Computational exploration of antiviral strategies against alphaviruses and flaviviruses : a metabolic modelling study

Current and reoccurring viral epidemic outbreaks such as those caused by Zika virus illustrate the need for rapid development of antivirals. Such development would be facilitated by computational approaches that can provide experimentally testable predictions for possible antiviral strategies. To this end, here the focus is on the fact that viruses are directly dependent on their host metabolism for reproduction. This thesis develops a set of stoichiometric, genome-scale metabolic models that integrates human macrophage cell metabolism with the biochemical demands arising from virus production and use it to virus impact on host metabolism and vice versa. While this approach applies to any host-virus pair, this project focuses on first applying it to currently epidemic viruses: Chikungunya, Dengue and Zika. Overall, it is found that each of these viruses causes specific alterations in the host metabolic flux towards fulfilling their biochemical demands as predicted by their genome and capsid structure. It is predicted that all three viruses utilise the host metabolic network in a different manner than that of the host, upregulating the areas of the network that are associated with the biosynthesis of their biomass components whilst downregulating areas that are not. Subsequent analysis of this integrated model allows the prediction a set of host reactions, which when constrained inhibit virus production. These prediction recovers most of the known targets of existing metabolism-orientated antiviral drugs while highlighting a set of hitherto unexplored reactions with either broad or virus-specific antiviral potential. To further probe how these reactions can be perturbed to inhibit virus production, the methodology created in this thesis is expanded from single-reactions to combinations of pairs of reactions, known as the double-reaction analysis. These predictions expand the novel repertoire of antiviral targets against Chikungunya, Dengue and Zika virus, and are combined with candidate drug compounds (identified from online databases) for experimental validation and implementation. This project demonstrates that, with a combination of the application of flux balance analysis and development of a novel, integrated computational platform, the viral infection process can be better understood and potential antiviral targets predicted. The insights gained are of significant relevance, not only to the understanding of Chikungunya, Dengue and Zika viruses, but also to the overall antiviral development biotechnology cycle.