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Computational Modeling of Lipid Metabolism in Yeast

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ABSTRACT

Lipid metabolism is essential for all major cell functions and has recently gained increasing attention in research and health studies. However, mathematical modeling by means of classical approaches such as stoichiometric networks and ordinary differential equation systems has not yet provided satisfactory insights, due to the complexity of lipid metabolism characterized by many different species with only slight differences and by promiscuous multifunctional enzymes. Here, we present an object-oriented stochastic model approach as a way to cope with the complex lipid metabolic network. While all lipid species are treated objects in the model, they can be modified by the respective converting reactions based on reaction rules, a hybrid method that integrates benefits of agent-based and classical stochastic simulation. This approach allows to follow the dynamics of all lipid species with different fatty acids, different degrees of saturation and different headgroups over time and to analyze the effect of parameter changes, potential mutations in the catalyzing enzymes or provision of different precursors. Applied to yeast metabolism during one cell cycle period, we could analyze the distribution of all lipids to the various membranes in time-dependent manner. The presented approach allows to efficiently treat the complexity of cellular lipid metabolism and to derive conclusions on the time- and location-dependent distributions of lipid species and their properties such as saturation. It is widely applicable, easily extendable and will provide further insights in healthy and diseased states of cell metabolism.

No MeSH data available.


Sensitivities. Sensitivities of the membrane sizes to changes in the maximum rates Nmax of the synthesis reactions. The sensitivities are shown as percent of change in membrane size upon a parameter change of one unit. Sensitivities are color coded, values larger than one percent are additionally given as numbers.
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Figure 5: Sensitivities. Sensitivities of the membrane sizes to changes in the maximum rates Nmax of the synthesis reactions. The sensitivities are shown as percent of change in membrane size upon a parameter change of one unit. Sensitivities are color coded, values larger than one percent are additionally given as numbers.

Mentions: We see that in our modeled system, the size of the lipid droplets is most sensitive to perturbations in the reaction rates (Figure 5). In turn, the reactions which synthesize and degrade lipids from the droplets have a larger influence also on the sizes of the other cellular membranes, highlighting the importance of storage lipids and their mobilization. Overall the sensitivities are relatively small, with a maximum of 48.6% change per parameter unit changed (TAG lipase on lipid droplets), but with the majority of the sensitivities lying below 1% (see also Supplementary Figure 3 for sensitivities of membrane compositions). Hence, the model behavior is in general rather robust to changes in the rate parameters.


Computational Modeling of Lipid Metabolism in Yeast
Sensitivities. Sensitivities of the membrane sizes to changes in the maximum rates Nmax of the synthesis reactions. The sensitivities are shown as percent of change in membrane size upon a parameter change of one unit. Sensitivities are color coded, values larger than one percent are additionally given as numbers.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037213&req=5

Figure 5: Sensitivities. Sensitivities of the membrane sizes to changes in the maximum rates Nmax of the synthesis reactions. The sensitivities are shown as percent of change in membrane size upon a parameter change of one unit. Sensitivities are color coded, values larger than one percent are additionally given as numbers.
Mentions: We see that in our modeled system, the size of the lipid droplets is most sensitive to perturbations in the reaction rates (Figure 5). In turn, the reactions which synthesize and degrade lipids from the droplets have a larger influence also on the sizes of the other cellular membranes, highlighting the importance of storage lipids and their mobilization. Overall the sensitivities are relatively small, with a maximum of 48.6% change per parameter unit changed (TAG lipase on lipid droplets), but with the majority of the sensitivities lying below 1% (see also Supplementary Figure 3 for sensitivities of membrane compositions). Hence, the model behavior is in general rather robust to changes in the rate parameters.

View Article: PubMed Central - PubMed

ABSTRACT

Lipid metabolism is essential for all major cell functions and has recently gained increasing attention in research and health studies. However, mathematical modeling by means of classical approaches such as stoichiometric networks and ordinary differential equation systems has not yet provided satisfactory insights, due to the complexity of lipid metabolism characterized by many different species with only slight differences and by promiscuous multifunctional enzymes. Here, we present an object-oriented stochastic model approach as a way to cope with the complex lipid metabolic network. While all lipid species are treated objects in the model, they can be modified by the respective converting reactions based on reaction rules, a hybrid method that integrates benefits of agent-based and classical stochastic simulation. This approach allows to follow the dynamics of all lipid species with different fatty acids, different degrees of saturation and different headgroups over time and to analyze the effect of parameter changes, potential mutations in the catalyzing enzymes or provision of different precursors. Applied to yeast metabolism during one cell cycle period, we could analyze the distribution of all lipids to the various membranes in time-dependent manner. The presented approach allows to efficiently treat the complexity of cellular lipid metabolism and to derive conclusions on the time- and location-dependent distributions of lipid species and their properties such as saturation. It is widely applicable, easily extendable and will provide further insights in healthy and diseased states of cell metabolism.

No MeSH data available.