approaches, 166

5.4.3 Evaluation of Quality of the Reconstructed Network, 171

References, 173

Exercises, 178

6 Dynamical Systems and Interaction Networks 183

6.1 Some Basic Control–Theoretic Concepts, 185

6.2 Discrete–Time Boolean Network Models, 186

6.3 Artificial Neural Network Models, 188

6.3.1 Computational Powers of ANNs, 189

6.3.2 Reverse Engineering of ANNs, 190

6.3.3 Applications of ANN Models in Studying Biological Networks, 192

6.4 Piecewise Linear Models, 192

6.4.1 Dynamics of PL Models, 194

6.4.2 Biological Application of PL Models, 195

6.5 Monotone Systems, 200

6.5.1 Definition of Monotonicity, 201

6.5.2 Combinatorial Characterizations and Measure of Monotonicity, 203

6.5.3 Algorithmic Issues in Computing the Degree of Monotonicity , 207

References, 209

Exercises, 214

7 Case Study of Biological Models 217

7.1 Segment Polarity Network Models, 217

7.1.1 Boolean Network Model, 218

7.1.2 Signal Transduction Network Model, 218

7.2 ABA–Induced Stomatal Closure Network, 219

7.3 Epidermal Growth Factor Receptor Signaling Network, 220

7.4 C. elegans Metabolic Network, 223

7.5 Network for T–Cell Survival and Death in Large Granular Lymphocyte Leukemia, 223

References, 224

Exercises, 225

Glossary 227

Index 229

O autorze

Bhaskar DasGupta is an associate professor within the Department of Computer Science at the University of Illinois at Chicago. He is a senior member of IEEE. He has written numerous journal papers.

Jie Liang is a professor within the Department of Bioengineering and Computer Science at the University of Illinois at Chicago. He has a Ph.D. in Biophysics. He has received numerous awards for his research. He has served as co–editor for several journal articles.