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Title In silico immunology / edited by Darren Flower and Jon Timmis.
Imprint New York : Springer, 2007.

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Series Lecture notes in mathematics (Springer-Verlag) ; 1898.
Subject Immunoinformatics.
Artificial immune systems.
Computational Biology.
Immune System.
Models, Biological.
Models, Theoretical.
Alt Name Flower, Darren R.
Timmis, Jonathan, 1970-
Description 1 online resource (xviii, 450 pages) : illustrations
Bibliography Note Includes bibliographical references ([399]-446) and index.
Summary Theoretical immunology is the application of mathematical modeling to diverse aspects of immunology. Immuno informatics, the application of computational informatics to the study of immunological macromolecules, addresses questions in immunobiology and vaccinology, as well as addressing issues of data management, and can design and implement new experimental strategies. Artificial Immune Systems (AIS) uses ideas and concepts from immunology to guide and inspire new algorithms, data structures, and software development. 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" will summarize these emergent disciplines and will address the issue of synergy as it shows how these three are set to transform immunological science and the future of health care.
Contents Overview of the book -- Overview of the book -- Introducing In Silico Immunology -- Innate and Adaptive Immunity -- Immunoinformatics and Computational Vaccinology: A Brief Introduction -- A Beginners Guide to Artificial Immune Systems -- 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 -- 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 Artificial Immune System Dynamics and Performance -- Conceptualizing the Self-Nonself Discrimination by the Vertebrate Immune System.
Note English.
Print version record.
ISBN 9780387392417
0387392386 (Cloth)
9780387392387 (Cloth)
ISBN/ISSN 10.1007/978-0-387-39241-7
OCLC # 186549167
Additional Format Print version: In silico immunology. New York : Springer, 2007 9780387392387 0387392386 (DLC) 2006931791 (OCoLC)78203300