Ebook: In Silico Immunology

Immunology is an all important science, addressing, as it does the most pressing medical needs of our time: infectious disease and transplantation medicine. It has given us vaccines on the one hand and therapeutic antibodies on the other. After a century of empirical research, it is now poised to finally reinvent itself as a quantitative, genome-based science. Like most biological disciplines, immunology must capitalize on the potentially overwhelming deluge of new data delivered by post-genomic, high throughput technologies; data which is both bewilderingly complex and delivered on a hitherto unimaginable scale.

Theoretical immunology is the application of mathematical modeling to diverse aspects of immunology ranging from T cell selection in the Thymus to the epidemiology of vaccination. Immunoinformatics, the application of computational informatics to the study of immunological macromolecules, addresses important questions in immunobiology and vaccinology. Immunoinformatics, addresses issues of data management, and has the ability to design and implement efficient new experimental strategies. Artificial Immune Systems (AIS) is an area of computer science which uses ideas and concepts from immunology to guide and inspire new algorithms, data structures, and software development. The influence of AIS is now becoming highly synergistic through its interaction with immunoinformatics.

These three different disciplines are now poised to engineer a paradigm shift from hypothesis- to data-driven research, with new understanding emerging from the analysis of complex datasets: theoretical immunology, immunoinformatics, and Artificial Immune Systems (AIS). "in silico Immunology" is a book for the future: it will summarize these emergent disciplines and, while focusing on cutting edge developments, will address the issue of synergy as it shows how these three are set to transform immunological science and the future of health care.

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Immunology is an all important science, addressing, as it does the most pressing medical needs of our time: infectious disease and transplantation medicine. It has given us vaccines on the one hand and therapeutic antibodies on the other. After a century of empirical research, it is now poised to finally reinvent itself as a quantitative, genome-based science. Like most biological disciplines, immunology must capitalize on the potentially overwhelming deluge of new data delivered by post-genomic, high throughput technologies; data which is both bewilderingly complex and delivered on a hitherto unimaginable scale.

Theoretical immunology is the application of mathematical modeling to diverse aspects of immunology ranging from T cell selection in the Thymus to the epidemiology of vaccination. Immunoinformatics, the application of computational informatics to the study of immunological macromolecules, addresses important questions in immunobiology and vaccinology. Immunoinformatics, addresses issues of data management, and has the ability to design and implement efficient new experimental strategies. Artificial Immune Systems (AIS) is an area of computer science which uses ideas and concepts from immunology to guide and inspire new algorithms, data structures, and software development. The influence of AIS is now becoming highly synergistic through its interaction with immunoinformatics.

These three different disciplines are now poised to engineer a paradigm shift from hypothesis- to data-driven research, with new understanding emerging from the analysis of complex datasets: theoretical immunology, immunoinformatics, and Artificial Immune Systems (AIS). "in silico Immunology" is a book for the future: it will summarize these emergent disciplines and, while focusing on cutting edge developments, will address the issue of synergy as it shows how these three are set to transform immunological science and the future of health care.

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Immunology is an all important science, addressing, as it does the most pressing medical needs of our time: infectious disease and transplantation medicine. It has given us vaccines on the one hand and therapeutic antibodies on the other. After a century of empirical research, it is now poised to finally reinvent itself as a quantitative, genome-based science. Like most biological disciplines, immunology must capitalize on the potentially overwhelming deluge of new data delivered by post-genomic, high throughput technologies; data which is both bewilderingly complex and delivered on a hitherto unimaginable scale.

Theoretical immunology is the application of mathematical modeling to diverse aspects of immunology ranging from T cell selection in the Thymus to the epidemiology of vaccination. Immunoinformatics, the application of computational informatics to the study of immunological macromolecules, addresses important questions in immunobiology and vaccinology. Immunoinformatics, addresses issues of data management, and has the ability to design and implement efficient new experimental strategies. Artificial Immune Systems (AIS) is an area of computer science which uses ideas and concepts from immunology to guide and inspire new algorithms, data structures, and software development. The influence of AIS is now becoming highly synergistic through its interaction with immunoinformatics.

These three different disciplines are now poised to engineer a paradigm shift from hypothesis- to data-driven research, with new understanding emerging from the analysis of complex datasets: theoretical immunology, immunoinformatics, and Artificial Immune Systems (AIS). "in silico Immunology" is a book for the future: it will summarize these emergent disciplines and, while focusing on cutting edge developments, will address the issue of synergy as it shows how these three are set to transform immunological science and the future of health care.

