Nikolaus Sonnenschein

This paper presents a computational workflow and software package to robustly estimate enzyme kinetic parameters, reconciling inconsistencies in data from in vitro and in vivo experiments.

This paper updates geckopy to improve enzyme-constraint modeling by integrating proteomics data and thermodynamic constraints, benchmarking against E. coli datasets.

CRI-SPA transfers genetic features between yeast strains to map sequence to function, demonstrating its ability to transfer genes for betaxanthin production across a yeast knock-out collection.

The paper introduces teemi, an open-source platform for bioengineering that supports simulation, data integration, and machine learning for metabolic pathway design under FAIR principles.

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

The paper develops a whole-cell metabolic model for yeast to predict compartment-specific proteome constraints that explain metabolic strategies under different growth conditions.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.

The paper presents a genome-scale metabolic model for Methylococcus capsulatus, showing that methane oxidation by pMMO is driven by uphill electron transfer with reduced efficiency.