Lipids carry an important role in cell structure and function, as well as in the physiopathology of multiple diseases. Maintenance of the lipid profiles should be tightly regulated for preserving membrane permeability, cell integrity and several other functions. Large-scale kinetic models of metabolic networks are essential in order to capture and predict such behaviors of cellular systems when subject to perturbations. To this end, we developed a detailed model of the lipid metabolism, based on genome-scale metabolic models of S. cerevisiae, in order to identify how the stoichiometric and kinetic coupling determines lipid homeostasis and its regulation. We curated this model using thermodynamic data as well as lipidomic measurements and we used the Optimization and Risk Analysis of Complex Living Entities (ORACLE) framework to generate populations of parametrized kinetic models that are consistent with the given physiology, while satisfying the stoichiometric and thermodynamic constraints and accounting for the parametric uncertainty. The model encompasses 1143 reactions and 799 metabolites across 6 cellular compartments (cytosol, mitochondria, peroxisomes, endoplasmic reticulum, Golgi and nucleus), and includes the following lipid-related subsystems: biosynthesis, elongation, and degradation of fatty acids, biosynthesis and esterification of sterols, biosynthesis of phospholipids, sphingolipids, and cardiolipin, triacylglyceride decomposition, dolichol biosynthesis and the mevalonate pathway. It also includes several key parts of yeast metabolism such as glycolysis, citric acid cycle, oxidative phosphorylation etc. Using the distributions of the computed kinetic models' parameters, we constructed the dynamic mass balances of the species, in order to simulate the dynamic evolution of concentration profiles in response to small perturbations of enzyme activities, and to identify the enzymes that control the distributions of fluxes and metabolic concentrations at a representative steady state. We can further use this analysis to identify the changes in specific enzyme activities that are responsible for given mutant phenotypes.