Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeast
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Abstract
We present the quantification and kinetic characterisation of the enzymes of the pentose phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical model that describes the dynamics of this system and allows for the predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models.
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2013. Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeast. PeerJ PrePrints 1:e146v2 https://doi.org/10.7287/peerj.preprints.146v2Author comment
Same as version 1, except for updated affiliations for one of the authors.
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Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Hanan L. Messiha conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper.
Edward Kent performed the experiments, analyzed the data, wrote the paper.
Naglis Malys conceived and designed the experiments, performed the experiments, contributed reagents/materials/analysis tools, wrote the paper.
Kathleen M. Carroll performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper.
Pedro Mendes conceived and designed the experiments, wrote the paper.
Kieran Smallbone conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper.
Grant Disclosures
The following grant information was disclosed by the authors:
BBSRC and EPSRC grant BB/C008219, Pfizer and BBSRC grant BB/G529859,
EU FP7 grant 289434,
BBSRC grants BB/J019259 and BB/K019783,
NIH grant R01-GM080219
Data Deposition
The following information was supplied regarding the deposition of related data:
BioModels database, MODEL1311290000
BioModels database, MODEL1311290001
Funding
We thank the BBSRC and EPSRC for their financial support of this work (grant BB/C008219/1) through the Manchester Centre for Integrative Systems Biology. EK was funded by Pfizer Inc. and BBSRC (grant BB/G529859). KS and PM are grateful for the financial support of the EU FP7 project BioPreDyn (grant 289434). PM was also funded by BBSRC (grants BB/J019259 and BB/K019783) and NIH (R01-GM080219). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
