Ulf W. Liebal

The paper introduces FROG analysis to standardize reproducibility assessments of genome-scale metabolic models, resulting in full reproducibility of all tested models.

Yeast9 improves yeast metabolic modeling by integrating multi-omics data, showing better prediction of growth rates and gene function than transcriptomics.

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

The paper develops a genome-scale metabolic model for Ustilago maydis to study metabolic adaptations and biotechnological applications.

The paper introduces a machine-learning workflow for analyzing promoter libraries to identify sequence design principles that predict gene expression activity.