000231785 001__ 231785
000231785 005__ 20190118053639.0
000231785 037__ $$aPOST_TALK 000231785 245__$$aToward the identification of cellular mechanisms behind the lethal phenotypes in malaria parasites blood stages with PlasmoGEM and metabolic modeling
000231785 269__ $$a2017 000231785 260__$$c2017
000231785 336__ $$aTalks 000231785 520__$$aRecent advances in cell genome editing techniques enable the generation of high-throughput gene knockout data in the malaria parasites in vivo. Integrative analysis of this data can lead to the identification of biological mechanisms that explain the observed phenotypes and that provide testable hypotheses for further discoveries. Metabolic modelling can cope with the tangled and versatile metabolism of the malaria parasites, and hence is a valuable approach for understanding the parasites physiology. In this study, we present a combined experimental and computational study that suggests cellular mechanisms behind experimentally determined lethal phenotypes. We perform computational analyses on a newly developed genome-scale model of the malaria parasite Plasmodium berghei (iPbe), and we use high-throughput gene knockout data generated in the PlasmoGEM project. The comparison between data and gene essentiality predictions allow the understanding of the parasite’s physiology in the blood stages. When we simulate in iPbe the hypothesised physiology, we achieve an 80% consistency between the prediction of essential genes and the experimental data. We further predict in silico and test in vivo synthetic lethal pairs in the blood stages of the malaria infection. We identify the thermodynamic bottlenecks, genetic interactions, and the accessibility to nutrients behind the phenotypes. This result indicates that our model iPbe is a valuable framework for the generation of testable hypothesis. Overall, the knowledge generated in this experimental and computational framework will serve to tackle more efficiently the malaria parasites’ metabolism during infection.
000231785 6531_ $$ahigh-throughput gene knockout data 000231785 6531_$$aPlasmodium
000231785 6531_ $$aphenotypes 000231785 6531_$$amodeling
000231785 6531_ $$aphysiology 000231785 700__$$0247949$$g239558$$aChiappino Pepe, Anush
000231785 700__ $$aBushell, Ellen 000231785 700__$$aRayner, Julian C.
000231785 700__ $$aBillker, Oliver 000231785 700__$$0240657$$g174688$$aHatzimanikatis, Vassily
000231785 7112_ $$dJuly 16-20, 2017$$cNewport Beach, CA, USA$$aBiochemical and Molecular Engineering XX 000231785 909C0$$xU11422$$0252131$$pLCSB
000231785 909CO $$ppresentation$$pSB$$ooai:infoscience.tind.io:231785 000231785 917Z8$$x239558
000231785 917Z8 $$x239558 000231785 937__$$aEPFL-TALK-231785
000231785 973__ $$aEPFL 000231785 980__$$aTALK