Lipids carry a very important role in cell structure and function, as well as in the physiopathology of many diseases. Maintenance of the lipid profiles should be tightly regulated as it is very important for preserving membrane permeability, cell integrity and several other functions. Large-scale kinetic models of metabolic networks are essential in order to accurately capture and predict such behaviors of cellular systems when subject to perturbations. To this end, we developed a detailed model of the lipid metabolism, in order to identify how the stoichiometric and kinetic coupling determines lipid homeostasis and its regulation. We have created a comprehensive model of the lipids network of yeast, based on the genome-scale metabolic model of S. cerevisiae. 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 metabolic model encompasses 843 reactions and 571 metabolites across 4 cellular compartments (cytosol, mitochondria, peroxisomes and endoplasmic reticulum), 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 and the mevalonate pathway. It also includes several key parts of yeast metabolism such as glycolysis, citric acid cycle, oxidative phosphorylation etc. Having computed the distributions of the computed kinetic models' parameters, we constructed the dynamic mass balances of the species. The model can be used to simulate the dynamic evolution of concentration profiles in response to small perturbations of enzyme activities, as well as to identify the enzymes that control the distributions of fluxes through reactions and metabolic concentrations at a representative steady state. Given a particular metabolic phenotype, we can further use this analysis to identify the changes in specific enzyme activities that are responsible for this particular phenotype.