Integration of lipidomics and transcriptomics data towards a systems biology model of sphingolipid metabolism
1 Department of Bioengineering, University of California, San Diego, 9500 Gilman Dr., La Jolla CA 92093, USA
2 School of Biology & Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332-0230, USA
3 Department of Cellular and Molecular Medicine, University of California, San Diego, 9500 Gilman Dr., La Jolla CA 92093, USA
4 Department of Chemistry & Biochemistry, San Diego Supercomputer Center and Graduate Program in Bioinformatics, University of California, San Diego, 9500 Gilman Dr., La Jolla CA 92093, USA
BMC Systems Biology 2011, 5:26 doi:10.1186/1752-0509-5-26Published: 8 February 2011
Sphingolipids play important roles in cell structure and function as well as in the pathophysiology of many diseases. Many of the intermediates of sphingolipid biosynthesis are highly bioactive and sometimes have antagonistic activities, for example, ceramide promotes apoptosis whereas sphingosine-1-phosphate can inhibit apoptosis and induce cell growth; therefore, quantification of the metabolites and modeling of the sphingolipid network is imperative for an understanding of sphingolipid biology.
In this direction, the LIPID MAPS Consortium is developing methods to quantitate the sphingolipid metabolites in mammalian cells and is investigating their application to studies of the activation of the RAW264.7 macrophage cell by a chemically defined endotoxin, Kdo2-Lipid A. Herein, we describe a model for the C16-branch of sphingolipid metabolism (i.e., for ceramides with palmitate as the N-acyl-linked fatty acid, which is selected because it is a major subspecies for all categories of complex sphingolipids in RAW264.7 cells) integrating lipidomics and transcriptomics data and using a two-step matrix-based approach to estimate the rate constants from experimental data. The rate constants obtained from the first step are further refined using generalized constrained nonlinear optimization. The resulting model fits the experimental data for all species. The robustness of the model is validated through parametric sensitivity analysis.
A quantitative model of the sphigolipid pathway is developed by integrating metabolomics and transcriptomics data with legacy knowledge. The model could be used to design experimental studies of how genetic and pharmacological perturbations alter the flux through this important lipid biosynthetic pathway.