Main description:

This book outlines three emergent disciplines, which are now poised to engineer a paradigm shift from hypothesis- to data-driven research: theoretical immunology, immunoinformatics, and Artificial Immune Systems. It details how these disciplines will enable new understanding to emerge from the analysis of complex datasets. Coverage shows how these three are set to transform immunological science and the future of health care.

Contents:

Preface.- List of Contributors.- Overview.- Innate and Adaptive Immunity.- Part I Introducing In Silico Immunology: Immunoinformatics and Computational Vaccinology: A Brief Introduction.- A Beginners Guide to Artificial Immune Systems.- Part II The Nature of Natural and Artificial Immune Systems: Computational Models of B Cell and T Cell Receptors.- Modelling Immunological Memory.- Capturing Degeneracy in the Immune System.- Alternative Inspiration for Artificial Immune Systems: Exploiting Cohen's Cognitive Immune Model.- Empirical, AI, and QSAR Approaches to Peptide-MHC Binding Prediction.- MHC Diversity in Individuals and Populations.- Identifying Major Histocompatibility Complex Supertypes.- Biomolecular Structure Prediction Using Immune Inspired Algorithms.- Part III How Natural and Artificial Immune Systems Interact with the World: Embodiment.- The Multi-Scale Immune Response to Pathogens: M. Tuberculosis as an Example.- Go Dutch: Exploit Interactions and Environments with Artificial Immune Systems.- Immune Inspired Learning in a Distributed Environment.- Mathematical Analysis of AIS Dynamics and Performance.- Conceptualizing the Self-Nonself Discrimination by the Vertebrate Immune System.- References.- Index.