期刊
PLOS COMPUTATIONAL BIOLOGY
卷 8, 期 4, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1002483
关键词
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资金
- IOP Genomics of Senter Novem
- EU-FP7
- EU-FP7 Yeast Systems Biology Network
- SysMO
- SysMO2 through NWO
- SysMO2 through BBSRC
- Netherlands Consortium for Systems Biology
- Kluyver Centre for Industrial Genomics of Industrial Fermentations
- BBSRC
- EPSRC
- EU-FP7 flagship ITFoM
- University of Groningen
- Biotechnology and Biological Sciences Research Council [BB/C008219/1] Funding Source: researchfish
A decade ago, a team of biochemists including two of us, modeled yeast glycolysis and showed that one of the most studied biochemical pathways could not be quite understood in terms of the kinetic properties of the constituent enzymes as measured in cell extract. Moreover, when the same model was later applied to different experimental steady-state conditions, it often exhibited unrestrained metabolite accumulation. Here we resolve this issue by showing that the results of such ab initio modeling are improved substantially by (i) including appropriate allosteric regulation and (ii) measuring the enzyme kinetic parameters under conditions that resemble the intracellular environment. The following modifications proved crucial: (i) implementation of allosteric regulation of hexokinase and pyruvate kinase, (ii) implementation of V-max values measured under conditions that resembled the yeast cytosol, and (iii) redetermination of the kinetic parameters of glyceraldehyde-3-phosphate dehydrogenase under physiological conditions. Model predictions and experiments were compared under five different conditions of yeast growth and starvation. When either the original model was used (which lacked important allosteric regulation), or the enzyme parameters were measured under conditions that were, as usual, optimal for high enzyme activity, fructose 1,6-bisphosphate and some other glycolytic intermediates tended to accumulate to unrealistically high concentrations. Combining all adjustments yielded an accurate correspondence between model and experiments for all five steady-state and dynamic conditions. This enhances our understanding of in vivo metabolism in terms of in vitro biochemistry.
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