Immunological Bioinformatics
Ole Lund, Morten Nielsen, Claus Lundegaard, Can Kesmir, Søren Brunak
- 出版商: MIT
- 出版日期: 2005-06-17
- 售價: $1,980
- 貴賓價: 9.8 折 $1,940
- 語言: 英文
- 頁數: 312
- 裝訂: Hardcover
- ISBN: 0262122804
- ISBN-13: 9780262122801
-
相關分類:
生物資訊 Bioinformatics
立即出貨 (庫存 < 3)
買這商品的人也買了...
-
$2,830$2,689 -
$980$784 -
$1,663$1,575 -
$560$504 -
$299$254 -
$299$254 -
$1,986Code Complete: A Practical Handbook of Software Construction, 2/e (Paperback)
-
$1,121Bioinformatics Biocomputing and Perl: An Introduction to Bioinformatics Computing Skills and Practice
-
$650$507 -
$550$468 -
$199$179 -
$2,110$2,005 -
$1,400$1,330 -
$490$382 -
$860$731 -
$880$581 -
$680$537 -
$3,040CCIE Professional Development: Routing TCP/IP, Volume 1, 2/e (Hardcover)
-
$580$493 -
$390$332 -
$650$507 -
$720$612 -
$1,323Introduction to Machine Learning, 2/e (Hardcover)
-
$550$468 -
$4,725$4,489
相關主題
商品描述
Description:
Despite the fact that advanced bioinformatics methodologies have not been used as extensively in immunology as in other subdisciplines within biology, research in immunological bioinformatics has already developed models of components of the immune system that can be combined and that may help develop therapies, vaccines, and diagnostic tools for such diseases as AIDS, malaria, and cancer.
In a broader perspective, specialized bioinformatics methods in immunology make possible for the first time a systems-level understanding of the immune system. The traditional approaches to immunology are reductionist, avoiding complexity but providing detailed knowledge of a single event, cell, or molecular entity. Today, a variety of experimental bioinformatics techniques connected to the sequencing of the human genome provides a sound scientific basis for a comprehensive description of the complex immunological processes.
This book offers a description of bioinformatics techniques as they are applied to immunology, including a succinct account of the main biological concepts for students and researchers with backgrounds in mathematics, statistics, and computer science as well as explanations of the new data-driven algorithms in the context of biological data that will be useful for immunologists, biologists, and biochemists working on vaccine design. In each chapter the authors show interesting biological insights gained from the bioinformatics approach. The book concludes by explaining how all the methods presented in the book can be integrated to identify immunogenic regions in microorganisms and host genomes.
Ole Lund is Associate Professor and leader of the Immunological Bioinformatics group at the Center for Biological Sequence Analysis at Technical University of Denmark.
Morten Nielsen is Associate Professor at the Center for Biological Sequence Analysis at Technical University of Denmark.
Claus Lundegaard is Associate Professor at the Center for Biological Sequence Analysis at Technical University of Denmark.
Can Kesmir is Assistant Professor in the Department of Theoretical Biology at Utrecht University.
Søren Brunak is Professor and Director of the Center for Biological Sequence Analysis at the Technical University of Denmark.
Table of Contents:
