Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data
IntroductionThe availability of large-scale multi-omic data has revolution-ized the study of cellular machinery, enabling a systematic understanding of biological processes. However, the integration of these datasets into Genome-Scale Models of Metabolism (GEMs) re-mains underexplored. Existing meth...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Systems Biology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsysb.2024.1500710/full |
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| author | Andrea Angarita-Rodríguez Andrea Angarita-Rodríguez Andrea Angarita-Rodríguez Nicolás Mendoza-Mejía Nicolás Mendoza-Mejía Janneth González Jason Papin Jason Papin Jason Papin Andrés Felipe Aristizábal Andrés Pinzón |
| author_facet | Andrea Angarita-Rodríguez Andrea Angarita-Rodríguez Andrea Angarita-Rodríguez Nicolás Mendoza-Mejía Nicolás Mendoza-Mejía Janneth González Jason Papin Jason Papin Jason Papin Andrés Felipe Aristizábal Andrés Pinzón |
| author_sort | Andrea Angarita-Rodríguez |
| collection | DOAJ |
| description | IntroductionThe availability of large-scale multi-omic data has revolution-ized the study of cellular machinery, enabling a systematic understanding of biological processes. However, the integration of these datasets into Genome-Scale Models of Metabolism (GEMs) re-mains underexplored. Existing methods often link transcriptome and proteome data independently to reaction boundaries, providing models with estimated maximum reaction rates based on individual datasets. This independent approach, however, introduces uncertainties and inaccuracies.MethodsTo address these challenges, we applied a principal component analysis (PCA)-based approach to integrate transcriptome and proteome data. This method facilitates the reconstruction of context-specific models grounded in multi-omics data, enhancing their biological relevance and predictive capacity.ResultsUsing this approach, we successfully reconstructed an astrocyte GEM with improved prediction capabilities compared to state-of-the-art models available in the literature.DiscussionThese advancements underscore the potential of multi-omic inte-gration to refine metabolic modeling and its critical role in studying neurodegeneration and developing effective therapies. |
| format | Article |
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| institution | Kabale University |
| issn | 2674-0702 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Systems Biology |
| spelling | doaj-art-40ce49bea47a4dd890959c7ad552f6112025-01-03T06:47:24ZengFrontiers Media S.A.Frontiers in Systems Biology2674-07022025-01-01410.3389/fsysb.2024.15007101500710Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic dataAndrea Angarita-Rodríguez0Andrea Angarita-Rodríguez1Andrea Angarita-Rodríguez2Nicolás Mendoza-Mejía3Nicolás Mendoza-Mejía4Janneth González5Jason Papin6Jason Papin7Jason Papin8Andrés Felipe Aristizábal9Andrés Pinzón10Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, ColombiaLaboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, Bogotá, ColombiaDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, United StatesDepartamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, ColombiaLaboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, Bogotá, ColombiaDepartamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, ColombiaDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, United StatesDepartment of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, United StatesDepartment of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, United StatesDepartamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, ColombiaLaboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, Bogotá, ColombiaIntroductionThe availability of large-scale multi-omic data has revolution-ized the study of cellular machinery, enabling a systematic understanding of biological processes. However, the integration of these datasets into Genome-Scale Models of Metabolism (GEMs) re-mains underexplored. Existing methods often link transcriptome and proteome data independently to reaction boundaries, providing models with estimated maximum reaction rates based on individual datasets. This independent approach, however, introduces uncertainties and inaccuracies.MethodsTo address these challenges, we applied a principal component analysis (PCA)-based approach to integrate transcriptome and proteome data. This method facilitates the reconstruction of context-specific models grounded in multi-omics data, enhancing their biological relevance and predictive capacity.ResultsUsing this approach, we successfully reconstructed an astrocyte GEM with improved prediction capabilities compared to state-of-the-art models available in the literature.DiscussionThese advancements underscore the potential of multi-omic inte-gration to refine metabolic modeling and its critical role in studying neurodegeneration and developing effective therapies.https://www.frontiersin.org/articles/10.3389/fsysb.2024.1500710/fullgenome-scale metabolic modelstranscriptomeproteomedimensional reductionastrocyte |
| spellingShingle | Andrea Angarita-Rodríguez Andrea Angarita-Rodríguez Andrea Angarita-Rodríguez Nicolás Mendoza-Mejía Nicolás Mendoza-Mejía Janneth González Jason Papin Jason Papin Jason Papin Andrés Felipe Aristizábal Andrés Pinzón Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data Frontiers in Systems Biology genome-scale metabolic models transcriptome proteome dimensional reduction astrocyte |
| title | Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data |
| title_full | Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data |
| title_fullStr | Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data |
| title_full_unstemmed | Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data |
| title_short | Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data |
| title_sort | improvement in the prediction power of an astrocyte genome scale metabolic model using multi omic data |
| topic | genome-scale metabolic models transcriptome proteome dimensional reduction astrocyte |
| url | https://www.frontiersin.org/articles/10.3389/fsysb.2024.1500710/full |
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