Final Report Summary - SBLIME (Systems Biology of Lipids Metabolism)
Sphingolipids are abundant components of eukaryotic cell membranes where they play important structural and functional roles. Sphingolipids primarily contribute to the structure of the cell membrane. They aggregate with other lipids to form structures, which regulates the transport of molecule or transmit signals across the cell membrane. Sphingolipids transmit also important intracellular signals for apoptosis, regulation of cell growth and response to the heat stress. At the organism level, the importance of sphingolipids reflects their key functions at the cellular level: sphingolipids are involved in most common and currently relevant human diseases including diabetes, a range of cancers, infections by microorganisms and Alzheimer's disease. This motivates the growing interest raised by sphingolipids in the research community.
Although a large amount of data has been collected on sphingolipids, yet most of the gathered knowledge is descriptive and phenomenological, and a clear explanation of the molecular mechanisms by which sphingolipids exert their actions is still missing. One of the main difficulties, here, is the large variety of naturally occurring sphingolipid species, each with different cellular functions. Where this variety came from? The sphingolipid molecule is a combination of three units: a sphingoid base, a fatty acid and an head-group. The large number of possible combinations of different head-groups, length of carbon chains and number and position of hydroxyl groups gives rise to the large number of different sphingolipid molecules.
Fundamental questions are how different sphingolipids associate with other lipids in the plasma membrane, with which functions, and how the respective biosynthesis pathways are interconnected to guarantee the functional equilibrium between the lipid species are fundamental questions.
A detailed characterisation of the distribution of sphingolipids according to their hydroxylation state is therefore central to identify key interactions between sphingolipid metabolism and other cellular pathways. Where and how these interactions take place are still unsettled questions.
We developed a kinetic model of the sphingolipid metabolism in yeast with the aim to identify the critical key parameters responsible for the regulation of the sphingolipid metabolism and its interaction with the global cell metabolism.
The model provides an accurate description of the kinetics of each reaction and of enzyme saturation. The kinetic constants, which characterise each enzyme reaction, are derived from biochemical data found in the literature. Lipidomic data for the lipid distribution at balanced growth are used to estimate the maximum reaction rates. This approach demonstrates the use of metabolomics data for system modelling. The model capability to reproduce distribution of sphingolipid concentrations, in stationary and dynamic conditions, has been verified. We demonstrated the possibility to discover interactions within the system from model-based analysis of lipidomic profiles obtained under different experimental conditions.
We performed a detailed sensitivity analysis of the steady-state concentrations and fluxes and we identified the key parameters that determine the function of the sphingolipid pathway and the distribution of the hydroxylation states.
In the process, several sensitivity analysis techniques have been considered and compared. A series of tools based on these techniques has been implemented. The modularity of the chosen implementation allows us to easily apply these tools not only to our sphingolipid model, but also to any model based on the System Biology Markup Language (SBML), as SBML is taking over as a standard for mathematical and numerical description of biological systems.
Although a large amount of data has been collected on sphingolipids, yet most of the gathered knowledge is descriptive and phenomenological, and a clear explanation of the molecular mechanisms by which sphingolipids exert their actions is still missing. One of the main difficulties, here, is the large variety of naturally occurring sphingolipid species, each with different cellular functions. Where this variety came from? The sphingolipid molecule is a combination of three units: a sphingoid base, a fatty acid and an head-group. The large number of possible combinations of different head-groups, length of carbon chains and number and position of hydroxyl groups gives rise to the large number of different sphingolipid molecules.
Fundamental questions are how different sphingolipids associate with other lipids in the plasma membrane, with which functions, and how the respective biosynthesis pathways are interconnected to guarantee the functional equilibrium between the lipid species are fundamental questions.
A detailed characterisation of the distribution of sphingolipids according to their hydroxylation state is therefore central to identify key interactions between sphingolipid metabolism and other cellular pathways. Where and how these interactions take place are still unsettled questions.
We developed a kinetic model of the sphingolipid metabolism in yeast with the aim to identify the critical key parameters responsible for the regulation of the sphingolipid metabolism and its interaction with the global cell metabolism.
The model provides an accurate description of the kinetics of each reaction and of enzyme saturation. The kinetic constants, which characterise each enzyme reaction, are derived from biochemical data found in the literature. Lipidomic data for the lipid distribution at balanced growth are used to estimate the maximum reaction rates. This approach demonstrates the use of metabolomics data for system modelling. The model capability to reproduce distribution of sphingolipid concentrations, in stationary and dynamic conditions, has been verified. We demonstrated the possibility to discover interactions within the system from model-based analysis of lipidomic profiles obtained under different experimental conditions.
We performed a detailed sensitivity analysis of the steady-state concentrations and fluxes and we identified the key parameters that determine the function of the sphingolipid pathway and the distribution of the hydroxylation states.
In the process, several sensitivity analysis techniques have been considered and compared. A series of tools based on these techniques has been implemented. The modularity of the chosen implementation allows us to easily apply these tools not only to our sphingolipid model, but also to any model based on the System Biology Markup Language (SBML), as SBML is taking over as a standard for mathematical and numerical description of biological systems.