Advances in Computational Immunology.

Francesco Pappalardo,Marzio Pennisi,Guanglan Zhang,Vladimir Brusic

doi:10.1155/2015/170920

Abstract

Computational immunology and immunological bioinformatics are firm and quickly growing research fields. Whereas the former aims to develop mathematical and/or computational methods to study the dynamics of cellular and molecular entities during the immune response [1–4], the latter focuses on proposing methods to investigate big genomic and proteomic immunological-related datasets and predict new knowledge mainly by statistical inference and machine learning algorithms.

Highlights

The exploitation of such a huge amount of immunological data usually requires its conversion into computational problems, their solution using mathematical and computational approaches, and the translation of the obtained results into immunologically meaningful interpretations
We take an interest from mathematicians, bioinformaticians, computational scientists, and engineers together with experimental immunologists to present and discuss latest developments in different subareas of computational immunology, ranging from databases applications to computational vaccine design, modelling, and simulation and their application to basic and clinical immunology
Sepulveda et al calls attention to serology data in conjunction with mathematical modelling in providing a powerful approach to inform on malaria transmission intensity and putative changes over time. Their conclusions show that an interesting idea with public health potential is to use a panel of multidisease antibodies that can be instrumental to know what the infectious agents are in circulation in a given population and their putative dynamics

Summary

Introduction

The exploitation of such a huge amount of immunological data usually requires its conversion into computational problems, their solution using mathematical and computational approaches, and the translation of the obtained results into immunologically meaningful interpretations. The glut of data produced by high-throughput instrumentation, notably genomics, transcriptomics, epigenetics, and proteomics methods, requires computational tools for acquisition, storage, and analysis of immunological data. This idea has not been tested in practice, but definitely will require the extension of classical mathematical models to fully account the immunological interaction between different diseases.

Results

Conclusion