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Computational modeling of sphingolipid metabolism.

Wronowska W, Charzyńska A, Nienałtowski K, Gambin A - BMC Syst Biol (2015)

Bottom Line: Contrary to the previous approaches, we use a model that reflects cell compartmentalization thereby highlighting the differences among individual organelles.The model that we present here was validated using recently proposed methods of model analysis, allowing to detect the most sensitive and experimentally non-identifiable parameters and determine the main sources of model variance.Moreover, we demonstrate the usefulness of our model in the study of molecular processes underlying Alzheimer's disease, which are associated with sphingolipid metabolism.

View Article: PubMed Central - PubMed

Affiliation: Institute of Computer Science Polish Academy of Sciences, Warsaw, Poland. wwro@biol.uw.edu.pl.

ABSTRACT

Background: As suggested by the origin of the word, sphingolipids are mysterious molecules with various roles in antagonistic cellular processes such as autophagy, apoptosis, proliferation and differentiation. Moreover, sphingolipids have recently been recognized as important messengers in cellular signaling pathways. Notably, sphingolipid metabolism disorders have been observed in various pathological conditions such as cancer and neurodegeneration.

Results: The existing formal models of sphingolipid metabolism focus mainly on de novo ceramide synthesis or are limited to biochemical transformations of particular subspecies. Here, we propose the first comprehensive computational model of sphingolipid metabolism in human tissue. Contrary to the previous approaches, we use a model that reflects cell compartmentalization thereby highlighting the differences among individual organelles.

Conclusions: The model that we present here was validated using recently proposed methods of model analysis, allowing to detect the most sensitive and experimentally non-identifiable parameters and determine the main sources of model variance. Moreover, we demonstrate the usefulness of our model in the study of molecular processes underlying Alzheimer's disease, which are associated with sphingolipid metabolism.

No MeSH data available.


Related in: MedlinePlus

The variance decomposition of ceramide concentrations into components steaming from all model reactions. The red lines denote the average variance components of the investigated species. The red bars denote the variance components that exceeded the threshold of 110 % of average. The x-axis denotes reactions numbers and y-axis denotes the size of variance components
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Fig3: The variance decomposition of ceramide concentrations into components steaming from all model reactions. The red lines denote the average variance components of the investigated species. The red bars denote the variance components that exceeded the threshold of 110 % of average. The x-axis denotes reactions numbers and y-axis denotes the size of variance components

Mentions: The variance decomposition method enables to decompose noise associated with uncertainty of the modeled output into components related to different reactions [31, 63]. The application of this method to our model principally indicates the reactions corresponding to edges in Fig. 1 incident to investigated species as the highest noise generators. Nevertheless, some reactions were more significant for investigated species than other incident reactions, whereas for some other species, variances are distributed equally among all reactions. To find the distinctive reactions, we calculated the mean variance for each investigated species and set the threshold to 110 % of the mean variance. The results for CER species are depicted in Fig. 3 and those for Sph, S1P and SM are depicted in Additional file 1: Figures S4, S5 and S6, respectively.


Computational modeling of sphingolipid metabolism.

Wronowska W, Charzyńska A, Nienałtowski K, Gambin A - BMC Syst Biol (2015)

The variance decomposition of ceramide concentrations into components steaming from all model reactions. The red lines denote the average variance components of the investigated species. The red bars denote the variance components that exceeded the threshold of 110 % of average. The x-axis denotes reactions numbers and y-axis denotes the size of variance components
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4537549&req=5

Fig3: The variance decomposition of ceramide concentrations into components steaming from all model reactions. The red lines denote the average variance components of the investigated species. The red bars denote the variance components that exceeded the threshold of 110 % of average. The x-axis denotes reactions numbers and y-axis denotes the size of variance components
Mentions: The variance decomposition method enables to decompose noise associated with uncertainty of the modeled output into components related to different reactions [31, 63]. The application of this method to our model principally indicates the reactions corresponding to edges in Fig. 1 incident to investigated species as the highest noise generators. Nevertheless, some reactions were more significant for investigated species than other incident reactions, whereas for some other species, variances are distributed equally among all reactions. To find the distinctive reactions, we calculated the mean variance for each investigated species and set the threshold to 110 % of the mean variance. The results for CER species are depicted in Fig. 3 and those for Sph, S1P and SM are depicted in Additional file 1: Figures S4, S5 and S6, respectively.

Bottom Line: Contrary to the previous approaches, we use a model that reflects cell compartmentalization thereby highlighting the differences among individual organelles.The model that we present here was validated using recently proposed methods of model analysis, allowing to detect the most sensitive and experimentally non-identifiable parameters and determine the main sources of model variance.Moreover, we demonstrate the usefulness of our model in the study of molecular processes underlying Alzheimer's disease, which are associated with sphingolipid metabolism.

View Article: PubMed Central - PubMed

Affiliation: Institute of Computer Science Polish Academy of Sciences, Warsaw, Poland. wwro@biol.uw.edu.pl.

ABSTRACT

Background: As suggested by the origin of the word, sphingolipids are mysterious molecules with various roles in antagonistic cellular processes such as autophagy, apoptosis, proliferation and differentiation. Moreover, sphingolipids have recently been recognized as important messengers in cellular signaling pathways. Notably, sphingolipid metabolism disorders have been observed in various pathological conditions such as cancer and neurodegeneration.

Results: The existing formal models of sphingolipid metabolism focus mainly on de novo ceramide synthesis or are limited to biochemical transformations of particular subspecies. Here, we propose the first comprehensive computational model of sphingolipid metabolism in human tissue. Contrary to the previous approaches, we use a model that reflects cell compartmentalization thereby highlighting the differences among individual organelles.

Conclusions: The model that we present here was validated using recently proposed methods of model analysis, allowing to detect the most sensitive and experimentally non-identifiable parameters and determine the main sources of model variance. Moreover, we demonstrate the usefulness of our model in the study of molecular processes underlying Alzheimer's disease, which are associated with sphingolipid metabolism.

No MeSH data available.


Related in: MedlinePlus