Computational modelling approaches to vaccinology

Abstract

Excepting the Peripheral and Central Nervous Systems, the Immune System is the most complex of somatic systems in higher animals. This complexity manifests itself at many levels from the molecular to that of the whole organism. Much insight into this confounding complexity can be gained through computational simulation. Such simulations range in application from epitope prediction through to the modelling of vaccination strategies. In this review, we evaluate selectively various key applications relevant to computational vaccinology: these include technique that operates at different scale that is, from molecular to organisms and even to population level.

Publication DOI: https://doi.org/10.1016/j.phrs.2014.08.006
Divisions: College of Health & Life Sciences > Aston Pharmacy School
College of Health & Life Sciences
College of Health & Life Sciences > Chronic and Communicable Conditions
Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Pharmacological research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pappalardo, F, Flower, D, Russo, G, Pennisi, M & Motta, S, 'Computational modelling approaches to vaccinology' Pharmacological research, vol 92 (2015) DOI: http://dx.doi.org/10.1016/j.phrs.2014.08.006
Uncontrolled Keywords: computational vaccinology,epitopes,immune system,modelling,simulations,vaccine research,Pharmacology
Publication ISSN: 1096-1186
Last Modified: 04 Nov 2024 08:43
Date Deposited: 10 Mar 2015 15:10
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2015-02
Published Online Date: 2014-09-16
Authors: Pappalardo, Francesco
Flower, Darren (ORCID Profile 0000-0002-8542-7067)
Russo, Giulia
Pennisi, Marzio
Motta, Santo

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Version: Accepted Version


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