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Computational Modeling of Lipid Metabolism in Yeast

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.


Model scheme. (A) Schematic representation of the model. Small molecule precursors, whose concentrations are held constant during the simulation, are depicted in orange, intermediate metabolites in light blue and lipids that can be incorporated in the different membranes in dark blue (abbreviations as in Table 1). The reactions I - XIX are described in detail in Supplementary Table 1. (B) Schematic representation of the transport reactions that move produced lipids to the compartment membranes with different stoichiometries (gray arrows).
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Figure 2: Model scheme. (A) Schematic representation of the model. Small molecule precursors, whose concentrations are held constant during the simulation, are depicted in orange, intermediate metabolites in light blue and lipids that can be incorporated in the different membranes in dark blue (abbreviations as in Table 1). The reactions I - XIX are described in detail in Supplementary Table 1. (B) Schematic representation of the transport reactions that move produced lipids to the compartment membranes with different stoichiometries (gray arrows).

Mentions: To describe the production of lipids from metabolic precursors lumped enzymatic reactions were implemented, similarly to the rules in purely agent-based approaches (all reactions depicted in Figure 2A). We used two general parameters to describe how often each of those reactions is executed during the current time step. In analogy to the Michaelis-Menten kinetic, which is commonly used to describe enzymatic reactions, each reaction has a maximum number of executions Nmax per time step (corresponding to the vmax) and a certain probability p that the reaction actually takes place in each execution. Following the analogy, the probability is calculated from the substrate-limited KM term in the MM equation as(1)p = [S]KM + [S]To implement substrate dependency, the probability p is updated before every time step according to the substrate concentrations after the previous time step. For reactions with more than one substrate we used a product of the above simple saturation terms for each substrate, resulting in exactly one KM parameter per substrate and reaction.


Computational Modeling of Lipid Metabolism in Yeast
Model scheme. (A) Schematic representation of the model. Small molecule precursors, whose concentrations are held constant during the simulation, are depicted in orange, intermediate metabolites in light blue and lipids that can be incorporated in the different membranes in dark blue (abbreviations as in Table 1). The reactions I - XIX are described in detail in Supplementary Table 1. (B) Schematic representation of the transport reactions that move produced lipids to the compartment membranes with different stoichiometries (gray arrows).
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Related In: Results  -  Collection

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

Figure 2: Model scheme. (A) Schematic representation of the model. Small molecule precursors, whose concentrations are held constant during the simulation, are depicted in orange, intermediate metabolites in light blue and lipids that can be incorporated in the different membranes in dark blue (abbreviations as in Table 1). The reactions I - XIX are described in detail in Supplementary Table 1. (B) Schematic representation of the transport reactions that move produced lipids to the compartment membranes with different stoichiometries (gray arrows).
Mentions: To describe the production of lipids from metabolic precursors lumped enzymatic reactions were implemented, similarly to the rules in purely agent-based approaches (all reactions depicted in Figure 2A). We used two general parameters to describe how often each of those reactions is executed during the current time step. In analogy to the Michaelis-Menten kinetic, which is commonly used to describe enzymatic reactions, each reaction has a maximum number of executions Nmax per time step (corresponding to the vmax) and a certain probability p that the reaction actually takes place in each execution. Following the analogy, the probability is calculated from the substrate-limited KM term in the MM equation as(1)p = [S]KM + [S]To implement substrate dependency, the probability p is updated before every time step according to the substrate concentrations after the previous time step. For reactions with more than one substrate we used a product of the above simple saturation terms for each substrate, resulting in exactly one KM parameter per substrate and 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.