Abstract
Motivation Constraint-based models (CBMs) are used to study the metabolic networks of organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture metabolic differences between cell types, tissues, environments, or other conditions. However, only a subset of reactions in a model 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.
Results 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. Reaction contents and metabolic task feasibility predictions of context-specific CBMs were mainly determined by the MEM that was used, but life stage explained significant variance in both contents and predictions for some MEMs. Three MEMs clearly outperformed the others in terms of their ability to capture context-specific metabolic activities inferred directly from the data, and one of these (GIMME) was much faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling captures more realistic representations of Atlantic salmon metabolism.
Contact jon.vik{at}nmbu.no
Competing Interest Statement
The authors have declared no competing interest.
