Richard Szubin

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

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.

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 demonstrates that Escherichia coli can utilize extracellular electron transfer via 2-hydroxy-1,4-naphthoquinone to support anaerobic growth, revealing a new energy metabolism pathway.

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.

This study uses transcriptomic data to construct a regulatory network of Pseudomonas aeruginosa, revealing roles of efflux pumps and media composition in antibiotic responses.

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.

This study identifies and characterizes 34 new DNA-binding transcription factors in E. coli, predicts their functions, and expands the known total number of TFs.

The paper uses machine learning to analyze Pseudomonas aeruginosa transcriptomes and identifies gene sets regulated by known transcriptional factors, revealing insights into biosynthetic clusters and metabolic regulation.

The paper identifies regulons of five two-component systems in E. coli using RNA-seq and ChIP-exo data, revealing novel regulatory relationships and non-cognate inducers.

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 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.