Computational immunology

In academia, computational immunology is a field of science that encompasses high-throughput genomic and bioinformatics approaches to immunology. The field's main aim is to convert immunological data into computational problems, solve these problems using mathematical and computational approaches and then convert these results into immunologically meaningful interpretations.The immune system is a complex system of the human body and understanding it is one of the most challenging topics in biology. Immunology research is important for understanding the mechanisms underlying the defense of human body and to develop drugs for immunological diseases and maintain health. Recent findings in genomic and proteomic technologies have transformed the immunology research drastically. Sequencing of the human and other model organism genomes has produced increasingly large volumes of data relevant to immunology research and at the same time huge amounts of functional and clinical data are being reported in the scientific literature and stored in clinical records. Recent advances in bioinformatics or computational biology were helpful to understand and organize these large scale data and gave rise to new area that is called Computational immunology or immunoinformatics.Computational immunology began over 90 years ago with the theoretic modeling of malaria epidemiology. At that time, the emphasis was on the use of mathematics to guide the study of disease transmission. Since then, the field has expanded to cover all other aspects of immune system processes and diseases.After the recent advances in sequencing and proteomics technology, there have been many fold increase in generation of molecular and immunological data. The data are so diverse that they can be categorized in different databases according to their use in the research. Until now there are total 31 different immunological databases noted in the Nucleic Acids Research (NAR) Database Collection, which are given in the following table, together with some more immune related databases. The information given in the table is taken from the database descriptions in NAR Database Collection.A variety of computational, mathematical and statistical methods are available and reported. These tools are helpful for collection, analysis, and interpretation of immunological data. They include text mining, information management, sequence analysis, analysis of molecular interactions, and mathematical models that enable advanced simulations of immune system and immunological processes. Attempts are being made for the extraction of interesting and complex patterns from non-structured text documents in the immunological domain. Such as categorization of allergen cross-reactivity information, identification of cancer-associated gene variants and the classification of immune epitopes.Allergies, while a critical subject of immunology, also vary considerably among individuals and sometimes even among genetically similar individuals. The assessment of protein allergenic potential focuses on three main aspects: (i) immunogenicity; (ii) cross-reactivity; and (iii) clinical symptoms. Immunogenicity is due to responses of an IgE antibody-producing B cell and/or of a T cell to a particular allergen. Therefore, immunogenicity studies focus mainly on identifying recognition sites of B-cells and T-cells for allergens. The three-dimensional structural properties of allergens control their allergenicity.