Lipids, as vital eukaryotic cellular components, play a major role in cell, tissue and organ physiology. Alterations of lipid enzymes and metabolic pathways are related to many human diseases, such as cancer, diabetes and neurodegenerative diseases. In this study, we focus on phospholipids, the major components of all cell membranes, which not only act as structural membrane constituents but also play a vital role in cellular energy storage and in signaling pathways. The yeast, Saccharomyces cerevisiae, has been chosen as a cell model, due to its strong homology of proteins and pathways with higher eukaryotes. The benefits of studying the glycerophospholipid metabolism of S. cerevisiae will provide critical insight into phospholipid homeostasis of this model organism in various cellular states. The reconstructed model of phospholipid metabolism consists of 118 reactions and 80 metabolites. Beyond combining knowledge of all published yeast genome-scale models, it incorporates missing reactions reported in the literature along with the current state of the art concerning the participating enzymes. Pathways regarding compounds from the glycolysis pathway and synthesis of acyl-CoAs, required for phospholipid biosynthesis, have also been included. A steady state flux analysis was performed with the growth capacity of the cell being able to vary between maintenance and a maximum experimentally observed growth rate, in aerobic carbon-limiting conditions, with mass balance constraints calculated based on experimental data reported in the most recent yeast genome scale model. A thermodynamic curation of all the reactions of the pathway followed, which constrains the allowed space of the concentration ranges of metabolites of the pathway and also provides information about the directionality of all the reactions. Finally, we applied the ORACLE methodology in order to identify enzymes that determine the lipid composition in the various steady states of the cell. Generating a comprehensive kinetic model will be a useful tool for predictive analysis, drug designing or further biomedical research.