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Variation in simulated steady state concentration of chloroplast stromal glucose with the β-amylase turnover number for starch hydrolysis. The dependence of the simulated chloroplastic glucose steady state concentration on β-amylase turnover number for starch hydrolysis () is seen to be linear over a range from ~0.03 to 0.3 s-1.
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Figure 7: Variation in simulated steady state concentration of chloroplast stromal glucose with the β-amylase turnover number for starch hydrolysis. The dependence of the simulated chloroplastic glucose steady state concentration on β-amylase turnover number for starch hydrolysis () is seen to be linear over a range from ~0.03 to 0.3 s-1.

Mentions: In other words, Si varies as the ρith power of pm. Because the response coefficient of chloroplastic glucose with respect to is positive and very close to unity, if is varied while keeping all other parameters fixed the steady-state concentration of chloroplastic glucose should increase linearly with the parameter value. This prediction is validated by the plot in Figure 7, where the steady-state concentration of glucose in the chloroplast stroma is shown to vary linearly with β-amylase .

Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation

Nag A, Lunacek M, Graf PA, Chang CH - BMC Syst Biol (2011)

Bottom Line: Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and β-amylase k(cat) and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole.The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways.In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions.

Affiliation: Computational Sciences Center, National Renewable Energy Laboratory, 1617 Cole Boulevard, MS 1608, Golden, CO 80401, USA.

ABSTRACT

Background: Higher plants and algae are able to fix atmospheric carbon dioxide through photosynthesis and store this fixed carbon in large quantities as starch, which can be hydrolyzed into sugars serving as feedstock for fermentation to biofuels and precursors. Rational engineering of carbon flow in plant cells requires a greater understanding of how starch breakdown fluxes respond to variations in enzyme concentrations, kinetic parameters, and metabolite concentrations. We have therefore developed and simulated a detailed kinetic ordinary differential equation model of the degradation pathways for starch synthesized in plants and green algae, which to our knowledge is the most complete such model reported to date.

Results: Simulation with 9 internal metabolites and 8 external metabolites, the concentrations of the latter fixed at reasonable biochemical values, leads to a single reference solution showing β-amylase activity to be the rate-limiting step in carbon flow from starch degradation. Additionally, the response coefficients for stromal glucose to the glucose transporter k(cat) and KM are substantial, whereas those for cytosolic glucose are not, consistent with a kinetic bottleneck due to transport. Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and β-amylase k(cat) and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole. The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways. Exploration of the steady-state solution space with respect to concentrations of 6 external metabolites and 8 dynamic metabolite concentrations show that stromal metabolism is strongly coupled to starch levels, and that transport between compartments serves to lower coupling between metabolic subsystems in different compartments.

Conclusions: We find that in the reference steady state, starch cleavage is the most significant determinant of carbon flux, with turnover of oligosaccharides playing a secondary role. Independence of stationary point with respect to initial dynamic variable values confirms a unique stationary point in the phase space of dynamically varying concentrations of the model network. Stromal maltooligosaccharide metabolism was highly coupled to the available starch concentration. From the most highly converged trajectories, distances between unique fixed points of phase spaces show that cytosolic maltose levels depend on the total concentrations of arabinogalactan and glucose present in the cytosol. In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions.

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