Darren R. Flower – författare

“… this book was written from start to finish by one extremely dedicated and erudite individual. The author has done an excellent job of covering the many topics that fall under the umbrella of computational biology for vaccine design, demonstrating an admirable command of subject matter in fields as disparate as object-oriented databases and regulation of T cell response. Simply put, it has just the right breadth and depth, and it reads well. In fact, readability is one of its virtues—making the book enticing and useful, all at once…” Human Vaccines, 2010 "... This book has several strong points. Although there are many textbooks that deal with vaccinology, few attempts have been made to bring together descriptions of vaccines in history, basic bioinformatics, various computational solutions and challenges in vaccinology, detailed experimental methodologies, and cutting-edge technologies... This book may well serve as a first line of reference for all biologists and computer scientists..." –Virology Journal, 2009 Vaccines have probably saved more lives and reduced suffering in a greater number of people than any other medical intervention in human history, succeeding in eradicating smallpox and significantly reducing the mortality and incidence of other diseases. However, with the emergence of diseases such as SARS and the threat of biological warfare, vaccination has once again become a topic of major interest in public health. Vaccinology now has at its disposal an array of post-genomic approaches of great power. None has a more persuasive potential impact than the application of computational informatics to vaccine discovery; the recent expansion in genome data and the parallel increase in cheap computing power have placed the bioinformatics exploration of pathogen genomes centre stage for vaccine researchers. This is the first book to address the area of bioinformatics as applied to rational vaccine design, discussing the ways in which bioinformatics can contribute to improved vaccine development by introducing the subject of harnessing the mathematical and computing power inherent in bioinformatics to the study of vaccinologyputting it into a historical and societal context, and exploring the scope of its methods and applications.Bioinformatics for Vaccinology is a one-stop introduction to computational vaccinology. It will be of particular interest to bioinformaticians with an interest in immunology, as well as to immunologists, and other biologists who need to understand how advances in theoretical and computational immunobiology can transform their working practices.

Pharmaceutical research draws on increasingly complex techniques to solve the challenges of drug design. Bringing together a number of the latest informatics techniques, this book looks at modelling and bioinformatic strategies; structural genomics and X-ray crystallography; virtual screening; lead optimisation; ADME profiling and vaccine design. A number of relevant case studies, focussing on techniques that have demonstrated their use, will concentrate on G-protein coupled receptors as potential disease targets. Providing details of state-of-the-art research, Drug Design: Cutting Edge Approaches will be invaluable to all drug discovery scientists, including medicinal and combinatorial chemists, molecular modellers, bio- and chemoinformaticians, and pharmacologists, amongst others. University and pharmaceutical company libraries will also benefit from having a copy on their shelves.

This volume will address an important emergent area within the field of immunomics: the discovery of antigens and adjuvants within the context of reverse vaccinology. Conventional approaches to vaccine design and development requires pathogens to be cultivated in the laboratory and the immunogenic molecules within them to be identifiable. Conventional vaccinology is no longer universally successful, particularly for recalcitrant pathogens. By using genomic information we can study vaccine development in silico: 'reverse vaccinology', can identify candidate subunits vaccines by identifying antigenic proteins and by using equally rational approaches to identify novel immune response-enhancing adjuvants.

This volume will address an important emergent area within the field of immunomics: the discovery of antigens and adjuvants within the context of reverse vaccinology. Conventional approaches to vaccine design and development requires pathogens to be cultivated in the laboratory and the immunogenic molecules within them to be identifiable. Conventional vaccinology is no longer universally successful, particularly for recalcitrant pathogens. By using genomic information we can study vaccine development in silico: 'reverse vaccinology', can identify candidate subunits vaccines by identifying antigenic proteins and by using equally rational approaches to identify novel immune response-enhancing adjuvants.

Immunoinformatics: Predicting Immunogenicity In Silico is a primer for researchers interested in this emerging and exciting technology and provides examples in the major areas within the field of immunoinformatics. This volume both engages the reader and provides a sound foundation for the use of immunoinformatics techniques in immunology and vaccinology.The volume is conveniently divided into four sections. The first section, Databases, details various immunoinformatic databases, including IMGT/HLA, IPD, and SYEPEITHI. In the second section, Defining HLA Supertypes, authors discuss supertypes of GRID/CPCA and hierarchical clustering methods, Hla-Ad supertypes, MHC supertypes, and Class I Hla Alleles. The third section, Predicting Peptide-MCH Binding, includes discussions of MCH binders, T-Cell epitopes, Class I and II Mouse Major Histocompatibility, and HLA-peptide binding. Within the fourth section, Predicting Other Properties of Immune Systems, investigators outline TAP binding, B-cell epitopes, MHC similarities, and predicting virulence factors of immunological interest.Immunoinformatics: Predicting Immunogenicity In Silico merges skill sets of the lab-based and the computer-based science professional into one easy-to-use, insightful volume.

Immunoinformatics: Predicting Immunogenicity In Silico is a primer for researchers interested in this emerging and exciting technology and provides examples in the major areas within the field of immunoinformatics. This volume both engages the reader and provides a sound foundation for the use of immunoinformatics techniques in immunology and vaccinology.The volume is conveniently divided into four sections. The first section, Databases, details various immunoinformatic databases, including IMGT/HLA, IPD, and SYEPEITHI. In the second section, Defining HLA Supertypes, authors discuss supertypes of GRID/CPCA and hierarchical clustering methods, Hla-Ad supertypes, MHC supertypes, and Class I Hla Alleles. The third section, Predicting Peptide-MCH Binding, includes discussions of MCH binders, T-Cell epitopes, Class I and II Mouse Major Histocompatibility, and HLA-peptide binding. Within the fourth section, Predicting Other Properties of Immune Systems, investigators outline TAP binding, B-cell epitopes, MHC similarities, and predicting virulence factors of immunological interest.Immunoinformatics: Predicting Immunogenicity In Silico merges skill sets of the lab-based and the computer-based science professional into one easy-to-use, insightful volume.