What is computational immunology?

Computational immunology is an interdisciplinary field that combines the principles of computer science and immunology to develop algorithms and computational models for studying the immune system. The aim of computational immunology is to gain insights into the underlying mechanisms and processes of the immune system, which can then be used in the development of new drugs, vaccines, and immunotherapies.

Techniques used in computational immunology include data mining, machine learning, bioinformatics, and systems biology. The data used in these techniques includes information from molecular and cellular processes, genomics, proteomics, and immunophenotyping. Computational models may be used for analyzing complex immune system interactions, predicting immunological responses to vaccines or drugs, and evaluating personalized treatment options.

Applications of computational immunology include the study of immune system disorders such as autoimmune diseases, infectious diseases, cancer immunology, and the identification of new drug targets. Computational immunology is also used to predict immune responses to potential pandemics, such as the COVID-19 pandemic, which may help to develop appropriate public health responses.