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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.


Trajectories of the membrane sizes (time courses). Dynamic behavior of the model during one cell cycle, with a G1-phase duration of 30 min. The time evolution of the membrane sizes, measured by the number of lipids in each membrane is shown. Thick lines represent the mean, shaded areas the standard deviation of 1000 model simulations. (A) Plasma membrane growth (B) Growth of all other subcellular membranes occurring in the model.
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Figure 3: Trajectories of the membrane sizes (time courses). Dynamic behavior of the model during one cell cycle, with a G1-phase duration of 30 min. The time evolution of the membrane sizes, measured by the number of lipids in each membrane is shown. Thick lines represent the mean, shaded areas the standard deviation of 1000 model simulations. (A) Plasma membrane growth (B) Growth of all other subcellular membranes occurring in the model.

Mentions: The developed model was able to simulate the growth of all membranes over the time of one cell cycle (Figure 3). The simulated growth reproduces with satisfactory accuracy the benchmark data values (Table 2), while including stochastic effects. It also captures the dynamic build-up of lipid droplets during G1 phase (first 30 min of the simulation). Upon entry to S phase, the formation of the bud requires a higher biosynthetic capacity. In the model, the lipid droplets are consumed in this phase, to allow for a faster growth of all other organelles' membranes, in accordance with their behavior in vivo (Wang, 2015). The lipid droplets are also the component with the highest variability in its size (c.f. standard deviation in Figure 3B), which can be explained by the reversible nature of their build-up. In contrast to the remaining membranes, from which lipids cannot be freed once they were included in the membrane, neutral lipids can be released again from lipid droplets via the TAG lipase reaction.


Computational Modeling of Lipid Metabolism in Yeast
Trajectories of the membrane sizes (time courses). Dynamic behavior of the model during one cell cycle, with a G1-phase duration of 30 min. The time evolution of the membrane sizes, measured by the number of lipids in each membrane is shown. Thick lines represent the mean, shaded areas the standard deviation of 1000 model simulations. (A) Plasma membrane growth (B) Growth of all other subcellular membranes occurring in the model.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: Trajectories of the membrane sizes (time courses). Dynamic behavior of the model during one cell cycle, with a G1-phase duration of 30 min. The time evolution of the membrane sizes, measured by the number of lipids in each membrane is shown. Thick lines represent the mean, shaded areas the standard deviation of 1000 model simulations. (A) Plasma membrane growth (B) Growth of all other subcellular membranes occurring in the model.
Mentions: The developed model was able to simulate the growth of all membranes over the time of one cell cycle (Figure 3). The simulated growth reproduces with satisfactory accuracy the benchmark data values (Table 2), while including stochastic effects. It also captures the dynamic build-up of lipid droplets during G1 phase (first 30 min of the simulation). Upon entry to S phase, the formation of the bud requires a higher biosynthetic capacity. In the model, the lipid droplets are consumed in this phase, to allow for a faster growth of all other organelles' membranes, in accordance with their behavior in vivo (Wang, 2015). The lipid droplets are also the component with the highest variability in its size (c.f. standard deviation in Figure 3B), which can be explained by the reversible nature of their build-up. In contrast to the remaining membranes, from which lipids cannot be freed once they were included in the membrane, neutral lipids can be released again from lipid droplets via the TAG lipase reaction.

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.