Limits...
Deterministic mathematical models of the cAMP pathway in Saccharomyces cerevisiae.

Williamson T, Schwartz JM, Kell DB, Stateva L - BMC Syst Biol (2009)

Bottom Line: These results were used to develop the Complete cAMP Model.The Complete cAMP model is easier to simulate, and although significantly simpler than the existing stochastic one, it recreates cAMP levels and patterns of changes in cAMP levels observed experimentally in vivo in response to glucose addition in wild-type as well as representative mutant strains such as pde1Delta, pde2Delta, cyr1Delta, and others.Similar models could be also useful for studies in the human pathogen Candida albicans as well as other less well-characterized fungal species.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Life Sciences, The University of Manchester, Manchester, M13 9PT, UK. Thomas.Williamson@postgrad.manchester.ac.uk

ABSTRACT

Background: Cyclic adenosine monophosphate (cAMP) has a key signaling role in all eukaryotic organisms. In Saccharomyces cerevisiae, it is the second messenger in the Ras/PKA pathway which regulates nutrient sensing, stress responses, growth, cell cycle progression, morphogenesis, and cell wall biosynthesis. A stochastic model of the pathway has been reported.

Results: We have created deterministic mathematical models of the PKA module of the pathway, as well as the complete cAMP pathway. First, a simplified conceptual model was created which reproduced the dynamics of changes in cAMP levels in response to glucose addition in wild-type as well as cAMP phosphodiesterase deletion mutants. This model was used to investigate the role of the regulatory Krh proteins that had not been included previously. The Krh-containing conceptual model reproduced very well the experimental evidence supporting the role of Krh as a direct inhibitor of PKA. These results were used to develop the Complete cAMP Model. Upon simulation it illustrated several important features of the yeast cAMP pathway: Pde1p is more important than is Pde2p for controlling the cAMP levels following glucose pulses; the proportion of active PKA is not directly proportional to the cAMP level, allowing PKA to exert negative feedback; negative feedback mechanisms include activating Pde1p and deactivating Ras2 via phosphorylation of Cdc25. The Complete cAMP model is easier to simulate, and although significantly simpler than the existing stochastic one, it recreates cAMP levels and patterns of changes in cAMP levels observed experimentally in vivo in response to glucose addition in wild-type as well as representative mutant strains such as pde1Delta, pde2Delta, cyr1Delta, and others. The complete model is made available in SBML format.

Conclusion: We suggest that the lower number of reactions and parameters makes these models suitable for integrating them with models of metabolism or of the cell cycle in S. cerevisiae. Similar models could be also useful for studies in the human pathogen Candida albicans as well as other less well-characterized fungal species.

Show MeSH

Related in: MedlinePlus

Steady state levels of free C in the PKA models under various cAMP levels. The blue trace shows the simulation of PKA Model B, the red trace – PKA Model C, and the green trace – PKA Model D. Parameters of PKA Model B are the same as in Figure 3. Parameters of PKA Model C are: kA = 8.72 × 10-17; kR = 1000. Parameters of PKA Model D are: kcat = 10-13; KmF = 107; VmaxR = 1000; KmR = 0.01.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2719611&req=5

Figure 4: Steady state levels of free C in the PKA models under various cAMP levels. The blue trace shows the simulation of PKA Model B, the red trace – PKA Model C, and the green trace – PKA Model D. Parameters of PKA Model B are the same as in Figure 3. Parameters of PKA Model C are: kA = 8.72 × 10-17; kR = 1000. Parameters of PKA Model D are: kcat = 10-13; KmF = 107; VmaxR = 1000; KmR = 0.01.

Mentions: We also compared steady state proportions of free catalytic subunit of PKA (Cfree) of each PKA model as a function of the cAMP concentration (Figure 4). At low cAMP concentrations, the Michaelis-Menten based model (PKA Model D) slightly over-estimated, while the mass action based model (PKA Model C) slightly underestimated the level of Cfree, respectively, in comparison to the optimised PKA Model B.


