Markus de Raad

The paper shows that some bacteria grow better on mixed carbon sources than on individual ones, and links this to their genetic makeup and metabolic preferences.

The paper presents a multi-omic dataset examining microbial exometabolite responses to root exudates from six tallgrass prairie plant species and a model grass under controlled conditions.

The paper introduces a Bayesian Personalized Ranking model to predict microbial substrate preferences and develops an agent that integrates this model with a growth model and LLM for autonomous microbial analysis.

The paper uses consumer-resource models parametrized from monoculture exometabolite data to predict and design synthetic microbial consortia, revealing the importance of cross-feeding interactions and nonlinear dynamics.

The paper updates GapMind to include novel enzymes for amino acid biosynthesis, reducing pathway gaps in prokaryotes from 1.4 to 0.7 per genome.

MAGI integrates metabolomics and genomics data to improve metabolite identifications and gene annotations by scoring consensus through biochemical reactions.

This study shows that enzyme promiscuity enables E. coli to adapt to novel substrates through computational modeling and lab evolution experiments.