Recent 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.