Jason A. Papin

The paper shows that the source of annotation data significantly affects the quality of automatically reconstructed genome-scale metabolic models.

This study shows how environmental factors like nutrients, pH, and oxygen affect the evolution of antibiotic resistance in Pseudomonas aeruginosa.

The paper describes an internal grant review program for early-career researchers to improve NIH application quality through feedback and automation.

This study benchmarks large language models' ability to generate biochemical networks, finding moderate accuracy in signaling and metabolic networks but variable prediction performance.

This study shows that pathogenic Leptospira uses glucose under host-like temperatures, contradicting previous assumptions about its metabolic capabilities.

This study identifies metabolites from the soil bacterium Massilia that promote Leptospira growth, revealing a metabolic interaction between environmental bacteria and a pathogen.

The paper evaluates probiotic candidates for vaginal health using metabolic models and experimental assays, finding that D-lactic acid production, not metabolic similarity, drives inhibition of G. vaginalis.

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

The paper characterizes the functional metabolic diversity of Pseudomonas aeruginosa clinical isolates by analyzing genotype-phenotype correlations and genome-scale metabolic networks.

The paper develops a systems biology pipeline to design bacterial consortia that protect against severe Clostridioides difficile infection by targeting amino acid cross-feeding.

Reconstructor is a user-friendly COBRApy tool for automated genome-scale metabolic network reconstruction with gap-filling, generating high-quality models for further biological analysis.

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

The paper uses metabolic modeling and gene expression data to identify sex-specific differences in liver metabolism and their implications for toxicological responses.

The paper identifies niche-specific metabolic targets in pathobionts by analyzing genome-scale metabolic networks and demonstrates their potential for targeted antimicrobial therapies.

The paper uses metabolic network models to analyze the Gardnerella pangenome and identifies core metabolic functions in the vaginal environment.

The paper presents a comparative analysis of 192 protozoan parasite genomes using a metabolic network reconstruction pipeline to identify species-specific metabolic differences and improve experimental system selection.

The paper introduces a method to quantify genomic, biochemical, and phenotypic evidence for metabolic network reconstructions, improving accuracy by integrating continuous evidence.

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

Medusa is a Python package that generates and analyzes ensembles of genome-scale metabolic network reconstructions, enabling ensemble simulations and machine learning-guided curation.

RIPTiDe improves metabolic predictions by integrating transcriptomic data with flux minimization to identify cost-effective metabolic pathways in bacteria without requiring knowledge of 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 develops a machine learning approach to guide curation of metabolic models by identifying structural variants that improve predictiveness across bacterial species.

This study introduces PROTEAN, a method to analyze metabolic capabilities of Lactobacillus strains, revealing site-specific differences in predicted metabolic production.

The paper introduces three algorithms to improve flux coupling analysis in genome-scale metabolic models, enhancing efficiency and biological interpretability.

The paper reconciles gene essentiality data with metabolic network models to explain essentiality differences and improve identification of condition-independent essential genes in Pseudomonas aeruginosa.

This study identifies metabolic changes in antibiotic-resistant Pseudomonas aeruginosa using growth profiling and network analysis, revealing both reduced and enhanced growth on specific carbon sources.

This study shows that the order of antibiotic exposure affects bacterial resistance development, with past drug regimens influencing future resistance profiles.