Deterministic mathematical models of the cAMP pathway in Saccharomyces cerevisiae.

Williamson T, Schwartz JM, Kell DB, Stateva L - BMC Syst Biol (2009)

Steady state levels of free C in the PKA models under various cAMP levels. The blue trace shows the simulation of PKA Model B, the red trace – PKA Model C, and the green trace – PKA Model D. Parameters of PKA Model B are the same as in Figure 3. Parameters of PKA Model C are: kA = 8.72 × 10-17; kR = 1000. Parameters of PKA Model D are: kcat = 10-13; KmF = 107; VmaxR = 1000; KmR = 0.01.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2719611&req=5

Figure 4: Steady state levels of free C in the PKA models under various cAMP levels. The blue trace shows the simulation of PKA Model B, the red trace – PKA Model C, and the green trace – PKA Model D. Parameters of PKA Model B are the same as in Figure 3. Parameters of PKA Model C are: kA = 8.72 × 10-17; kR = 1000. Parameters of PKA Model D are: kcat = 10-13; KmF = 107; VmaxR = 1000; KmR = 0.01.
Mentions: We also compared steady state proportions of free catalytic subunit of PKA (Cfree) of each PKA model as a function of the cAMP concentration (Figure 4). At low cAMP concentrations, the Michaelis-Menten based model (PKA Model D) slightly over-estimated, while the mass action based model (PKA Model C) slightly underestimated the level of Cfree, respectively, in comparison to the optimised PKA Model B.

Bottom Line: These results were used to develop the Complete cAMP Model.The Complete cAMP model is easier to simulate, and although significantly simpler than the existing stochastic one, it recreates cAMP levels and patterns of changes in cAMP levels observed experimentally in vivo in response to glucose addition in wild-type as well as representative mutant strains such as pde1Delta, pde2Delta, cyr1Delta, and others.Similar models could be also useful for studies in the human pathogen Candida albicans as well as other less well-characterized fungal species.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Life Sciences, The University of Manchester, Manchester, M13 9PT, UK. Thomas.Williamson@postgrad.manchester.ac.uk

ABSTRACT

Background: Cyclic adenosine monophosphate (cAMP) has a key signaling role in all eukaryotic organisms. In Saccharomyces cerevisiae, it is the second messenger in the Ras/PKA pathway which regulates nutrient sensing, stress responses, growth, cell cycle progression, morphogenesis, and cell wall biosynthesis. A stochastic model of the pathway has been reported.

Results: We have created deterministic mathematical models of the PKA module of the pathway, as well as the complete cAMP pathway. First, a simplified conceptual model was created which reproduced the dynamics of changes in cAMP levels in response to glucose addition in wild-type as well as cAMP phosphodiesterase deletion mutants. This model was used to investigate the role of the regulatory Krh proteins that had not been included previously. The Krh-containing conceptual model reproduced very well the experimental evidence supporting the role of Krh as a direct inhibitor of PKA. These results were used to develop the Complete cAMP Model. Upon simulation it illustrated several important features of the yeast cAMP pathway: Pde1p is more important than is Pde2p for controlling the cAMP levels following glucose pulses; the proportion of active PKA is not directly proportional to the cAMP level, allowing PKA to exert negative feedback; negative feedback mechanisms include activating Pde1p and deactivating Ras2 via phosphorylation of Cdc25. The Complete cAMP model is easier to simulate, and although significantly simpler than the existing stochastic one, it recreates cAMP levels and patterns of changes in cAMP levels observed experimentally in vivo in response to glucose addition in wild-type as well as representative mutant strains such as pde1Delta, pde2Delta, cyr1Delta, and others. The complete model is made available in SBML format.

Conclusion: We suggest that the lower number of reactions and parameters makes these models suitable for integrating them with models of metabolism or of the cell cycle in S. cerevisiae. Similar models could be also useful for studies in the human pathogen Candida albicans as well as other less well-characterized fungal species.

Show MeSH
Related in: MedlinePlus