The Resource In Silico Immunology, edited by Darren D.R. Flower, Jon Timmis, (electronic resource)

In Silico Immunology, edited by Darren D.R. Flower, Jon Timmis, (electronic resource)

Label
In Silico Immunology
Title
In Silico Immunology
Statement of responsibility
edited by Darren D.R. Flower, Jon Timmis
Contributor
Editor
Editor
Subject
Language
  • eng
  • eng
Summary
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
Dewey number
  • 616.07/90285
  • 616.0790285
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsedt
  • IKvdN0X7lbw
  • baKtqXgfl6E
Language note
English
LC call number
QR180-189.5
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Flower, Darren D.R.
  • Timmis, Jon.
http://library.link/vocab/subjectName
  • Immunology
  • Bioinformatics
  • Microbial genetics
  • Microbial genomics
  • Proteomics
  • Physiology
  • Immunology
  • Bioinformatics
  • Microbial Genetics and Genomics
  • Proteomics
  • Physiological, Cellular and Medical Topics
  • Mathematical and Computational Biology
Label
In Silico Immunology, edited by Darren D.R. Flower, Jon Timmis, (electronic resource)
Instantiates
Publication
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references ([399]-446) and index
Carrier category
online resource
Carrier category code
cr
Content category
text
Content type code
txt
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
Dimensions
unknown
Edition
1st ed. 2007.
Extent
1 online resource (452 p.)
Form of item
online
Isbn
9780387392417
Media category
computer
Media type code
c
Other control number
10.1007/978-0-387-39241-7
Specific material designation
remote
System control number
  • (CKB)1000000000284620
  • (EBL)372034
  • (OCoLC)828801163
  • (SSID)ssj0000299458
  • (PQKBManifestationID)11229244
  • (PQKBTitleCode)TC0000299458
  • (PQKBWorkID)10242625
  • (PQKB)11480577
  • (SSID)ssj0000770895
  • (PQKBManifestationID)12310852
  • (PQKBTitleCode)TC0000770895
  • (PQKBWorkID)10792153
  • (PQKB)11704128
  • (DE-He213)978-0-387-39241-7
  • (MiAaPQ)EBC372034
  • (EXLCZ)991000000000284620
Label
In Silico Immunology, edited by Darren D.R. Flower, Jon Timmis, (electronic resource)
Publication
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references ([399]-446) and index
Carrier category
online resource
Carrier category code
cr
Content category
text
Content type code
txt
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
Dimensions
unknown
Edition
1st ed. 2007.
Extent
1 online resource (452 p.)
Form of item
online
Isbn
9780387392417
Media category
computer
Media type code
c
Other control number
10.1007/978-0-387-39241-7
Specific material designation
remote
System control number
  • (CKB)1000000000284620
  • (EBL)372034
  • (OCoLC)828801163
  • (SSID)ssj0000299458
  • (PQKBManifestationID)11229244
  • (PQKBTitleCode)TC0000299458
  • (PQKBWorkID)10242625
  • (PQKB)11480577
  • (SSID)ssj0000770895
  • (PQKBManifestationID)12310852
  • (PQKBTitleCode)TC0000770895
  • (PQKBWorkID)10792153
  • (PQKB)11704128
  • (DE-He213)978-0-387-39241-7
  • (MiAaPQ)EBC372034
  • (EXLCZ)991000000000284620

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