Enhanced flux potential analysis links changes in enzyme expression to metabolic flux
Abstract Algorithms that constrain metabolic network models with enzyme levels to predict metabolic activity assume that changes in enzyme levels are indicative of flux variations. However, metabolic flux can also be regulated by other mechanisms such as allostery and mass action. To systematically...
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| Format: | Article |
| Language: | English |
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Springer Nature
2025-02-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.1038/s44320-025-00090-9 |
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| author | Xuhang Li Albertha J M Walhout L Safak Yilmaz |
| author_facet | Xuhang Li Albertha J M Walhout L Safak Yilmaz |
| author_sort | Xuhang Li |
| collection | DOAJ |
| description | Abstract Algorithms that constrain metabolic network models with enzyme levels to predict metabolic activity assume that changes in enzyme levels are indicative of flux variations. However, metabolic flux can also be regulated by other mechanisms such as allostery and mass action. To systematically explore the relationship between fluctuations in enzyme expression and flux, we combine available yeast proteomic and fluxomic data to reveal that flux changes can be best predicted from changes in enzyme levels of pathways, rather than the whole network or only cognate reactions. We implement this principle in an ‘enhanced flux potential analysis’ (eFPA) algorithm that integrates enzyme expression data with metabolic network architecture to predict relative flux levels of reactions including those regulated by other mechanisms. Applied to human data, eFPA consistently predicts tissue metabolic function using either proteomic or transcriptomic data. Additionally, eFPA efficiently handles data sparsity and noisiness, generating robust flux predictions with single-cell gene expression data. Our approach outperforms alternatives by striking an optimal balance, evaluating enzyme expression at pathway level, rather than either single-reaction or whole-network levels. |
| format | Article |
| id | doaj-art-390b1c207ca541bfa15cbab1465f41c1 |
| institution | DOAJ |
| issn | 1744-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-390b1c207ca541bfa15cbab1465f41c12025-08-20T03:03:20ZengSpringer NatureMolecular Systems Biology1744-42922025-02-0121441344510.1038/s44320-025-00090-9Enhanced flux potential analysis links changes in enzyme expression to metabolic fluxXuhang Li0Albertha J M Walhout1L Safak Yilmaz2Department of Systems Biology, University of Massachusetts Chan Medical SchoolDepartment of Systems Biology, University of Massachusetts Chan Medical SchoolDepartment of Systems Biology, University of Massachusetts Chan Medical SchoolAbstract Algorithms that constrain metabolic network models with enzyme levels to predict metabolic activity assume that changes in enzyme levels are indicative of flux variations. However, metabolic flux can also be regulated by other mechanisms such as allostery and mass action. To systematically explore the relationship between fluctuations in enzyme expression and flux, we combine available yeast proteomic and fluxomic data to reveal that flux changes can be best predicted from changes in enzyme levels of pathways, rather than the whole network or only cognate reactions. We implement this principle in an ‘enhanced flux potential analysis’ (eFPA) algorithm that integrates enzyme expression data with metabolic network architecture to predict relative flux levels of reactions including those regulated by other mechanisms. Applied to human data, eFPA consistently predicts tissue metabolic function using either proteomic or transcriptomic data. Additionally, eFPA efficiently handles data sparsity and noisiness, generating robust flux predictions with single-cell gene expression data. Our approach outperforms alternatives by striking an optimal balance, evaluating enzyme expression at pathway level, rather than either single-reaction or whole-network levels.https://doi.org/10.1038/s44320-025-00090-9Enzyme ExpressionMetabolic Network ModelMetabolic FluxFlux Potential AnalysisSingle-cell Data |
| spellingShingle | Xuhang Li Albertha J M Walhout L Safak Yilmaz Enhanced flux potential analysis links changes in enzyme expression to metabolic flux Molecular Systems Biology Enzyme Expression Metabolic Network Model Metabolic Flux Flux Potential Analysis Single-cell Data |
| title | Enhanced flux potential analysis links changes in enzyme expression to metabolic flux |
| title_full | Enhanced flux potential analysis links changes in enzyme expression to metabolic flux |
| title_fullStr | Enhanced flux potential analysis links changes in enzyme expression to metabolic flux |
| title_full_unstemmed | Enhanced flux potential analysis links changes in enzyme expression to metabolic flux |
| title_short | Enhanced flux potential analysis links changes in enzyme expression to metabolic flux |
| title_sort | enhanced flux potential analysis links changes in enzyme expression to metabolic flux |
| topic | Enzyme Expression Metabolic Network Model Metabolic Flux Flux Potential Analysis Single-cell Data |
| url | https://doi.org/10.1038/s44320-025-00090-9 |
| work_keys_str_mv | AT xuhangli enhancedfluxpotentialanalysislinkschangesinenzymeexpressiontometabolicflux AT alberthajmwalhout enhancedfluxpotentialanalysislinkschangesinenzymeexpressiontometabolicflux AT lsafakyilmaz enhancedfluxpotentialanalysislinkschangesinenzymeexpressiontometabolicflux |