Jonathan M. Monk

The paper constructs a genome-scale metabolic model for E. faecium TX0016 that accurately predicts auxotrophies and carbon source utilization, and compares its gene essentiality predictions to E. faecalis data.

This study identifies disruption of the DNA repair protein RuvB as a mechanism promoting minocycline resistance in Acinetobacter baumannii through enhanced biofilm formation and extracellular DNA accumulation.

This paper uses pangenome decomposition to reveal lineage-specific immune-evasion and virulence architectures in group A Streptococcus, showing that virulence factors are organized into distinct modules.

This study analyzes 164 Pseudomonas putida strains to reveal metabolic and genetic diversity, curating GEMs and identifying strain-specific adaptations for biotechnology applications.

The paper identifies a clinically silent resistance phenotype in Acinetobacter baumannii that allows survival during colistin therapy despite standard susceptibility testing.

This study maps metabolic diversity in Klebsiella pneumoniae sub-lineages using genomics and metabolic modeling, revealing lineage-specific metabolic profiles that support coexistence through cross-feeding.

This study identifies CyuR as a dual regulator in E. coli that contributes to antimicrobial resistance through Cys-dependent mechanisms and modulates antibiotic transporter expression.

The paper analyzes the pangenome of Escherichia coli using 2,377 genomes to define accessory genes and rare genomic variation across phylogroups.

This paper constructs a comprehensive pangenome-scale metabolic model for the Klebsiella pneumoniae species complex, showing improved accuracy in growth predictions compared to previous models.

The paper identifies known and novel antimicrobial resistance genes across 12 species using pangenomics, gene annotation, and machine learning, finding gene transfer is mostly species-specific and demonstrating improved methods for AMR gene discovery.

Bactabolize generates strain-specific bacterial metabolic models and growth predictions, outperforming existing tools for Klebsiella pneumoniae.

The paper analyzes enterobacterial genomes to identify secondary metabolite gene clusters, finding 8604 BGCs across 2627 genomes and associating some with specific biological functions.

The paper identifies the functions of seven LysR-type transcription factors in E. coli using multi-omics methods, revealing their roles in nitrogen source utilization and amino acid metabolism.

This study characterizes antibiotic resistance in Danish Klebsiella pneumoniae isolates, finding differences in AMR profiles and virulence compared to global strains.

The paper uses genome-scale models to compute proteome allocation under different conditions, revealing variability in cofactor use and protein composition for E. coli growth.

The paper reconstructs the Fur pan-regulon in E. coli strains, revealing core, accessory, and unique regulons and their functional diversity.

This study uses multi-omics data from evolving E. coli strains to identify convergent and divergent adaptation principles at metabolic, transcriptomic, and genetic levels.

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 develops a quantitative framework to predict ROS stress responses in E. coli across molecular, network, and genetic levels.

The paper introduces ssbio, a Python framework for integrating structural biology data into genome-scale systems biology workflows.

BOFdat generates biomass objective function stoichiometric coefficients from omic data and calculates maintenance costs.