Models and algorithms for biomolecules and molecular networks

Models and algorithms for biomolecules and molecular networks

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 biol...

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Detalles Bibliográficos
Autor Corporativo: IEEE Engineering in Medicine and Biology Society (-)
Otros Autores: DasGupta, Bhaskar (Professor of computer science), autor (autor), Liang, Jie, 1964- autor
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken : Wiley : IEEE Press [2016]
Colección:Wiley ebooks.
IEEE Press Series in Biomedical Engineering.
Acceso en línea:Conectar con la versión electrónica
Ver en Universidad de Navarra:https://innopac.unav.es/record=b40610214*spi
Tabla de Contenidos:
  • 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.

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