Infoscience

Poster

Kinetic modeling of the yeast sphingolipid metabolism identifies prevalent mutant strains through integration of lipidomic data profiles

Sphingolipids are abundant components of eukaryotic cells. Their localization in the plasma membrane allows for the cell to carry out multiple important functions for its viability. One of the most important roles of the sphingolipids is their ability to form, along with other lipid compounds, complex structures which attribute to the membrane of the cell its functional role to selectively allow transport of molecules, participate in signaling, react under heat stress and regulate growth. In yeast multiple complex sphingolipids have been identified based on the localization of the head group on the long chain base. Recent evidence points to the fact that alterations in the sphingolipids levels cause numerous diseases such as infections, diabetes, Alzheimer’s disease and various types of cancer. In the present study we developed a mathematical model of the sphingolipid biosynthesis in Saccharomyces cerevisiae. The model accounts for all the different complex sphingolipids formed, according to the various hydroxylation states, as reported in recent studies. Biochemical and kinetic information integrated in the model are implemented from the literature. The resulting kinetic model is able to generate dynamic and steady state phase profiles of the all the species of the network. These results are in agreement with experiments performed; where radioactively labeled palmitate and inositol are used to quantify the metabolic steps in the backbone sphingolipid synthesis and the incorporation of the head group respectively. Finally, a systematic framework of identifying and ranking quantitatively, possible gene mutations from altered complex sphingolipid species profiles was developed. The proposed method was able to identify the genes targeted for regulation by incorporating the variation of the lipidomic profile of the network with respect to the steady state conditions.

    Reference

    • EPFL-POSTER-202701

    Record created on 2014-10-31, modified on 2016-08-09

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