Structural Bioinformatics: An Algorithmic Approach, 1st Edition (Hardback) book cover

Structural Bioinformatics

An Algorithmic Approach, 1st Edition

By Forbes J. Burkowski

Chapman and Hall/CRC

429 pages | 24 Color Illus. | 124 B/W Illus.

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pub: 2008-10-30
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Description

The Beauty of Protein Structures and the Mathematics behind Structural Bioinformatics

Providing the framework for a one-semester undergraduate course, Structural Bioinformatics: An Algorithmic Approach shows how to apply key algorithms to solve problems related to macromolecular structure.

Helps Students Go Further in Their Study of Structural Biology

Following some introductory material in the first few chapters, the text solves the longest common subsequence problem using dynamic programming and explains the science models for the Nussinov and MFOLD algorithms. It then reviews sequence alignment, along with the basic mathematical calculations needed for measuring the geometric properties of macromolecules. After looking at how coordinate transformations facilitate the translation and rotation of molecules in a 3D space, the author introduces structural comparison techniques, superposition algorithms, and algorithms that compare relationships within a protein. The final chapter explores how regression and classification are becoming more useful in protein analysis and drug design.

At the Crossroads of Biology, Mathematics, and Computer Science

Connecting biology, mathematics, and computer science, this practical text presents various bioinformatics topics and problems within a scientific methodology that emphasizes nature (the source of empirical observations), science (the mathematical modeling of the natural process), and computation (the science of calculating predictions and mathematical objects based on mathematical models).

Reviews

… the book presents a number of topics in structural bioinformatics, aiming to emphasize the beauty of the area as well as some of the main problems. It targets advanced undergraduate students and hence the description of more complicated algorithms is avoided. It nevertheless provides an interesting introduction to the area.

—Lucian Ilie, Mathematical Reviews, Issue 2009k

Table of Contents

Preface

The Study of Structural Bioinformatics

Motivation

Small Beginnings

Structural Bioinformatics and the Scientific Method

A More Detailed Problem Analysis: Force Fields

Modeling Issues

Sources of Error

Summary

Introduction to Macromolecular Structure

Motivation

Overview of Protein Structure

Overview of RNA Structure

Data Sources, Formats, and Applications

Motivation

Sources of Structural Data

PDB File Format

Visualization of Molecular Data

Software for Structural Bioinformatics

Dynamic Programming

Motivation

Introduction

A DP Example: The Al Gore Rhythm for Giving Talks

A Recipe for Dynamic Programming

Longest Common Subsequence

RNA Secondary Structure Prediction

Motivation

Introduction to the Problem

The Nussinov Dynamic Programming

The MFOLD Algorithm: Terminology

Protein Sequence Alignment

Protein Homology

Variations in the Global Alignment Algorithm

The Significance of a Global Alignment

Local Alignment

Protein Geometry

Introduction

Calculations Related to Protein Geometry

Ramachandran Plots

Inertial Axes

Coordinate Transformations

Motivation

Introduction

Translation Transformations

Rotation Transformations

Isometric Transformations

Structure Comparison, Alignment, and Superposition

Motivation

Introduction

Techniques for Structural Comparison

Scoring Similarities and Optimizing Scores

Superposition Algorithms

Algorithms Comparing Relationships within a Protein

Machine Learning

Motivation

Issues of Complexity

Prediction via Machine Learning

Data Used during Training and Testing

Objectives of the Learning Algorithm

Linear Regression

Ridge Regression

Preamble for Kernel Methods

Kernel Functions

Classification

Heuristics for Classification

Nearest Neighbor Classification

Support Vector Machines

Linearly Nonseparable Data

Support Vector Machines and Kernels

Expected Test Error

Transparency

Overview of the Appendices

Index

About the Series

Chapman & Hall/CRC Mathematical and Computational Biology

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
COM012040
COMPUTERS / Programming / Games
MAT003000
MATHEMATICS / Applied
SCI008000
SCIENCE / Life Sciences / Biology / General
SCI010000
SCIENCE / Biotechnology
SCI055000
SCIENCE / Physics