Ying Hefner

The paper uses machine learning to compare transcriptional regulatory networks of three Pseudomonas strains, revealing shared and unique regulatory mechanisms across species.

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

This study identifies a three-phase transcriptional response in E. coli to antibiotic exposure using iModulon analysis, revealing coordinated stress, redox, and metabolic responses.

This study uses adaptive laboratory evolution to identify five transcriptional mechanisms enabling Escherichia coli to grow at high temperatures.

The paper identifies mutations in S. aureus USA300 strains that affect gene regulation by combining genetic association testing with transcriptional regulatory network reconstruction.

This paper uses transcriptomics and machine learning to reconstruct the transcriptional regulatory network of L. reuteri, identifying key regulatory signals and metabolic pathways.

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.

The paper uses adaptive laboratory evolution and transcriptomic analysis to identify six mechanisms of paraquat tolerance in E. coli.

The paper evolves ETS variants in E. coli and finds they all optimize growth rate and proteome allocation, with conserved aero-type strategies except for ETS-4H.

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.

The paper estimates in vivo enzyme turnover numbers (kcats) in E. coli using metabolic specialist strains and shows their stability and predictive power over in vitro kcats.

The paper decomposes the Escherichia coli transcriptome into 70 independent regulatory signals, showing that 50 correspond to known transcriptional regulators and revealing a principle of signal summation in prokaryotic gene regulation.

This study uses adaptive laboratory evolution to generate E. coli strains with increased superoxide stress tolerance and identifies genetic and transcriptomic mechanisms underlying this adaptation.

This paper designs and evolves synthetic syntrophic E. coli pairs using a novel algorithm, revealing metabolic changes that enhance community fitness and guide co-culture optimization.

This study uses adaptive laboratory evolution to reveal divergent transcriptomic and metabolomic adaptations in E. coli knockout strains with similar initial perturbations.

The paper shows how multiple optimal phenotypes emerge in Escherichia coli pgi knockout strains through adaptive evolution, overcoming redox and metabolite imbalances.

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