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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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Exometabolome data of Saccharomyces cerevisiae and model predictions.Bars represent experimental and predicted values of exchange fluxes when the glucose uptake was A) 16.5 and B) 11.0 mmol/(gDW⋅h). Capital letters on the x-axis denote the production rates of extracellular metabolites, i.e., E: ethanol production, C: CO2 production, G: glycerol production, A: acetate production, T: trehalose production, L: lactose production, and B: biomass production. GIMME, Gene Inactivity Moderated by Metabolism and Expression; FBA, flux balance analysis; iMAT, integrative metabolic analysis tool.
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pone-0112524-g003: Exometabolome data of Saccharomyces cerevisiae and model predictions.Bars represent experimental and predicted values of exchange fluxes when the glucose uptake was A) 16.5 and B) 11.0 mmol/(gDW⋅h). Capital letters on the x-axis denote the production rates of extracellular metabolites, i.e., E: ethanol production, C: CO2 production, G: glycerol production, A: acetate production, T: trehalose production, L: lactose production, and B: biomass production. GIMME, Gene Inactivity Moderated by Metabolism and Expression; FBA, flux balance analysis; iMAT, integrative metabolic analysis tool.

Mentions: Figures 3A and 3B show experimentally measured exchange fluxes and model predictions at glucose uptake fluxes of 16.5 and 11.0 mmol/(gDW⋅h), respectively. For comparison, we also show the predictions from other methods, including GIMME, FBA, E-Flux, Lee et al.'s algorithm [19], and iMAT, along with those of E-Fmin. We set the maximization of biomass production as the objective of FBA and implemented it in two different forms: without flux minimization (or classical) and with flux minimization. For GIMME and iMAT, we constrained the production of biomass to be above a certain threshold (90% of the maximal growth rate as predicted by FBA), which prevents the predicted growth rate from becoming zero. See materialsandmethods for details on their implementation.


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

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

Exometabolome data of Saccharomyces cerevisiae and model predictions.Bars represent experimental and predicted values of exchange fluxes when the glucose uptake was A) 16.5 and B) 11.0 mmol/(gDW⋅h). Capital letters on the x-axis denote the production rates of extracellular metabolites, i.e., E: ethanol production, C: CO2 production, G: glycerol production, A: acetate production, T: trehalose production, L: lactose production, and B: biomass production. GIMME, Gene Inactivity Moderated by Metabolism and Expression; FBA, flux balance analysis; iMAT, integrative metabolic analysis tool.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0112524-g003: Exometabolome data of Saccharomyces cerevisiae and model predictions.Bars represent experimental and predicted values of exchange fluxes when the glucose uptake was A) 16.5 and B) 11.0 mmol/(gDW⋅h). Capital letters on the x-axis denote the production rates of extracellular metabolites, i.e., E: ethanol production, C: CO2 production, G: glycerol production, A: acetate production, T: trehalose production, L: lactose production, and B: biomass production. GIMME, Gene Inactivity Moderated by Metabolism and Expression; FBA, flux balance analysis; iMAT, integrative metabolic analysis tool.
Mentions: Figures 3A and 3B show experimentally measured exchange fluxes and model predictions at glucose uptake fluxes of 16.5 and 11.0 mmol/(gDW⋅h), respectively. For comparison, we also show the predictions from other methods, including GIMME, FBA, E-Flux, Lee et al.'s algorithm [19], and iMAT, along with those of E-Fmin. We set the maximization of biomass production as the objective of FBA and implemented it in two different forms: without flux minimization (or classical) and with flux minimization. For GIMME and iMAT, we constrained the production of biomass to be above a certain threshold (90% of the maximal growth rate as predicted by FBA), which prevents the predicted growth rate from becoming zero. See materialsandmethods for details on their implementation.

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