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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

a Dendrogram obtained for AD scenario by hierarchical clustering of parameters based on their functional redundancy. Model contains 36 non-identifiable parameters. b Clusters of reactions induced by the hierarchical grouping
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Fig6: a Dendrogram obtained for AD scenario by hierarchical clustering of parameters based on their functional redundancy. Model contains 36 non-identifiable parameters. b Clusters of reactions induced by the hierarchical grouping

Mentions: Clustering analysis of AD model resulted in new parameter dendrogram, with only two clusters in comparison to four clusters obtained in homoeostasis (Fig. 6 vs Fig. 4). Clusters distinguished in AD simulation can be described as follows.Fig. 6


Computational modeling of sphingolipid metabolism.

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

a Dendrogram obtained for AD scenario by hierarchical clustering of parameters based on their functional redundancy. Model contains 36 non-identifiable parameters. b Clusters of reactions induced by the hierarchical grouping
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig6: a Dendrogram obtained for AD scenario by hierarchical clustering of parameters based on their functional redundancy. Model contains 36 non-identifiable parameters. b Clusters of reactions induced by the hierarchical grouping
Mentions: Clustering analysis of AD model resulted in new parameter dendrogram, with only two clusters in comparison to four clusters obtained in homoeostasis (Fig. 6 vs Fig. 4). Clusters distinguished in AD simulation can be described as follows.Fig. 6

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