Speaker
Description
The genus Sulfolobus includes some of the most extensively studied thermophilic Archaea and is notable for its metabolic versatility and relevance to industrial biotechnology. Genome-scale metabolic models (GEMs) enable predictive analyses of metabolism, but their reconstruction is complex, time consuming and sensitive to environmental assumptions, particularly during gap filling.
Here, we reconstructed the Sulfolobus pan-reactome comprising 76 GEMs derived from publicly available genomes. Models were generated using two different growth media to assess how environmental assumptions influence model structure and predictions. While reaction content differed by less than 1% across models, media composition strongly affected growth feasibility, auxotrophic behavior, and predicted growth rates. Predicted growth rates deviated from experimental values by 4–24%, despite minimal differences in global predictive accuracy.
This work presents the first genus-wide Sulfolobus pan-reactome and a curated genome-scale model for S.acidocaldarius, highlighting how reconstruction assumptions can bias inferred metabolic capabilities in archaeal GEMs.