Content:
Front Matter....Pages I-XVIII
Overview of the book....Pages 1-8
Front Matter....Pages 9-9
Innate and Adaptive Immunity....Pages 11-21
Immunoinformatics and Computational Vaccinology: A Brief Introduction....Pages 23-46
A Beginners Guide to Artificial Immune Systems....Pages 47-62
Front Matter....Pages 63-63
Computational Models of B cell and T cell Receptors....Pages 65-81
Modelling Immunological Memory....Pages 83-108
Capturing Degeneracy in the Immune System....Pages 109-118
Alternative Inspiration For Artificial Immune Systems: Exploiting Cohen’s Cognitive Immune Model....Pages 119-137
Empirical, AI, and QSAR Approaches to Peptide-MHC Binding Prediction....Pages 139-175
MHC diversity in Individuals and Populations....Pages 177-195
Identifying Major Histocompatibility Complex Supertypes....Pages 197-233
Biomolecular Structure Prediction Using Immune Inspired Algorithms....Pages 235-261
Front Matter....Pages 263-263
Embodiment....Pages 265-288
The Multi-scale Immune Response to Pathogens: M. tuberculosis as an Example....Pages 289-311
Go Dutch: Exploit Interactions and Environments with Artificial Immune Systems....Pages 313-330
Immune Inspired Learning in a Distributed Environment....Pages 331-350
Mathematical Analysis of Artificial Immune System Dynamics and Performance....Pages 351-374
Conceptualizing the Self-Nonself Discrimination by the Vertebrate Immune System....Pages 375-398
Back Matter....Pages 399-450

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Immunology is an all important science, addressing, as it does the most pressing medical needs of our time: infectious disease and transplantation medicine. It has given us vaccines on the one hand and therapeutic antibodies on the other. After a century of empirical research, it is now poised to finally reinvent itself as a quantitative, genome-based science. Like most biological disciplines, immunology must capitalize on the potentially overwhelming deluge of new data delivered by post-genomic, high throughput technologies; data which is both bewilderingly complex and delivered on a hitherto unimaginable scale.

Theoretical immunology is the application of mathematical modeling to diverse aspects of immunology ranging from T cell selection in the Thymus to the epidemiology of vaccination. Immunoinformatics, the application of computational informatics to the study of immunological macromolecules, addresses important questions in immunobiology and vaccinology. Immunoinformatics, addresses issues of data management, and has the ability to design and implement efficient new experimental strategies. Artificial Immune Systems (AIS) is an area of computer science which uses ideas and concepts from immunology to guide and inspire new algorithms, data structures, and software development. The influence of AIS is now becoming highly synergistic through its interaction with immunoinformatics.

These three different disciplines are now poised to engineer a paradigm shift from hypothesis- to data-driven research, with new understanding emerging from the analysis of complex datasets: theoretical immunology, immunoinformatics, and Artificial Immune Systems (AIS). "in silico Immunology" is a book for the future: it will summarize these emergent disciplines and, while focusing on cutting edge developments, will address the issue of synergy as it shows how these three are set to transform immunological science and the future of health care.

Content:
Front Matter....Pages I-XVIII
Overview of the book....Pages 1-8
Front Matter....Pages 9-9
Innate and Adaptive Immunity....Pages 11-21
Immunoinformatics and Computational Vaccinology: A Brief Introduction....Pages 23-46
A Beginners Guide to Artificial Immune Systems....Pages 47-62
Front Matter....Pages 63-63
Computational Models of B cell and T cell Receptors....Pages 65-81
Modelling Immunological Memory....Pages 83-108
Capturing Degeneracy in the Immune System....Pages 109-118
Alternative Inspiration For Artificial Immune Systems: Exploiting Cohen’s Cognitive Immune Model....Pages 119-137
Empirical, AI, and QSAR Approaches to Peptide-MHC Binding Prediction....Pages 139-175
MHC diversity in Individuals and Populations....Pages 177-195
Identifying Major Histocompatibility Complex Supertypes....Pages 197-233
Biomolecular Structure Prediction Using Immune Inspired Algorithms....Pages 235-261
Front Matter....Pages 263-263
Embodiment....Pages 265-288
The Multi-scale Immune Response to Pathogens: M. tuberculosis as an Example....Pages 289-311
Go Dutch: Exploit Interactions and Environments with Artificial Immune Systems....Pages 313-330
Immune Inspired Learning in a Distributed Environment....Pages 331-350
Mathematical Analysis of Artificial Immune System Dynamics and Performance....Pages 351-374
Conceptualizing the Self-Nonself Discrimination by the Vertebrate Immune System....Pages 375-398
Back Matter....Pages 399-450
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