Extracting functionally accurate context-specific models of Atlantic salmon metabolism
Abstract Constraint-based models (CBMs) are used to study metabolic network structure and function in organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture differences in reaction activities, whi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-023-00280-x |
| _version_ | 1797817897086615552 |
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| author | Håvard Molversmyr Ove Øyås Filip Rotnes Jon Olav Vik |
| author_facet | Håvard Molversmyr Ove Øyås Filip Rotnes Jon Olav Vik |
| author_sort | Håvard Molversmyr |
| collection | DOAJ |
| description | Abstract Constraint-based models (CBMs) are used to study metabolic network structure and function in organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture differences in reaction activities, which, in turn, determine metabolic capabilities, between cell types, tissues, environments, or other conditions. Only a subset of a CBM’s metabolic reactions and capabilities are likely to be active in any given context, and several methods have therefore been developed to extract context-specific models from generic CBMs through integration of omics data. We tested the ability of six model extraction methods (MEMs) to create functionally accurate context-specific models of Atlantic salmon using a generic CBM (SALARECON) and liver transcriptomics data from contexts differing in water salinity (life stage) and dietary lipids. Three MEMs (iMAT, INIT, and GIMME) outperformed the others in terms of functional accuracy, which we defined as the extracted models’ ability to perform context-specific metabolic tasks inferred directly from the data, and one MEM (GIMME) was faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling better captures salmon metabolism. Thus, we demonstrate that results from human studies also hold for a non-mammalian animal and major livestock species. |
| first_indexed | 2024-03-13T09:00:14Z |
| format | Article |
| id | doaj.art-32855d39937b479c8e68371796922405 |
| institution | Directory Open Access Journal |
| issn | 2056-7189 |
| language | English |
| last_indexed | 2024-03-13T09:00:14Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj.art-32855d39937b479c8e683717969224052023-05-28T11:20:28ZengNature Portfolionpj Systems Biology and Applications2056-71892023-05-019111010.1038/s41540-023-00280-xExtracting functionally accurate context-specific models of Atlantic salmon metabolismHåvard Molversmyr0Ove Øyås1Filip Rotnes2Jon Olav Vik3Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life SciencesFaculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life SciencesFaculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life SciencesFaculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life SciencesAbstract Constraint-based models (CBMs) are used to study metabolic network structure and function in organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture differences in reaction activities, which, in turn, determine metabolic capabilities, between cell types, tissues, environments, or other conditions. Only a subset of a CBM’s metabolic reactions and capabilities are likely to be active in any given context, and several methods have therefore been developed to extract context-specific models from generic CBMs through integration of omics data. We tested the ability of six model extraction methods (MEMs) to create functionally accurate context-specific models of Atlantic salmon using a generic CBM (SALARECON) and liver transcriptomics data from contexts differing in water salinity (life stage) and dietary lipids. Three MEMs (iMAT, INIT, and GIMME) outperformed the others in terms of functional accuracy, which we defined as the extracted models’ ability to perform context-specific metabolic tasks inferred directly from the data, and one MEM (GIMME) was faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling better captures salmon metabolism. Thus, we demonstrate that results from human studies also hold for a non-mammalian animal and major livestock species.https://doi.org/10.1038/s41540-023-00280-x |
| spellingShingle | Håvard Molversmyr Ove Øyås Filip Rotnes Jon Olav Vik Extracting functionally accurate context-specific models of Atlantic salmon metabolism npj Systems Biology and Applications |
| title | Extracting functionally accurate context-specific models of Atlantic salmon metabolism |
| title_full | Extracting functionally accurate context-specific models of Atlantic salmon metabolism |
| title_fullStr | Extracting functionally accurate context-specific models of Atlantic salmon metabolism |
| title_full_unstemmed | Extracting functionally accurate context-specific models of Atlantic salmon metabolism |
| title_short | Extracting functionally accurate context-specific models of Atlantic salmon metabolism |
| title_sort | extracting functionally accurate context specific models of atlantic salmon metabolism |
| url | https://doi.org/10.1038/s41540-023-00280-x |
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