Nag2011_ChloroplasticStarchDegradation

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This model is from the article:
Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation.
Nag A, Lunacek M, Graf PA, Chang CH. BMC Syst Biol.2011 Jun 18;5:94. 21682905,
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 kcat 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 kcat 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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Format
SBML (L3V1)
Related Publication
  • Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation.
  • Nag A, Lunacek M, Graf PA, Chang CH
  • BMC systems biology , 1/ 2011 , Volume 5 , pages: 94
  • Computational Sciences Center, National Renewable Energy Laboratory, 1617 Cole Boulevard, MS 1608, Golden, CO 80401, USA.
  • 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.
Contributors
Krishna Kumar Tiwari, Ambarish Nag

Metadata information

is
BioModels Database MODEL1106030000
BioModels Database BIOMD0000000353
BioModels Database BIOMD0000000353
BioModels Database MODEL1106030000
isDescribedBy
PubMed 21682905
hasTaxon
isVersionOf
Gene Ontology starch catabolic process
hasProperty
Mathematical Modelling Ontology Ordinary differential equation model

Curation status
Curated


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Model files

BIOMD0000000353_url.xml SBML L3V1 representation of Nag2011_ChloroplasticStarchDegradation 226.49 KB Preview | Download

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  • Model originally submitted by : Ambarish Nag
  • Submitted: Jun 3, 2011 7:18:53 PM
  • Last Modified: May 16, 2019 10:15:00 AM
Revisions
  • Version: 4 public model Download this version
    • Submitted on: May 16, 2019 10:15:00 AM
    • Submitted by: Krishna Kumar Tiwari
    • With comment: Automatically added model identifier BIOMD0000000353
  • Version: 2 public model Download this version
    • Submitted on: Apr 1, 2014 2:29:14 PM
    • Submitted by: Ambarish Nag
    • With comment: Current version of Nag2011_ChloroplasticStarchDegradation
  • Version: 1 public model Download this version
    • Submitted on: Jun 3, 2011 7:18:53 PM
    • Submitted by: Ambarish Nag
    • With comment: Original import of BIOMD0000000353.xml.origin
Curator's comment:
(added: 15 Aug 2011, 15:13:24, updated: 15 Aug 2011, 15:13:24)
Figure 7 and Figure 11 of the reference article has been reproduced here. The concentrations of Beta-amylase and Isoamylase are in gram/L in the model. So, the units for x-axis (Beta-amylase) is in gram/L in the reproduced figure whereas in the article, it is in microM. The model was integrated and simulated using Copasi v4.7 (Build 34).