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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 by hierarchical clustering of parameters based on their functional redundancy. Identifiability analysis yielded 37 unidentifiable parameters (marked in red).The labels and corresponding names of parameters are provided in Additional file 1. b Clusters of reactions induced by hierarchical grouping. The colors of connections between species are compatible with the colors of clusters in the dendrogram. Color intensity within the cluster corresponds with the level of redundancy between reaction parameters and other parameters in the cluster
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Fig4: a Dendrogram obtained by hierarchical clustering of parameters based on their functional redundancy. Identifiability analysis yielded 37 unidentifiable parameters (marked in red).The labels and corresponding names of parameters are provided in Additional file 1. b Clusters of reactions induced by hierarchical grouping. The colors of connections between species are compatible with the colors of clusters in the dendrogram. Color intensity within the cluster corresponds with the level of redundancy between reaction parameters and other parameters in the cluster

Mentions: Through sensitivity clustering of parameters (see Section Methods) we obtained a dendrogram where four clusters may be clearly distinguished (Fig. 4). These clusters may be interpreted as specific functional modules. Our results are compatible with the theoretical compartments recognized by Rao et al. [65], who presented the sphingolipid metabolism pathway as a combination of the following units: (i) the C1 compartment that represents the de novo biosynthesis of CER, (ii) the C2 compartment that reflects the conversion of CER into complex sphingolipids such as SM and GSL, (iii) the C3 compartment that represents the hydrolysis of SM to CER and (iv) the C4 compartment that reflects the conversion of CER into bioactive molecules such as C1P and S1P.Fig. 4


Computational modeling of sphingolipid metabolism.

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

a Dendrogram obtained by hierarchical clustering of parameters based on their functional redundancy. Identifiability analysis yielded 37 unidentifiable parameters (marked in red).The labels and corresponding names of parameters are provided in Additional file 1. b Clusters of reactions induced by hierarchical grouping. The colors of connections between species are compatible with the colors of clusters in the dendrogram. Color intensity within the cluster corresponds with the level of redundancy between reaction parameters and other parameters in the cluster
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: a Dendrogram obtained by hierarchical clustering of parameters based on their functional redundancy. Identifiability analysis yielded 37 unidentifiable parameters (marked in red).The labels and corresponding names of parameters are provided in Additional file 1. b Clusters of reactions induced by hierarchical grouping. The colors of connections between species are compatible with the colors of clusters in the dendrogram. Color intensity within the cluster corresponds with the level of redundancy between reaction parameters and other parameters in the cluster
Mentions: Through sensitivity clustering of parameters (see Section Methods) we obtained a dendrogram where four clusters may be clearly distinguished (Fig. 4). These clusters may be interpreted as specific functional modules. Our results are compatible with the theoretical compartments recognized by Rao et al. [65], who presented the sphingolipid metabolism pathway as a combination of the following units: (i) the C1 compartment that represents the de novo biosynthesis of CER, (ii) the C2 compartment that reflects the conversion of CER into complex sphingolipids such as SM and GSL, (iii) the C3 compartment that represents the hydrolysis of SM to CER and (iv) the C4 compartment that reflects the conversion of CER into bioactive molecules such as C1P and S1P.Fig. 4

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