(285f) Metabolic Modeling of Sphingolipid Metabolism to Elucidate the Flux Changes and Regulatory Events Induced By Biologically-Significant Perturbations

Sphingolipids are an essential component of a plant cell’s plasma membrane and endomembranes. This class of lipids plays several functional roles including providing structural integrity to the membrane, Golgi trafficking, and protein organizational domains. In addition, sphingolipids have been implicated in physiological processes such as the signaling of Programmed Cell Death (PCD) and the hypersensitive response associated with plant resistance to bacterial and fungal pathogens. The metabolic pathways associated with Sphingolipid biosynthesis are tightly controlled to ensure sufficient sphingolipid availability for normal cell growth. Simultaneously, metabolic controls constrain the accumulation of sphingolipid building blocks responsible for the induction of PCD until this process is required (e.g. during the pathogen triggered hypersensitive response). Recent work has shown that the first committed step in Sphingolipid metabolism catalyzed by Serine palmitoyltransferase (SPT) is the primary control point for regulation of this pathway. Therefore, in this work, a combined computational and experimental approach was taken to mechanistically decipher the regulation of sphingolipid biosynthesis. A compartmentalized metabolic network of sphingolipid biosynthesis in Arabidopsis thalianacomprised of 399 metabolites and 739 reactions was reconstructed from available literature and the KEGG database. Flux Balance Analysis (FBA) was used to simulate the steady-state flux distribution of the network at the measured uptake rates of the starting material Sphingosine. Next, experimental data on the changes of metabolite profiles under different conditions were used to further constrain the flux distribution through additional ad hoc constraints. Finally, an Ensemble Modeling (EM) framework is being implemented to construct a kinetic model of the metabolic and regulatory network of Sphingolipid biosynthesis. This EM approach allows for the prediction of enzyme kinetics and metabolite concentrations given a metabolic network and a set of experimentally determined reaction fluxes. Once the set of experimental reaction fluxes is generated, the kinetic model will be used to predict plausible regulatory mechanisms implicated in the control of Sphingolipid metabolism. Experiments will next be conducted to probe these predictions and subsequently update the model with the new findings to improve its predictive accuracy, therefore closing the design-build-test cycle.