Lipids are major constituents of the cell, implicated with its functional and structural properties, along with a number of chronic diseases. Applying computational techniques with systematic modeling of lipids can provide insights about mechanisms that regulate lipid synthesis and guide biomedical research. In the present study we developed a comprehensive kinetic model of the sphingolipid biosynthesis in the yeast Saccharomyces cerevisiae. We first constructed a stoichiometric model that contains all the known reactions for the biosynthesis of ceramides and complex sphingolipids. We next designed and constructed kinetic model populations. We used lipidomic measurements of wild type yeast to consistently calibrate our model. Curation of the kinetic information of the model came from the comprehensive mining of references for operation of the enzymes as well as ranges of kinetic parameters from online databases. We performed a thorough kinetic analysis of the system by examining the impact of different assumptions in enzyme operation on the levels of ceramides and complex sphingolipids (e.g. multiple competitive inhibitions). By applying the principles of Metabolic Control Analysis (MCA) we were able to quantify the effect of enzyme activities on the synthesized lipid profiles. Finaly, we developed and applied an algorithm based on the inverse MCA coefficients, which we call iMCA. iMCA can perform identification of genetic mutations by exploring the information contained within lipidomic measurements and the kinetic representation of the metabolic network. We demonstrate the predictive capability of the method by comparing the calculated changes in enzyme levels with protein expression data of mutant strains.