Ove Øyås

The paper introduces minimal metabolic pathways to enable scalable analysis of metabolic subnetworks in organisms and communities.

This paper presents an updated genome-scale metabolic model for Ruminiclostridium cellulolyticum to simulate lignocellulosic biomass fermentation.

CompareM2 is a pipeline that compares microbial genomes to generate reports on quality, annotation, taxonomy, function, and phylogeny.

The paper identifies two rumen community types linked to protozoal populations and shows their impact on microbial interactions and metabolite profiles.

The paper analyzes metabolite exchanges in microbial metabolic models using pathway definitions, revealing a hierarchy among exchanges and consistent frequency patterns across methods.

This paper develops a model to predict the nutritional and environmental impacts of alternative feeds for Atlantic salmon, identifying sustainable feed formulations that optimize efficiency and reduce CO2 footprint.

This paper trains an artificial neural network to predict microbial genome-scale metabolic networks directly from 16S rRNA gene sequences, enabling metabolic analysis of microbes without full genomes.

The paper evaluates six methods for creating context-specific metabolic models of Atlantic salmon using transcriptomics data, finding that some methods better capture context-specific metabolism than others.

SALARECON links the Atlantic salmon genome to metabolic fluxes and growth, showing that marine feed efficiency can be achieved by supplementing plant- and insect-based feeds with specific amino acids.

This paper introduces minimal pathways for scalable metabolic pathway analysis in genome-scale models, improving gene importance predictions and metabolite exchange analysis in microbial and mammalian systems.

The paper integrates metabolomics and genomics data to identify functional SNPs in Mycobacterium tuberculosis strains, revealing genetic basis for antibiotic sensitivity.