In Silico Immunology

ISBN-13: 9780387392387 / Angielski / Twarda / 2006 / 451 str.

Darren R. Flower; Jon Timmis

Opis

Whatever its final readership and impact, we, the Editors, feel this book is im- portant. It addresses the realisation that there is a deep and abiding synergy, albeit one only now being properly explored and exploited, between immunol- ogy and computational science. This area of intersection we christen in silico immunology. Immunology is an inspiration for computational scientists seek- ing practical and philosophical metaphors for their work; but, at the same time, it is itself a biological discipline of such discombobulating complexity that only computational help as different as simulation and data warehousing can make its modern study tractable. Thus immunology both inspires but also requires computational science. This book deals in detail with the three main areas of in silico immunology: theoretical immunology, immunoinformatics, and artificial immune systems. While all of these are now well-established the interactions between the three are only beginning to be developed. It is a truly exciting time to be working in in silicio immunology. We are reaching a critical mass that will enable great strides to be taken and significant achievements to be made. Like David Hume, we may yet come to regret that this book falls still born from the press but we hope not. Hopefully it will instead strike a cord and tap into a burgeoning Zeitgeist ready to capitalise on the remarkable potential that is in silico immunology.

Spis treści

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.

Blurb

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.