Models and Algorithms for Biomolecules and Molecular Networks E-Book

Bhaskar DasGupta

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: IEEE Press Series on Biomedical Engineering
• Sprache: Englisch

Seitenzahl: 550

Veröffentlichungsjahr: 2016

Beschreibung

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

Leseprobe

CONTENTS

Cover

Series Page

Title Page

Copyright

Dedication

List of Figures

List of Tables

Foreword

Acknowledgments

Chapter 1: Geometric Models of Protein Structure and Function Prediction

1.1 Introduction

1.2 Theory and Model

1.3 Algorithm and Computation

1.4 Applications

1.5 Discussion and Summary

References

Exercises

Chapter 2: Scoring Functions for Predicting Structure and Binding of Proteins

2.1 Introduction

2.2 General Framework of Scoring Function and Potential Function

2.3 Statistical Method

2.4 Optimization Method

2.5 Applications

2.6 Discussion and Summary

References

Exercises

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

3.1 Introduction

3.2 Principles of Monte Carlo Sampling

3.3 Markov Chains and Metropolis Monte Carlo Sampling

3.4 Sequential Monte Carlo Sampling

3.5 Applications

3.6 Discussion and summary

References

Exercises

Chapter 4: Stochastic Molecular Networks

4.1 Introduction

4.2 Reaction System and Discrete Chemical Master Equation

4.3 Direct Solution of Chemical Master Equation

4.4 Quantifying and Controlling Errors from State Space Truncation

4.5 Approximating Discrete Chemical Master Equation

4.6 Stochastic Simulation

4.7 Applications

4.8 Discussions and Summary

References

Exercises

Chapter 5: Cellular Interaction Networks

5.1 Basic Definitions and Graph-theoretic Notions

5.2 Boolean Interaction Networks

5.3 Signal Transduction Networks

5.4 Reverse Engineering of Biological Networks

References

Exercises

Chapter 6: Dynamical Systems and Interaction Networks

6.1 Some Basic Control-Theoretic Concepts

6.2 Discrete-Time Boolean Network Models

6.3 Artificial Neural Network Models

6.4 Piecewise Linear Models

6.5 Monotone Systems

References

Exercises

Chapter 7: Case Study of Biological Models

7.1 Segment Polarity Network Models

7.2 ABA-Induced Stomatal Closure Network

7.3 Epidermal Growth Factor Receptor Signaling Network

7.4 C. Elegans Metabolic Network

7.5 Network for T-Cell Survival and Death in Large Granular Lymphocyte Leukemia

References

Exercises

Glossary

Index

End User License Agreement

List of Tables

Table 2.1

Table 2.2

List of Illustrations

Figure 1.1

Figure 1.2

Figure 1.3

Figure 1.4

Figure 1.5

Figure 1.6

Figure 1.7

Figure 1.8

Figure 1.9

Figure 1.10

Figure 2.1

Figure 2.2

Figure 2.3

Figure 2.4

Figure 2.5

Figure 3.1

Figure 3.2

Figure 3.3

Figure 3.4

Figure 4.1

Figure 4.2

Figure 4.3

Figure 4.4

Figure 4.5

Figure 5.1

Figure 5.2

Figure 5.3

Figure 5.4

Figure 5.5

Figure 5.6

Figure 5.7

Figure 5.8

Figure 5.9

Figure 5.10

Figure 5.11

Figure 5.12

Figure 5.13

Figure 5.14

Figure 5.15

Figure 5.16

Figure 5.17

Figure 5.18

Figure 5.19

Figure 5.20

Figure 5.21

Figure 5.22

Figure 5.23

Figure 5.24

Figure 5.25

Figure 6.1

Figure 6.2

Figure 6.3

Figure 6.4

Figure 6.5

Figure 6.6

Figure 6.7

Figure 6.8

Figure 6.9

Figure 6.10

Figure 6.11

Figure 7.1

Figure 7.2

Figure 7.3

Figure 7.4

Guide

Cover

Table of Contents

Begin Reading

Chapter 1

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IEEE Press445 Hoes LanePiscataway, NJ 08854

