(242g) Integrated Modeling of Lipids Metabolism and Signaling Pathways

Maer, A. - Presenter, GHC Technologies, Inc.
Subramaniam, S. - Presenter, University of California, San Diego

Lipids are the main structural component of cell membranes, modulate cellular trafficking and also function as cellular signaling molecules. Many lipid-derived metabolites play an important role in the regulation and control of various cellular functions, e.g. cell proliferation, apoptosis, etc and in many pathophysiology, e.g. inflammation, atherosclerosis, etc. 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. Goal of the modeling work presented here is to construct a predictive kinetic model for lipid metabolism and signaling using the lipid pathways gleaned from the literature and the time-course data from LIPID MAPS.

We have developed an integrated network map of lipid metabolism and signaling pathways containing glycerophospholipids, glycerolipids, sphingolipids and eicosanoids based on the literature and the KEGG pathways. The matrix-based approach was used to estimate rate constants using experimental data. The system was modeled as a set of ordinary differential equations. The flux expressions were based on law of mass action kinetics. Thus, the flux expressions are linear in rate parameters and nonlinear in metabolite concentrations. In order to use linear algebra-based methods for estimating the rate constants, the pathway map was simplified to retain only the measured metabolites and discretization was used to convert the differential relationships into algebraic relationships. The proposed matrix-based approach uses Matlab's optimization functions lsqlin (constrained least squares-based optimization) and fmincon (general constrained nonlinear optimization). The function lsqlin provides a good initial guess for the values of rate parameters which are then used in the function fmincon. This makes the overall process computationally efficient. The resulting model fits the experimental data well for all species and demonstrate that the integrated metabolic and signaling network and the experimental data are consistent with each other.