Developing a Reconciled Genome-Scale Model of the Malaria Parasite Plasmodium falciparum and its Thermodynamics-based Flux Balance Analysis

Understanding the metabolism of the malaria parasites is of high priority to identify novel enzymatic drug targets and face the rise in drug resistant parasites. Based on the knowledge of metabolic functions and molecular interactions, we can reconstruct genome-scale models (GEMs) to study the metabolic behavior of the organism as a whole. In this work, we have developed a reconciled GEM of Plasmodium falciparum, the deadliest of the malaria parasites. To accomplish this, we firstly reconstructed the metabolic network of P. falciparum using the RAVEN toolbox. Then, we curated the biochemistry of the metabolic network by integrating information from two previously published GEMs (Plata et al. and by Hutmacher et al., 2010) and from the experimental characterization of enzymatic activity in the malaria parasites. Using a gap-filling approach, we hypothesized which enzymatic reactions are required to fulfill certain metabolic capabilities, such as anaerobic lactate production from glucose. We then added thermodynamic constraints and three sets of metabolomics data, which were measured during infection of the red blood cells, and we performed Thermodynamics-based Flux Balance Analysis. With our in silico approach, we have identified enzymes that are essential for the proliferation of the parasites at the blood stage. Some of these enzymes were reported to be essential in the literature, but previous computational studies had failed to identify them as essential. Our GEM of P. falciparum is a valuable tool for understanding the metabolism of the malaria parasite, for the integration of omics data, and for the identification of novel antimalarial drug targets.

Hatzimanikatis, Vassily
Soldati-Favre, Dominique
Presented at:
4th Conference on Constraint-Based Reconstruction and Analysis, Heidelberg, Germany, September 16-18, 2015

 Record created 2015-10-14, last modified 2018-12-21

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