IEEE Press Editorial BoardTariq Samad, Editor in Chief

George W. Arnold

Vladimir Lumelsky

Linda Shafer

Dmitry Goldgof

Pui-In Mak

Zidong Wang

Ekram Hossain

Jeffrey Nanzer

MengChu Zhou

Mary Lanzerotti

Ray Perez

George Zobrist

Kenneth Moore, Director of IEEE Book and Information Services (BIS)

Technical Reviewer

Dong Xu, University of Missouri

Models and Algorithms for Biomolecules and Molecular Networks

Bhaskar Dasgupta

Department of Computer ScienceUniversity of Illinois at ChicagoChicago, IL

Jie Liang

Department of BioengineeringUniversity of Illinois at ChicagoChicago, IL

Copyright © 2016 by The Institute of Electrical and Electronics Engineers, Inc.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. All rights reserved

Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data is available.

ISBN: 978-0-470-60193-8

Dedication

Dedicated to our spouses and students

List of Tables

2.1

Miyazawa–Jernigan Contact Energies in

kT

Units.

2.2

Recognition of Native Binding Surface of CAPRI Targets by Alpha Potential Function

Foreword

The subjects of this book are biomolecules and biomolecular networks. The first part of the book will cover surface and volume representation of the structures of biomolecules based on the idealized ball model. The underlying geometric constructs as well as their computation will then be discussed. This will be followed by the chapter on constructing effective scoring functions in different functional forms using either the statistical approach or the optimization approach, with the goal of identifying native-like protein structures or protein–protein interfaces, as well as constructing a general fitness landscape for protein design. The topic of sampling and estimation that can be used to generate biomolecular structures and to estimate their evolutionary patterns are then discussed, with equal emphasis on the Metropolis Monte Carlo (or Markov Chain Monte Carlo) approach and the chain growth (or sequential Monte Carlo) approach. This is followed by a chapter covering the topic of stochastic networks formed by interacting biomolecules and the framework of discrete chemical master equations, as well as computational methods for direct numerical computation and for sampling reaction trajectories of the probabilistic landscape of these networks.

The second part of the book will cover interaction networks of biomolecules. We will discuss stochastic models for networks with small copy numbers of molecular species, such as those arising in genetic circuits, protein synthesis, and transcription binding, and algorithms of computing the properties of stochastic molecular networks. We will then cover signal transduction networks that arise, for example, in complex interactions between the numerous constituents such as DNAs, RNAs, proteins, and small molecules in a complex biochemical system such as a cell. We will also discuss the experimental protocols and algorithmic methodologies necessary to synthesize these networks. Of special interest will be the synthesis of these networks from double-causal experimental evidences, as well as methods for reverse engineering of such networks based on suitable experimental protocols.

This book is written for graduate students, upper division undergraduate students, engineers, and scientists in academia and industries from a variety of disciplines, such as bioengineering, biophysics, electric engineering, chemical engineering, mathematics, biology, and computer science. It may also serve as a useful reference for researchers in these disciplines, including professional engineers and professional statisticians as well as practicing scientists in the pharmaceutical industry and the biotechnology industry. This book may be used as a monograph for learning important research topics and for finding algorithms and solutions to problems encountered in research and in practice.

Acknowledgments

For Bhaskar DasGupta, this book could not have been written without collaboration with a large number of collaborators from different research areas, and he thanks all of them for their involvements. Special thanks go to his colleagues Réka Albert, Piotr Berman, and Eduardo Sontag for their enormous patience and contribution during collaborations. He would like to thank individually all the students and postdoctoral fellows involved in these projects (Anthony Gitter, Gamze Gürsoy, Rashmi Hegde, Gowri Sangeetha Sivanathan, Pradyut Pal, Paola Vera-Licona, Riccardo Dondi, Sema Kachalo, Ranran Zhang, Yi Zhang, Kelly Westbrooks, and German Enciso). Bhaskar DasGupta thankfully acknowledges generous financial support from the National Science Foundation through grants DBI-1062328, IIS-1064681, IIS-0346973, DBI-0543365, IIS-0610244, CCR-9800086, CNS-0206795, and CCF-0208749, along with generous support from the DIMACS Center of Rutgers University during his Sabbatical leave through their special focus on computational and mathematical epidemiology. Last, but not least, Bhaskar DasGupta thanks his wife Paramita Bandopadhyay for her help, understanding, and cooperation while the book was being written.

