Eduard Johannes Kerkhoven

This study performs pan-GWAS across four Aspergillus species to show that pathogenicity involves rare genome expansion while industrial strains show rare genome contraction.

AlphaGEM constructs genome-scale metabolic models using structural alignment and deep learning, improving accuracy over existing methods for diverse species.

The paper develops Yeast-MetaTwin, a metabolic model for yeast that expands coverage of the metabolome using retrobiosynthesis and deep learning, revealing new insights into underground metabolism.

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

This paper evolves a Saccharomyces cerevisiae strain tolerant to oxidative stress, leading to higher taxadiene production, and identifies metabolic changes contributing to this improvement.

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

The paper develops methods to generate context-specific genome-scale metabolic models from single-cell RNA-Seq data and shows that cell subtypes have unique metabolic profiles.

This study shows that removing storage lipids in Yarrowia lipolytica causes protein misfolding and stress responses, and alters lipid composition under nitrogen limitation.

The paper upgrades the GECKO method to create enzyme-constrained models for yeast and human, revealing common metabolic adaptations under stress.

A substrate graph neural network plus protein-sequence CNN predicts enzyme kcat (test Pearson r 0.71), tracks mutation effects, and parameterizes enzyme-constrained models for 343 yeast/fungi species.

The paper develops a genome-scale model of the yeast protein secretory pathway to identify targets for improving recombinant protein production, finding that the secretory pathway has more limited capacity than metabolism.

This paper demonstrates the reconstruction of a genome-scale metabolic model for Hansenula polymorpha using the RAVEN toolbox through a homology-based approach.

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 presents rhto-GEM, the first genome-scale metabolic model of Rhodotorula toruloides, which accurately predicts growth and identifies genetic targets for improving lipid production.

FALCONET is an R package that automates the visualization of genome-scale metabolic models and multi-omics data using CellDesigner.

SLIMEr improves genome-scale metabolic models by accurately representing lipid requirements using experimental data, enabling analysis of lipid distribution flexibility and energy costs in yeast.

RAVEN 2.0 is a toolbox for metabolic network reconstruction, demonstrating its ability to generate accurate GEMs for Streptomyces coelicolor and improve existing models.