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Simulation and validation of modelled sphingolipid metabolism in Saccharomyces cerevisiae

Nature volume 433, pages 425–430 (2005)Cite this article

Abstract

Mathematical models have become a necessary tool for organizing the rapidly increasing amounts of large-scale data on biochemical pathways and for advanced evaluation of their structure and regulation. Most of these models have addressed specific pathways using either stoichiometric1 or flux-balance analysis2, or fully kinetic Michaelis–Menten representations3, metabolic control analysis4, or biochemical systems theory5,6,7. So far, the predictions of kinetic models have rarely been tested using direct experimentation. Here, we validate experimentally a biochemical systems theoretical model of sphingolipid metabolism in yeast8. Simulations of metabolic fluxes, enzyme deletion and the effects of inositol (a key regulator of phospholipid metabolism) led to predictions that show significant concordance with experimental results generated post hoc. The model also allowed the simulation of the effects of acute perturbations in fatty-acid precursors of sphingolipids, a situation that is not amenable to direct experimentation. The results demonstrate that modelling now allows testable predictions as well as the design and evaluation of hypothetical ‘thought experiments’ that may generate new metabolomic approaches.

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Figure 1: Metabolic map of sphingolipid metabolism and related processes in yeast.
Figure 2: Experimental and simulated lipid profiles from labelled palmitate.
Figure 3: Response of sphingolipid pathway to increased inositol.
Figure 4: Response of sphingolipid pathway to perturbation of palmitoyl-CoA precursors.

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Acknowledgements

This work was supported by a NIH grant. Doctoral studies of K.J.S. are supported by a NLM training grant. The authors would like to thank C. Mao and S. Vaena de Avalos for helpful discussions and the MUSC Lipidomics Core for sample analysis.Authors' contributions F.A.-V. developed the model and conducted the simulations. K.J.S. performed the experiments under the supervision of Y.O. and drafted the manuscript. L.A.C. performed the inositol experiment. Y.A.H. and E.O.V. conceived and supervised the collaboration and overall strategy of the project, and edited the manuscript.

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Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology,

    Fernando Alvarez-Vasquez, Kellie J. Sims, L. Ashley Cowart, Yasuo Okamoto, Eberhard O. Voit & Yusuf A. Hannun

  2. Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, South Carolina, 29425, USA

    Fernando Alvarez-Vasquez, Kellie J. Sims & Eberhard O. Voit

  3. The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory Medical School, 313 Ferst Drive, Georgia, 30332-0535, Atlanta, USA

    Eberhard O. Voit

Authors
  1. Fernando Alvarez-Vasquez
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  2. Kellie J. Sims
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  3. L. Ashley Cowart
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  4. Yasuo Okamoto
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  5. Eberhard O. Voit
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  6. Yusuf A. Hannun
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Corresponding authors

Correspondence to Eberhard O. Voit or Yusuf A. Hannun.

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Supplementary information

Supplementary Tables 1 and 2

Supplementary Table 1: Metabolite concentrations. Information taken from the literature for metabolite concentrations in the model. Lists metabolites with model symbol, concentration, comments and references. Supplementary Table 2: Specific activities and kinetic parameters. Information taken from the literature for specific activities and kinetic parameters in the model. Lists enzymes and transporters with model symbol, gene name, specific activity, kinetics values such as Km or Vmax, comments and references. (PDF 402 kb)

Supplementary Text 1

Model design. A brief description of the process of creating a biochemical systems model with an example of equation development using a reaction from sphingolipid metabolism. (DOC 46 kb)

Supplementary Text 2 and Supplementary Figures 1 and 2

Extensions to the prototype model. The rationale for changes made to the prototype model to better predict effects on sphingolipid metabolism. These include the addition and compartmentalization of the complex sphingolipids, the synthesis and elongation of fatty acids, and other small changes. (DOC 65 kb)

Supplementary Text 3 and Supplementary Table 3 and Supplementary Fig. 3

Sensitivity analysis. The methods of sensitivity analysis, with results from this model, (DOC 71 kb)

Supplementary Text 4 and Supplementary Figure 4

Substrate uptake function. The determination of a function for the uptake of labelled substrate. (DOC 27 kb)

Supplementary Text 5

Tracer simulation method and model equations. A method of modelling labelled and unlabelled pools of metabolites. The GMA system of equations for sphingolipid metabolism is given for total, labelled and unlabelled pools using palmitate as the tracer. Also included are the alterations made to the model equations to allow simulation of malonyl-CoA substrate. (DOC 592 kb)

Supplementary Figure 5

Autoradiographs of lipid profiles for wild type and DPL1 knockout strains. (DOC 571 kb)

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Alvarez-Vasquez, F., Sims, K., Cowart, L. et al. Simulation and validation of modelled sphingolipid metabolism in Saccharomyces cerevisiae. Nature 433, 425–430 (2005). https://doi.org/10.1038/nature03232

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