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Title: Improved computational model of the malaria metabolic network and flux analysis for drug target prediction
Author: Totanes, Francis Isidore Garcia
ISNI:       0000 0004 6351 712X
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2017
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In recent years, genome scale metabolic models have become an important tool for identifying potential drug targets against pathogens. These are particularly important where cultivation and genetic manipulation (conditional knockouts) are difficult. Malaria is a globally important disease infecting 212 million cases and causing more than 400,000 deaths in 2015. The resistance of the parasite to all antimalarial drugs on the market emphasises the urgent need to identify new drug targets. There are a few malaria metabolic models that have already been developed; however, these models are limited in terms of network size or input of accurate experimentally derived metabolomics and biomass data. With extensive curation and utilisation of parasite-specific constraints in the improvement of existing metabolic network models, a highly curated metabolic network model of Plasmodium falciparum, iFT342, was developed here. The model has updated gene and reaction annotations as well as additional species identifiers that will facilitate ease in comparison with other models. The model has no dead-end metabolites (compared to 5 to 39% for other highly curated models) and has the highest percentage of live reactions. With the addition of experimentally measured biomass composition and metabolite fluxes for glucose and 18 amino acids, iFT342 was able to model in vitro parasite growth in restricted glucose environment with remarkable fidelity. In addition, through single gene knockout analysis, the model was able to significantly enrich the number of experimentally validated essential genes (true positives) in the predicted essential gene set, and had the highest percentage of true positive predictions compared with other malaria models. Finally, as proof of concept, inhibition of parasite growth was demonstrated using gemcitabine, which targets UMP-CMP kinase, a novel target predicted by the model. Gemcitabine inhibited parasite growth in a dose-dependent fashion exhibiting an IC50 in the low micromolar range and blocked the development of the parasite from the trophozoite to the schizont stage.
Supervisor: McConkey, Glenn A. ; Westhead, David R. Sponsor: Marie Skłodowska Curie Initial Training Network
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available