MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models
Abstract Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. Erroneous or missing reactions, scattered throughout densely interconnected networks, are a limiting factor in these...
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| Format: | Article |
| Language: | English |
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BMC
2025-03-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03533-6 |
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| author | Devlin C. Moyer Justin Reimertz Daniel Segrè Juan I. Fuxman Bass |
| author_facet | Devlin C. Moyer Justin Reimertz Daniel Segrè Juan I. Fuxman Bass |
| author_sort | Devlin C. Moyer |
| collection | DOAJ |
| description | Abstract Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. Erroneous or missing reactions, scattered throughout densely interconnected networks, are a limiting factor in these applications. We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a suite of algorithms that helps to identify and visualize errors at the level of connected pathways, rather than individual reactions. We show how MACAW highlights inaccuracies of varying severity in manually curated and automatically generated GSMMs for humans, yeast, and bacteria and helps to identify systematic issues to be addressed in future model construction efforts. |
| format | Article |
| id | doaj-art-f89b6bbfb0d94bdcaf95da5f22bc59e9 |
| institution | DOAJ |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-f89b6bbfb0d94bdcaf95da5f22bc59e92025-08-20T02:49:30ZengBMCGenome Biology1474-760X2025-03-0126112610.1186/s13059-025-03533-6MACAW: a method for semi-automatic detection of errors in genome-scale metabolic modelsDevlin C. Moyer0Justin Reimertz1Daniel Segrè2Juan I. Fuxman Bass3Bioinformatics Program, Boston UniversityBioinformatics Program, Boston UniversityBioinformatics Program, Boston UniversityBioinformatics Program, Boston UniversityAbstract Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. Erroneous or missing reactions, scattered throughout densely interconnected networks, are a limiting factor in these applications. We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a suite of algorithms that helps to identify and visualize errors at the level of connected pathways, rather than individual reactions. We show how MACAW highlights inaccuracies of varying severity in manually curated and automatically generated GSMMs for humans, yeast, and bacteria and helps to identify systematic issues to be addressed in future model construction efforts.https://doi.org/10.1186/s13059-025-03533-6Metabolic networksMicrobial metabolismHuman metabolismFlux balance analysisGenome-scale metabolic modelsMetabolic pathway analysis |
| spellingShingle | Devlin C. Moyer Justin Reimertz Daniel Segrè Juan I. Fuxman Bass MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models Genome Biology Metabolic networks Microbial metabolism Human metabolism Flux balance analysis Genome-scale metabolic models Metabolic pathway analysis |
| title | MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models |
| title_full | MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models |
| title_fullStr | MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models |
| title_full_unstemmed | MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models |
| title_short | MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models |
| title_sort | macaw a method for semi automatic detection of errors in genome scale metabolic models |
| topic | Metabolic networks Microbial metabolism Human metabolism Flux balance analysis Genome-scale metabolic models Metabolic pathway analysis |
| url | https://doi.org/10.1186/s13059-025-03533-6 |
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