Tsouka, SofiaSavoglidis, GeorgiosRiezman, HowardHatzimanikatis, Vassily2017-10-032017-10-032017-10-032017https://infoscience.epfl.ch/handle/20.500.14299/140975Lipids play 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. We have thus developed a detailed model of the lipid metabolism for the yeast S. cerevisiae, in order to identify how the stoichiometric and kinetic coupling determines lipid homeostasis and its regulation. The model encompasses 308 reactions (of which 230 are enzymatic reactions, 32 are transport reactions and 29 are elementary reactions that contribute to biomass formation) and 212 unique metabolites, and includes the following subsystems: glycolysis, fatty acid biosynthesis and elongation, biosynthesis of phospholipids, sphingolipids, cardiolipin and sterols, triacylglycerides decomposition and the mevalonate pathway. 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. We computed and analyzed the distributions of these models’ flux and concentration control coefficients (FCCs and CCCs, respectively), which quantify the magnitude to which a change in a system parameter (i.e. enzyme activities) will affect and control fluxes through reactions and metabolic concentrations at a representative steady state. We used these coefficients to reverse engineer changes in enzyme activities that will lead to desired phenotypes as well as to identify mutations based on lipidomic measurements.text::conference output::conference poster not in proceedings