International Society of Computers and Their Applications (ISCA)
Abstract
Leishmaniasis is a virulent parasitic infection that causes a significant threat to human health worldwide. The existing drugs are becoming less effective due to the ability of Leishmania spp. to alter its metabolism to adapt to harsh environments. Understanding how this parasite manipulates its metabolism inside the host (e.g. sandfly and human) might underpin new ways to prevent the disease and develop effective treatment strategies. Despite significant advances in omics technologies, biochemistry of parasites still lacks the understanding of molecular components that determine the metabolic behavior under varying conditions. Metabolic network modeling might be of interest to identify physiologically relevant nodes in a metabolic network. The present work proposes a metabolic model iSK570 (an extension of the iAC560 model) with additional reactions for the metabolism of lipids, long chain fatty acids and carbohydrates to study the metabolic behavior of this parasite. Gene Inactivity Moderated by Metabolism and Expression (GIMME) algorithm was used to verify the consistency between model flux predictions and gene expression data. Improved flux distributions were obtained, allowing a more accurate understanding of stage-specific metabolism in of promastigotes and amastigotes.This work was supported by the Initial Training Network, GlycoPar, funded by the FP7 Marie Curie Actions of the European Commission (FP7-PEOPLE-2013-ITN-608295). The authors gratefully express appreciation to SilicoLife Lda for providing required infrastructural facilities related to this work. We also thank Bruno Pereira (systems biologist at SilicoLife) and Hugo Giesteira (programmer at SilicoLife) for scientific and technical assistance during various phases of the project.info:eu-repo/semantics/publishedVersio
