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Systematic construction of kinetic models from genome-scale metabolic networks.

Stanford NJ, Lubitz T, Smallbone K, Klipp E, Mendes P, Liebermeister W - PLoS ONE (2013)

Bottom Line: The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models.The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state.The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, United Kingdom.

ABSTRACT
The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

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Fluxes and control coefficients in the yeast metabolic model.(A) Fluxes obtained from Geometric FBA. Only selected reactions with large fluxes are depicted, co-substrates are not shown (flux directions and magnitudes shown by arrows). (B) Flux control coefficients. Top: Control exerted by the glucose transporter (GluT). Unscaled flux control coefficients are shown in shades of blue (positive values) and red (negative values). Bottom: control exerted by the biomass production reaction. High-flux reactions respond most strongly: an increased glucose import increases the glycolytic flux, while increased biomass production directs fluxes to other pathways and thereby decreases the glycolytic flux. Flux control coefficients for a model with allosteric regulation are shown in Figures G and H in File S1.
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pone-0079195-g003: Fluxes and control coefficients in the yeast metabolic model.(A) Fluxes obtained from Geometric FBA. Only selected reactions with large fluxes are depicted, co-substrates are not shown (flux directions and magnitudes shown by arrows). (B) Flux control coefficients. Top: Control exerted by the glucose transporter (GluT). Unscaled flux control coefficients are shown in shades of blue (positive values) and red (negative values). Bottom: control exerted by the biomass production reaction. High-flux reactions respond most strongly: an increased glucose import increases the glycolytic flux, while increased biomass production directs fluxes to other pathways and thereby decreases the glycolytic flux. Flux control coefficients for a model with allosteric regulation are shown in Figures G and H in File S1.

Mentions: As an example case for our workflow, we generated a large-scale kinetic model of yeast metabolism. Sacharomyces cerevisiae is one of the most studied eukaryotic model organisms, and a comparatively large amount of data is available for fluxes, concentrations, and equilibrium constants. The model generation process is described in detail in the Methods section. The final model contains 285 flux-carrying reactions and 294 metabolites. As expected, it displays a large flux through the glycolytic pathway and a large production of ethanol (see Fig. 3 (b)). This behaviour is primarily defined by the flux input from the kinetic models used and reflects the expected behaviour when growing the organism in the laboratory. A detailed list of flux values is given in S4.


Systematic construction of kinetic models from genome-scale metabolic networks.

Stanford NJ, Lubitz T, Smallbone K, Klipp E, Mendes P, Liebermeister W - PLoS ONE (2013)

Fluxes and control coefficients in the yeast metabolic model.(A) Fluxes obtained from Geometric FBA. Only selected reactions with large fluxes are depicted, co-substrates are not shown (flux directions and magnitudes shown by arrows). (B) Flux control coefficients. Top: Control exerted by the glucose transporter (GluT). Unscaled flux control coefficients are shown in shades of blue (positive values) and red (negative values). Bottom: control exerted by the biomass production reaction. High-flux reactions respond most strongly: an increased glucose import increases the glycolytic flux, while increased biomass production directs fluxes to other pathways and thereby decreases the glycolytic flux. Flux control coefficients for a model with allosteric regulation are shown in Figures G and H in File S1.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3852239&req=5

pone-0079195-g003: Fluxes and control coefficients in the yeast metabolic model.(A) Fluxes obtained from Geometric FBA. Only selected reactions with large fluxes are depicted, co-substrates are not shown (flux directions and magnitudes shown by arrows). (B) Flux control coefficients. Top: Control exerted by the glucose transporter (GluT). Unscaled flux control coefficients are shown in shades of blue (positive values) and red (negative values). Bottom: control exerted by the biomass production reaction. High-flux reactions respond most strongly: an increased glucose import increases the glycolytic flux, while increased biomass production directs fluxes to other pathways and thereby decreases the glycolytic flux. Flux control coefficients for a model with allosteric regulation are shown in Figures G and H in File S1.
Mentions: As an example case for our workflow, we generated a large-scale kinetic model of yeast metabolism. Sacharomyces cerevisiae is one of the most studied eukaryotic model organisms, and a comparatively large amount of data is available for fluxes, concentrations, and equilibrium constants. The model generation process is described in detail in the Methods section. The final model contains 285 flux-carrying reactions and 294 metabolites. As expected, it displays a large flux through the glycolytic pathway and a large production of ethanol (see Fig. 3 (b)). This behaviour is primarily defined by the flux input from the kinetic models used and reflects the expected behaviour when growing the organism in the laboratory. A detailed list of flux values is given in S4.

Bottom Line: The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models.The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state.The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, United Kingdom.

ABSTRACT
The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

Show MeSH