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Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle.

Song HS, Reifman J, Wallqvist A - PLoS ONE (2014)

Bottom Line: The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context.We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network.In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values.

View Article: PubMed Central - PubMed

Affiliation: Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America.

ABSTRACT
Prediction of possible flux distributions in a metabolic network provides detailed phenotypic information that links metabolism to cellular physiology. To estimate metabolic steady-state fluxes, the most common approach is to solve a set of macroscopic mass balance equations subjected to stoichiometric constraints while attempting to optimize an assumed optimal objective function. This assumption is justifiable in specific cases but may be invalid when tested across different conditions, cell populations, or other organisms. With an aim to providing a more consistent and reliable prediction of flux distributions over a wide range of conditions, in this article we propose a framework that uses the flux minimization principle to predict active metabolic pathways from mRNA expression data. The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context. We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network. In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values. In contrast to other approaches, our method was able to provide quantitative predictions for both model organisms under a variety of conditions. Our approach requires no prior knowledge or assumption of a context-specific metabolic functionality and does not require trial-and-error parameter adjustments. Thus, our framework is of general applicability for modeling the transcription-dependent metabolism of bacteria and yeasts.

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E-Fmin predictions of intracellular metabolic fluxes in wild-type Escherichia coli.Shown is a comparison of the metabolic fluxes at varied dilution rates (D; x-axis) as measured using 13C-metabolic flux analysis (13C-MFA; y-axis) and predicted by E-Fmin (z-axis). In all cases, flux comparisons were made using their relative values normalized with the glucose uptake flux of 100 mmol/(gDW⋅h).
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pone-0112524-g004: E-Fmin predictions of intracellular metabolic fluxes in wild-type Escherichia coli.Shown is a comparison of the metabolic fluxes at varied dilution rates (D; x-axis) as measured using 13C-metabolic flux analysis (13C-MFA; y-axis) and predicted by E-Fmin (z-axis). In all cases, flux comparisons were made using their relative values normalized with the glucose uptake flux of 100 mmol/(gDW⋅h).

Mentions: Figure 4 shows intracellular metabolic fluxes of wild-type E. coli strains obtained from E-Fmin and 13C-MFA at respective dilution rates. Predicted fluxes show good matches with 13C-MFA data in all cases. Table 2 shows ρ with an average value of 0.91 along with P values. Low P values indicate that the correlations obtained from E-Fmin are statistically significant. Table S1 shows relatively low values of SSE with an average value of 57.8. We observed that E-Fmin underestimated the CO2 production rate in all cases.


Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle.

Song HS, Reifman J, Wallqvist A - PLoS ONE (2014)

E-Fmin predictions of intracellular metabolic fluxes in wild-type Escherichia coli.Shown is a comparison of the metabolic fluxes at varied dilution rates (D; x-axis) as measured using 13C-metabolic flux analysis (13C-MFA; y-axis) and predicted by E-Fmin (z-axis). In all cases, flux comparisons were made using their relative values normalized with the glucose uptake flux of 100 mmol/(gDW⋅h).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4232356&req=5

pone-0112524-g004: E-Fmin predictions of intracellular metabolic fluxes in wild-type Escherichia coli.Shown is a comparison of the metabolic fluxes at varied dilution rates (D; x-axis) as measured using 13C-metabolic flux analysis (13C-MFA; y-axis) and predicted by E-Fmin (z-axis). In all cases, flux comparisons were made using their relative values normalized with the glucose uptake flux of 100 mmol/(gDW⋅h).
Mentions: Figure 4 shows intracellular metabolic fluxes of wild-type E. coli strains obtained from E-Fmin and 13C-MFA at respective dilution rates. Predicted fluxes show good matches with 13C-MFA data in all cases. Table 2 shows ρ with an average value of 0.91 along with P values. Low P values indicate that the correlations obtained from E-Fmin are statistically significant. Table S1 shows relatively low values of SSE with an average value of 57.8. We observed that E-Fmin underestimated the CO2 production rate in all cases.

Bottom Line: The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context.We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network.In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values.

View Article: PubMed Central - PubMed

Affiliation: Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America.

ABSTRACT
Prediction of possible flux distributions in a metabolic network provides detailed phenotypic information that links metabolism to cellular physiology. To estimate metabolic steady-state fluxes, the most common approach is to solve a set of macroscopic mass balance equations subjected to stoichiometric constraints while attempting to optimize an assumed optimal objective function. This assumption is justifiable in specific cases but may be invalid when tested across different conditions, cell populations, or other organisms. With an aim to providing a more consistent and reliable prediction of flux distributions over a wide range of conditions, in this article we propose a framework that uses the flux minimization principle to predict active metabolic pathways from mRNA expression data. The proposed algorithm minimizes a weighted sum of flux magnitudes, while biomass production can be bounded to fit an ample range from very low to very high values according to the analyzed context. We have formulated the flux weights as a function of the corresponding enzyme reaction's gene expression value, enabling the creation of context-specific fluxes based on a generic metabolic network. In case studies of wild-type Saccharomyces cerevisiae, and wild-type and mutant Escherichia coli strains, our method achieved high prediction accuracy, as gauged by correlation coefficients and sums of squared error, with respect to the experimentally measured values. In contrast to other approaches, our method was able to provide quantitative predictions for both model organisms under a variety of conditions. Our approach requires no prior knowledge or assumption of a context-specific metabolic functionality and does not require trial-and-error parameter adjustments. Thus, our framework is of general applicability for modeling the transcription-dependent metabolism of bacteria and yeasts.

Show MeSH
Related in: MedlinePlus