(173f) Modeling of Sphingolipids Metabolism in Mouse Macrophage RAW 264.7 Cells

Lipids are crucial for the life of the cell. Many lipid-derived metabolites play a vital role in the regulation and control of various cellular functions and in the pathophysiology of many diseases. The LIPID MAPS consortium (www.lipidmaps.org) has developed methods to quantitatively measure the composition of lipids and its metabolites in RAW 264.7 macrophage cells. Time-course data in response to treatment with KDO₂ lipid A (a lipopolysaccharide analogue) has been collected. Recently, we have developed a novel two steps approach to estimating rate parameters using temporal data. First, a constrained least squares-based optimization (the Matlab^® optimization functions lsqlin) is used to compute good initial guesses for the parameter values. Second, a generalized constrained nonlinear optimization (Matlab^® optimization function fmincon) is used to estimate the parameters. The combined use of both functions makes the overall process computationally efficient. We have used this approach in developing a kinetic model of eicosanoids metabolism in macrophage RAW 264.7 cells (1). Herein, we have extended our approach to develop a model for sphingolipids metabolism through integration of legacy knowledge of its metabolic network and experimental data.

One of the central molecules in the metabolism of sphingolipids is ceramide. Due to their role in apoptosis and thus in the treatment of cancer, they are being studied rigorously. LIPID MAPS initiative has measured ceramide and its derivatives e.g., glycero-ceramide, sphingomyelin, etc. Using the experimental data, we have developed a kinetic model and estimated the rate parameters. The resulting model fits the experimental data well for all the measured metabolites demonstrating that both the overall network structure and the experimental data are consistent with each other. The robustness of model parameters was also validated through parametric sensitivity analysis.

References

1. Gupta, S., M. R. Maurya, D. L. Stephens, E. A. Dennis, and S. Subramaniam. 2009. An Integrated Model of Eicosanoid Metabolism and Signaling Based on Lipidomics Flux Analysis. Biophys J. 96:In Print.