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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.


Simulated test cases of the model behavior. (A) Ergosterol content in each membrane after simulation of the terbinafine treatment (yellow) in comparison with previous untreated simulations (blue). (B) Fractions of PI in all membranes after standard simulation (blue) and simulated inositol addition (green). Error bars represent standard deviations of 1000 simulations.
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Figure 6: Simulated test cases of the model behavior. (A) Ergosterol content in each membrane after simulation of the terbinafine treatment (yellow) in comparison with previous untreated simulations (blue). (B) Fractions of PI in all membranes after standard simulation (blue) and simulated inositol addition (green). Error bars represent standard deviations of 1000 simulations.

Mentions: In the model the treatment was simulated by setting the ergosterol production rate to 0, leading to an average reduction in ergosterol content of about 30% in all membranes compared to the unperturbed simulations (Figure 6A). Leber et al. (1995) also reports a slightly reduced cell growth as a side effect of the blocked ergosterol production, which we also observe in the simulated membrane growth (~80,000 instead of ~96,000 lipids total). The different membrane growth rates after ergosterol synthesis inhibition are also a proof for the utilization of the storage lipids: While lipids from the lipid droplets can be mobilized, all membranes grow faster. However, once the lipid droplets are completely emptied, the membranes grow with significantly decreased rates (Supplementary Figures 4A,B).


Computational Modeling of Lipid Metabolism in Yeast
Simulated test cases of the model behavior. (A) Ergosterol content in each membrane after simulation of the terbinafine treatment (yellow) in comparison with previous untreated simulations (blue). (B) Fractions of PI in all membranes after standard simulation (blue) and simulated inositol addition (green). Error bars represent standard deviations of 1000 simulations.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5037213&req=5

Figure 6: Simulated test cases of the model behavior. (A) Ergosterol content in each membrane after simulation of the terbinafine treatment (yellow) in comparison with previous untreated simulations (blue). (B) Fractions of PI in all membranes after standard simulation (blue) and simulated inositol addition (green). Error bars represent standard deviations of 1000 simulations.
Mentions: In the model the treatment was simulated by setting the ergosterol production rate to 0, leading to an average reduction in ergosterol content of about 30% in all membranes compared to the unperturbed simulations (Figure 6A). Leber et al. (1995) also reports a slightly reduced cell growth as a side effect of the blocked ergosterol production, which we also observe in the simulated membrane growth (~80,000 instead of ~96,000 lipids total). The different membrane growth rates after ergosterol synthesis inhibition are also a proof for the utilization of the storage lipids: While lipids from the lipid droplets can be mobilized, all membranes grow faster. However, once the lipid droplets are completely emptied, the membranes grow with significantly decreased rates (Supplementary Figures 4A,B).

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.