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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.

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Subcellular membrane composition and agreement with data. (A) The relative contributions of each lipid class (abbreviations as in Table 1) to the total number of lipids in each membrane. Error bars denote the standard deviation of 1000 model simulations. (B) Agreement of the simulate fractions with the data of Zinser et al. (1991), each dot represents the fraction of a lipid in a subcellular membrane, all fractions of one membrane are colored according to the color key above. The red line describes a linear regression line between model and data fractions, the resulting coefficient of determination R2 is shown. Error bars as in (A).
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Figure 4: Subcellular membrane composition and agreement with data. (A) The relative contributions of each lipid class (abbreviations as in Table 1) to the total number of lipids in each membrane. Error bars denote the standard deviation of 1000 model simulations. (B) Agreement of the simulate fractions with the data of Zinser et al. (1991), each dot represents the fraction of a lipid in a subcellular membrane, all fractions of one membrane are colored according to the color key above. The red line describes a linear regression line between model and data fractions, the resulting coefficient of determination R2 is shown. Error bars as in (A).

Mentions: The lipid composition of each membrane can be retrieved from the model (Figure 4A and Supplementary Table 3). The membranes can have vastly different compositions, as was shown by Zinser et al. (1991), which is captured well by the model (Figure 4B). In addition, the model can also be used to gain insight into the fatty acid composition of the lipids, which again reproduces the experimental data of Martin and co-workers (Martin et al., 2007; Supplementary Figure 2).


Computational Modeling of Lipid Metabolism in Yeast
Subcellular membrane composition and agreement with data. (A) The relative contributions of each lipid class (abbreviations as in Table 1) to the total number of lipids in each membrane. Error bars denote the standard deviation of 1000 model simulations. (B) Agreement of the simulate fractions with the data of Zinser et al. (1991), each dot represents the fraction of a lipid in a subcellular membrane, all fractions of one membrane are colored according to the color key above. The red line describes a linear regression line between model and data fractions, the resulting coefficient of determination R2 is shown. Error bars as in (A).
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Related In: Results  -  Collection

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

Figure 4: Subcellular membrane composition and agreement with data. (A) The relative contributions of each lipid class (abbreviations as in Table 1) to the total number of lipids in each membrane. Error bars denote the standard deviation of 1000 model simulations. (B) Agreement of the simulate fractions with the data of Zinser et al. (1991), each dot represents the fraction of a lipid in a subcellular membrane, all fractions of one membrane are colored according to the color key above. The red line describes a linear regression line between model and data fractions, the resulting coefficient of determination R2 is shown. Error bars as in (A).
Mentions: The lipid composition of each membrane can be retrieved from the model (Figure 4A and Supplementary Table 3). The membranes can have vastly different compositions, as was shown by Zinser et al. (1991), which is captured well by the model (Figure 4B). In addition, the model can also be used to gain insight into the fatty acid composition of the lipids, which again reproduces the experimental data of Martin and co-workers (Martin et al., 2007; Supplementary Figure 2).

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.


Related in: MedlinePlus