Computational immunology is a field of science that combines mathematical, statistical, data analytics, and modeling approaches to immunology. The glut of data produced by high-throutput instrumentation, notably genomics, transcriptomics, epigenetics, and proteomics methods, require computational tools for acquisition, storage, and analysis of immunological data. To be able to analyze immunological data, we must convert it into computational problems, solve these problems using mathematical and computational approaches, and then translate these results into immunologically meaningful interpretations.

Immunology has embraced data-driven computational and mathematical approaches to help understand the immune system, its disorders, and interaction with infectious agents. Computational immunology covers a broad range of applications. Mathematical models have long been used for modeling immune responses, as well as molecular and cellular interactions, progression of infection, tumor growth, and host-pathogen interactions. The applied arm of computational immunolygy, immunoinformatics, is dedicated to development and application of computational methods and tools for the analyses of immunological data and knowledge extraction using statistical inference and machine learning algorithms. Computational immunology is a key driver that enhances basic and translational immunology by empowering data-driven research.

This special issue intends to collect contributions from immunologists along with mathematicians, bioinformaticians, computational scientists, and engineers to present and discuss latest developments in different subareas of computational immunology, ranging from databases applications to computational vaccine design, modeling, and simulation and their application to basic and clinical immunology.

Potential topics include, but are not limited to:

• Data analytics
• Databases
• Predictive systems
• Mathematical models of the immune system
• High-performance computing in immunology
• Computational vaccine design
• Design of immunotherapies
• Diagnostics in cancer immunology
• Proteogenomics
• Epigenetics