Eivind Almaas

The paper shows that genetic interactions in yeast change with protein availability and nutrient uptake, revealing new insights into how metabolic constraints affect epistasis.

The paper aligns metal-binding sites across proteins to construct a network revealing coordination chemistry patterns and drug off-target interactions.

This study develops a parallelized ALE platform to evolve wine yeasts with enhanced metabolic traits, finding that expanded replicates and lineages improve the chances of evolving improved strains with desirable industrial performance.

The paper uses genome-scale metabolic models to identify metabolic differences between non-Saccharomyces yeasts, finding that Complex I in the electron transport chain distinguishes Metschnikowia pulcherrima from other species.

The paper develops an evolutionary algorithm to improve parameter inference for enzyme and temperature constrained genome-scale models, showing that the Bayesian approach is unstable and that more data is needed to narrow predictions.

Standard-GEM provides a template for reproducible genome-scale metabolic model development following FAIR principles and software best practices.

The paper develops decFBAecc, an extended dynamic flux balance analysis method that incorporates enzyme change constraints, and validates it using diauxic growth experiments in E. coli.

The paper extends an enzyme-constrained GEM framework to include amino acid usage in yeast, showing how metabolic proteome demands affect amino acid availability and metabolic robustness.

The paper constructs a SNP-based disease network from UK Biobank data and maps it to co-occurring diseases in the HUNT study, revealing patterns of disease progression and mortality associations.

The paper introduces csdR, an R package for differential co-expression analysis, which improves performance and usability compared to existing implementations.

The paper develops a pipeline for experimental biomass quantification in E. coli, improving coverage and resolution, and evaluates its impact on metabolic model predictions.

This paper introduces two computational methods, BTW and HIP, to improve genome-scale metabolic models by accounting for environmental effects on biomass composition, showing differences in model performance and phenotypic predictions.

This paper predicts the evolution of fitness-costly traits by designing selection niches using metabolic models and validates it in yeast for increased aroma compound secretion.

This paper reconstructs a high-quality genome-scale metabolic model for Aurantiochytrium sp. T66, improving upon previous models and validating its accuracy with experimental data.

The paper develops a pipeline to automatically reconstruct metabolic pathways from biosynthetic gene clusters, demonstrating its utility in genome-scale metabolic modeling for heterologous compound production.

The paper constructs genome-scale metabolic reconstructions for 975 organisms using AutoKEGGRec and uses them to generate phylogenetic trees that closely resemble the Tree of Life.

The paper compares metabolic differences between Streptomyces coelicolor strains M1152 and M145 using omics data and a genome-scale model to identify metabolic changes affecting heterologous compound production.

The paper uses proteomic data from sticklebacks to identify key proteins and functions associated with environmental adaptation through system-level analysis.