Preface ix 1 Immune Systems and Systems Biology 1 1.1 Innate and Adaptive Immunity in Vertebrates 10 1.2 Antigen Processing and Presentation 11 1.3 Individualized Immune Reactivity 14 2 Contemporary Challenges to the Immune System 17 2.1 Infectious Diseases in the New Millennium 17 2.2 Major Killers in the World 17 2.3 Childhood Diseases 21 2.4 Clustering of Infectious Disease Organisms 22 2.5 Biodefense Targets 24 2.6 Cancer 30 2.7 Allergy 31 2.8 Autoimmune Diseases 32 3 Sequence Analysis in Immunology 35 3.1 Sequence Analysis 35 3.2 Alignments 36 3.3 Multiple Alignments 52 3.4 DNA Alignments 54 3.5 Molecular Evolution and Phylogeny 55 3.6 Viral Evolution and Escape: Sequence Variation 57 3.7 Prediction of Functional Features of Biological Sequences 61 4 Methods Applied in Immunological Bioinformatics 69 4.1 Simple Motifs, Motifs and Matrices 69 4.2 Information Carried by Immunogenic Sequences 72 4.3 Sequence Weighting Methods 75 4.4 Pseudocount Correction Methods 77 4.5 Weight on Pseudocount Correction 79 4.6 Position Specific Weighting 79 4.7 Gibbs Sampling 80 4.8 Hidden Markov Models 84 4.9 Artificial Neural Networks 91 4.10 Performance Measures for Prediction Methods 99 4.11 Clustering and Generation of Representative Sets 102 5 DNA Microarrays in Immunology 103 5.1 DNA Microarray Analysis 103 5.2 Clustering 106 5.3 Immunological Applications 108 6 Prediction of Cytotoxic T Cell (MHC Class I) Epitopes 111 6.1 Background and Historical Overview of Methods for Peptide MHC Binding Prediction 112 6.2 MHC Class I Epitope Binding Prediction Trained on Small Data Sets 114 6.3 Prediction of CTL Epitopes by Neural Network Methods 120 6.4 Summary of the Prediction Approach 133 7 Antigen Processing in the MHC Class I Pathway 135 7.1 The Proteasome 135 7.2 Evolution of the Immunosubunits 137 7.3 Specificity of the (Immuno)Proteasome 139 7.4 Predicting Proteasome Specificity 143 7.5 Comparison of Proteasomal Prediction Performance 147 7.6 Escape from Proteasomal Cleavage 149 7.7 Post-Proteasomal Processing of Epitopes 150 7.8 Predicting the Specificity of TAP 153 7.9 Proteasome and TAP Evolution 154 8 Prediction of Helper T Cell (MHC Class II) Epitopes 157 8.1 Prediction Methods 158 8.2 The Gibbs Sampler Method 159 8.3 Further Improvements of the Approach 172 9 Processing of MHC Class II Epitopes 175 9.1 Enzymes Involved in Generating MHC Class II Ligands 176 9.2 Selective Loading of Peptides to MHC Class II Molecules 179 9.3 Phylogenetic Analysis of the Lysosomal Proteases 180 9.4 Signs of the Specificities of Lysosomal Proteases on MHC Class II Epitopes 182 9.5 Predicting the Specificity of Lysosomal Enzymes 182 10 B Cell Epitopes 187 10.1 Affinty Maturation 188 10.2 Recognition of Antigen by B Cells 191 10.3 Neutralizing Antibodies 201 11 Vaccine Design 203 11.1 Categories of Vaccines 204 11.2 Polytope Vaccine: Optimizing Plasmid Design 207 11.3 Therapeutic Vaccines 209 11.4 Vaccine Market 213 12 Web-Based Tools for Vaccine Design 215 12.1 Databases of MHC Ligands 215 12.2 Prediction Servers 217 13 MHC Polymorphism 223 13.1 What Causes MHC Polymorphism? 223 13.2 MHC Supertypes 225 14 Predicting Immunogenicity: An Integrative Approach 243 14.1 Combination of MHC and Proteasome Predictions 244 14.2 Independent Contributions from TAP and Proteasome Predictions 245 14.3 Combinations of MHC, TAP, and Proteasome Predictions 247 14.4 Validation on HIV Data Set 251 14.5 Perspectives on Data Integration 252 References 254 Index 291
商品描述(中文翻譯)
描述:
儘管在生物學的其他子學科中,先進的生物信息學方法尚未像在免疫學中那樣廣泛應用,但免疫生物信息學的研究已經開發出了免疫系統組成部分的模型,這些模型可以結合起來,有助於開發治療方法、疫苗和診斷工具,用於艾滋病、瘧疾和癌症等疾病。從更廣泛的角度來看,免疫學中的專門生物信息學方法首次實現了對免疫系統的系統級理解。傳統的免疫學方法是還原主義的,避免複雜性,但提供了對單一事件、細胞或分子實體的詳細知識。如今,與人類基因組序列相關的各種實驗生物信息學技術為對複雜免疫過程的全面描述提供了堅實的科學基礎。本書介紹了生物信息學技術在免疫學中的應用,包括對數學、統計和計算機科學背景的學生和研究人員提供的主要生物概念的簡明介紹,以及在生物數據背景下解釋新的數據驅動算法,這對於從事疫苗設計的免疫學家、生物學家和生物化學家都是有用的。在每一章中,作者展示了從生物信息學方法中獲得的有趣的生物學見解。本書最後解釋了如何將書中介紹的所有方法整合起來,以識別微生物和宿主基因組中的免疫原性區域。Ole Lund是丹麥技術大學生物序列分析中心免疫生物信息學小組的副教授和領導者。Morten Nielsen是丹麥技術大學生物序列分析中心的副教授。Claus Lundegaard是丹麥技術大學生物序列分析中心的副教授。Can Kesmir是荷蘭烏特勒支大學理論生物學系的助理教授。Søren Brunak是丹麥技術大學生物序列分析中心的教授和主任。
目錄:
前言
1. 免疫系統和系統生物學
1.1