Hatzimanikatis, VassilyChiappino-Pepe, Anush2018-09-242018-09-242018-09-24201810.5075/epfl-thesis-8602https://infoscience.epfl.ch/handle/20.500.14299/148552Malaria puts at risk of infection half of the world population and still kills around half a million people every year. The rapid and worrisome rise of drug-resistant malaria parasites hinders the treatment of malaria and calls for a global collaboration to eradicate this disease. The technological advances have allowed sequencing the genome and measuring metabolites, proteins, and mRNAs of the parasites at a high-throughput, which has provided insights into the biological processes of these organisms. However, many aspects of the biology of the malaria parasites remain hidden and hinder the development of effective and selective antimalarial therapies. The understanding of the cell function as a whole rather than the study of its components in isolation will enable the development of strategies to engineer and target the cell function. Only computational tools can provide a comprehensive understanding of the cellular function in different contexts and upon perturbation. Moreover, computational methods provide testable hypotheses that can guide and accelerate experimental discoveries. Metabolism is to date the most characterized biological process in the cell and is an attractive source of drug targets. Metabolic networks encompass all available information on the biochemistry of an organism and represent an attractive scaffold to integrate omics data and analyze complex metabolic processes. In this thesis, we present the metabolic models of two malaria parasites, i.e., Plasmodium falciparum and Plasmodium berghei, and a set of mathematical and computational methods for the comprehensive and unbiased analysis of metabolic networks. We focus on the bioenergetics-based and metabolic control analysis of the malaria parasitesâ metabolism to find drug targets and investigate drug action mechanisms. Firstly, we develop computational methods that follow principles from network thermodynamics to identify essential metabolic functions, thermodynamic bottlenecks, and nutritional requirements of P. falciparum. Secondly, we define a mathematical framework for Phenotype Mapping (PhenoMapping), which associates a cellular phenotype with the underlying cellular processes and conditions. We applied PhenoMapping to investigate the PlasmoGEM phenotypes obtained for P. berghei in the blood stages of its life cycle. This analysis enables the identification of non-conditionally and conditionally essential metabolic functions and the development of context-specific metabolic models. Thirdly, we present a Mixed Integer Synthetic Lethal Enumeration (MISyLE) method for the optimal identification of essential and all redundant (synthetic lethal) sets of reactions and genes for a cellular phenotype. Fourthly, we investigate the effect of antimalarial-based therapies with one or multiple antifolates in the metabolism of P. falciparum in the blood stages, and we examine new drug targeting strategies that might maximize the impact on the intraerythrocytic development of the parasites. Fifthly, we discuss a systems biology approach to accelerate our understanding of biological systems based on the disagreements between in silico and in vivo observations. Finally, we suggest strategies to use the models and computational frameworks developed in this thesis to further investigate host-pathogen interactions, drug action mechanisms, and dormancy in the malaria parasites and other organisms.enmalaria parasitesmetabolic modelingphenotypic dataconditional essentialityMILPTFAMCAthermodynamicssynthetic lethalitydrug action mechanismsthesis::doctoral thesis