Selecting methods for draft GEM generation in multicellular eukaryotes: a comparative analysis

Motivated by the applications of genome-scale metabolic models (GEMs) for biological discovery and metabolic engineering, several approaches have been developed for automatic generation of draft GEMs. However, most of these methods are not optimized for their use in multicellular eukaryotes and their performance for this task is unclear. In this work we present a comparative analysis of seven automated reconstruction tools (AuReMe, CarveMe, merlin, ModelSEED, Pathway Tools, RAVEN and Reconstructor) applied to three multicellular eukaryotes: the mosquito Aedes aegypti, the CHO (Chinese Hamster Ovary) cell line from Cricetulus griseus and the brown algae Ectocarpus siliculosus. Evaluation of these tools was based on metrics for network size, functionality, consistency, representation of organelle-specific functions and organism-specific metabolites, annotation quality and execution time. We find that methods differ strongly in a trade-off between model functionality and representation of eukaryotic features such as compartmentalization and organism-specific metabolism, with no single approach excelling at both. Our work aims at providing a practical resource to guide researchers in selecting methods for draft generation tailored to organism characteristics and research goals.