Andreas Dräger

MCBO provides a standardized ontology for mammalian cell bioprocessing data to enable harmonization and querying of complex biomanufacturing datasets.

The paper introduces MCC, a Python package for automating mass and charge balancing in genome-scale metabolic models, and applies it to create a high-quality model for Corynebacterium tuberculostearicum.

This paper develops a genome-scale metabolic model of S. epidermidis that accurately predicts growth and carbon source utilization under in vitro conditions.

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

This paper develops a genome-scale metabolic model for A. baumannii to identify antimicrobial targets and validate its predictive capabilities using experimental data.

The paper develops a genome-scale metabolic model for Rothia mucilaginosa to predict its metabolic capabilities and identify potential antimicrobial targets for cystic fibrosis treatment.

This paper creates and validates five strain-specific genome-scale metabolic models for Corynebacterium striatum using in silico and in vitro methods, and introduces software for model curation.

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 model of Pseudomonas aeruginosa PA14 to study its metabolism, virulence, and drug interactions, revealing metabolic mechanisms and drug potentiation strategies.

The paper updates a genome-scale metabolic network for Pseudomonas aeruginosa PA14 and integrates it with transcriptomic data to reveal metabolic shifts in response to mucin exposure.

The paper describes the development of a computational knowledge repository, the COVID-19 Disease Map, which curates SARS-CoV-2 virus-host interaction mechanisms and provides tools for their analysis and application.

The paper identifies challenges in metabolic modeling standards and proposes a gold standard framework through community feedback.

This paper introduces a visualization method for time-course metabolomics data integrated into metabolic network maps, demonstrating its utility in analyzing platelet and erythrocyte storage.

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.