For Jie Liang, the material in this book draws on research collaborations with many colleagues, to whom he is grateful. Special thanks go to Rong Chen, Ken Dill, Linda Kenney, Herbert Edelsbrunner, and Shankar Subramaniam, with whom he has worked when embarking on new research directions. He also has the good fortunate of working with a group of talented students and postdoctoral researchers, who have contributed to research projects, some of which are reflected in material described in this book: Larisa Adamian, Andrew Binkowski, Youfang Cao, Joseph Dundas, Gamze Gürsoy, Changyu Hu, David Jiminez-Morales, Ronald Jauckups, Jr., Sema Kachalo, Xiang Li, Yingzi Li, Meishan Lin, Hsiao-Mei Lu, Chih-Hao Lu, Hammad Naveed, Zheng Ouyang, Arun Setty, Nathan Stitziel, Ke Tang, Anna Terebus, Wei Tian, Jeffrey Tseng, Yaron Turpaz, Yun Xu, Jian Zhang, and Jinfeng Zhang. Jie Liang also thanks students at the University of Illinois at Chicago who have taken the bioinformatics courses he taught. Jie Liang thanks Xiang Li for co-writing the material for the chapter on scoring function, and he also thanks Ke Tang for help in preparing the figures. Jie Liang also acknowledges generous research support from the National Institutes of Health (GM079804, GM086145, GM68958, and GM081682), the National Science Foundation (DBI-0078270, DBI-0133856, DBI-0646035, DMS-0800257, and DBI-1062328), the Office of Naval Research (N000140310329 and N00014-06), the Whittaker Foundation, the Chicago Biomedical Consortium, and the Petroleum Research Fund (PRF–35616-G7). Finally, he wishes to thank his wife Re-Jin Guo for her understanding and patience during the period when the book was being written.

B. D. and J. L.

1Geometric Models of Protein Structure and Function Prediction

1.1 Introduction

Three-dimensional atomic structures of protein molecules provide rich information for understanding how these working molecules of a cell carry out their biological functions. With the amount of solved protein structures rapidly accumulating, computation of geometric properties of protein structure becomes an indispensable component in studies of modern biochemistry and molecular biology. Before we discuss methods for computing the geometry of protein molecules, we first briefly describe how protein structures are obtained experimentally.

There are primarily three experimental techniques for obtaining protein structures: X-ray crystallography, solution nuclear magnetic resonance (NMR), and recently freeze-sample electron microscopy (cryo-EM). In X-ray crystallography, the diffraction patterns of X-ray irradiation of a high-quality crystal of the protein molecule are measured. Since the diffraction is due to the scattering of X-rays by the electrons of the molecules in the crystal, the position, the intensity, and the phase of each recorded diffraction spot provide information for the reconstruction of an electron density map of atoms in the protein molecule. Based on independent information of the amino acid sequence, a model of the protein conformation is then derived by fitting model conformations of residues to the electron density map. An iterative process called refinement is then applied to improve the quality of the fit of the electron density map. The final model of the protein conformation consists of the coordinates of each of the non-hydrogen atoms [46].

The solution NMR technique for solving protein structure is based on measuring the tumbling and vibrating motion of the molecule in solution. By assessing the chemical shifts of atomic nuclei with spins due to interactions with other atoms in the vicinity, a set of estimated distances between specific pairs of atoms can be derived from NOSEY spectra. When a large number of such distances are obtained, one can derive a set of conformations of the protein molecule, each being consistent with all of the distance constraints [10]. Although determining conformations from either X-ray diffraction patterns or NMR spectra is equivalent to solving an ill-posed inverse problem, a technique such as Bayesian Markov chain Monte Carlo with parallel tempering has been shown to be effective in obtaining protein structures from NMR spectra [52].