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Dynamics of the Heat Stress Response of Ceramides with Different Fatty-Acyl Chain Lengths in Baker's Yeast.

Chen PW, Fonseca LL, Hannun YA, Voit EO - PLoS Comput. Biol. (2015)

Bottom Line: The article demonstrates that computational modeling has the capacity to convert metabolic snapshots, taken sequentially over time, into a description of cellular, dynamic strategies.The specific application is a detailed analysis of a set of actions with which Saccharomyces cerevisiae responds to heat stress.The details of the sphingolipid responses to heat stress are important, because they guide some of the longer-term alterations in gene expression, with which the cells adapt to the increased temperature.

View Article: PubMed Central - PubMed

Affiliation: Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America; The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

ABSTRACT
The article demonstrates that computational modeling has the capacity to convert metabolic snapshots, taken sequentially over time, into a description of cellular, dynamic strategies. The specific application is a detailed analysis of a set of actions with which Saccharomyces cerevisiae responds to heat stress. Using time dependent metabolic concentration data, we use a combination of mathematical modeling, reverse engineering, and optimization to infer dynamic changes in enzyme activities within the sphingolipid pathway. The details of the sphingolipid responses to heat stress are important, because they guide some of the longer-term alterations in gene expression, with which the cells adapt to the increased temperature. The analysis indicates that all enzyme activities in the system are affected and that the shapes of the time trends in activities depend on the fatty-acyl CoA chain lengths of the different ceramide species in the system.

No MeSH data available.


Related in: MedlinePlus

Histograms of ceramide synthase fluxes from C16 DHS to C16 DHC (upper panel) and from C16 PHS to C16 PHC (lower panel).Time 0 represents normal steady-state temperature conditions at the beginning of the heat stress experiment, which lasts for 30 minutes.
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pcbi.1004373.g013: Histograms of ceramide synthase fluxes from C16 DHS to C16 DHC (upper panel) and from C16 PHS to C16 PHC (lower panel).Time 0 represents normal steady-state temperature conditions at the beginning of the heat stress experiment, which lasts for 30 minutes.

Mentions: As an example, the resulting fluxes of reactions catalyzed by ceramide synthase are given in Fig 12; other fluxes are shown in the Supplements (Fig S1 in S2 Text). Once the flux distributions are computed for each time point, we examine the histogram of each flux in each time point to ensure that the solutions are well constrained; details are presented in the Supplements (Fig S3 in S4 Text). The analysis revealed that most of the flux distributions at any given time point were rather tightly bell shaped, suggesting the use of the mean value of each flux at each time point as an appropriate, time-dependent estimate (Fig 13). To validate this result further, we compared the sum of squared errors (SSEs) between the 1,000 individual fluxes and the corresponding averaged fluxes at each time point. The SSE of the averaged flux always falls within the range of SSEs from individual fluxes (Fig S5 in S6 Text); further details are provided in the Supplements. We also redid the analysis with medians, but the results were essentially the same.


Dynamics of the Heat Stress Response of Ceramides with Different Fatty-Acyl Chain Lengths in Baker's Yeast.

Chen PW, Fonseca LL, Hannun YA, Voit EO - PLoS Comput. Biol. (2015)

Histograms of ceramide synthase fluxes from C16 DHS to C16 DHC (upper panel) and from C16 PHS to C16 PHC (lower panel).Time 0 represents normal steady-state temperature conditions at the beginning of the heat stress experiment, which lasts for 30 minutes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4524633&req=5

pcbi.1004373.g013: Histograms of ceramide synthase fluxes from C16 DHS to C16 DHC (upper panel) and from C16 PHS to C16 PHC (lower panel).Time 0 represents normal steady-state temperature conditions at the beginning of the heat stress experiment, which lasts for 30 minutes.
Mentions: As an example, the resulting fluxes of reactions catalyzed by ceramide synthase are given in Fig 12; other fluxes are shown in the Supplements (Fig S1 in S2 Text). Once the flux distributions are computed for each time point, we examine the histogram of each flux in each time point to ensure that the solutions are well constrained; details are presented in the Supplements (Fig S3 in S4 Text). The analysis revealed that most of the flux distributions at any given time point were rather tightly bell shaped, suggesting the use of the mean value of each flux at each time point as an appropriate, time-dependent estimate (Fig 13). To validate this result further, we compared the sum of squared errors (SSEs) between the 1,000 individual fluxes and the corresponding averaged fluxes at each time point. The SSE of the averaged flux always falls within the range of SSEs from individual fluxes (Fig S5 in S6 Text); further details are provided in the Supplements. We also redid the analysis with medians, but the results were essentially the same.

Bottom Line: The article demonstrates that computational modeling has the capacity to convert metabolic snapshots, taken sequentially over time, into a description of cellular, dynamic strategies.The specific application is a detailed analysis of a set of actions with which Saccharomyces cerevisiae responds to heat stress.The details of the sphingolipid responses to heat stress are important, because they guide some of the longer-term alterations in gene expression, with which the cells adapt to the increased temperature.

View Article: PubMed Central - PubMed

Affiliation: Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America; The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America.

ABSTRACT
The article demonstrates that computational modeling has the capacity to convert metabolic snapshots, taken sequentially over time, into a description of cellular, dynamic strategies. The specific application is a detailed analysis of a set of actions with which Saccharomyces cerevisiae responds to heat stress. Using time dependent metabolic concentration data, we use a combination of mathematical modeling, reverse engineering, and optimization to infer dynamic changes in enzyme activities within the sphingolipid pathway. The details of the sphingolipid responses to heat stress are important, because they guide some of the longer-term alterations in gene expression, with which the cells adapt to the increased temperature. The analysis indicates that all enzyme activities in the system are affected and that the shapes of the time trends in activities depend on the fatty-acyl CoA chain lengths of the different ceramide species in the system.

No MeSH data available.


Related in: MedlinePlus