Kevin Rychel

Laboratory evolution reveals transcriptional mechanisms underlying thermal adaptation of Escherichia coli

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

Mining all publicly available expression data to compute dynamic microbial transcriptional regulatory networks

The paper develops a workflow to reconstruct dynamic transcriptional regulatory networks from public gene expression data using Bacillus subtilis as a case study.

Deciphering the transcriptional regulatory network of Yarrowia lipolytica using machine learning

This paper uses machine learning to analyze transcriptomic data from Yarrowia lipolytica under various industrial stresses, identifying regulatory modules and their responses to environmental changes.

Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection

The paper uses iModulon analysis to uncover the transcriptional regulatory network of Pseudomonas syringae across diverse conditions and interactions.

Lab evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

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

Pan-genomic analysis of transcriptional modules across Salmonella Typhimurium reveals the regulatory landscape of different strains

This study uses machine learning to analyze RNA-Seq data from multiple Salmonella Typhimurium strains, revealing transcriptional modules and regulatory patterns across the pan-genome.

Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa

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

Machine-learning from Pseudomonas putida transcriptomes reveals its transcriptional regulatory network

This study uses machine learning to uncover the genome-scale transcriptional regulatory network in Pseudomonas putida using gene expression data.

Machine learning of all Mycobacterium Tuberculosis H37Rv RNA-seq data reveals complex interplay between metabolism, stress response, and infection

The paper uses machine learning to analyze M. tuberculosis RNA-seq data, identifying gene modules and their roles in metabolism, stress response, and infection.

Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal thermoacidophile Sulfolobus acidocaldarius

The paper uses machine learning to uncover a transcriptional regulatory network in Sulfolobus acidocaldarius, identifying new gene regulators and regulatory modules.

Machine Learning of Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

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.

A multi-scale transcriptional regulatory network knowledge base for Escherichia coli

The paper constructs a transcriptional regulatory network for Escherichia coli using machine learning on a large RNA-seq compendium, identifying regulatory modules and novel regulons.

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

The paper uses machine learning to identify gene modules in Bacillus subtilis, revealing known and novel regulatory roles and population-level sporulation states.

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

iModulonDB provides a knowledgebase of microbial transcriptional regulation derived from machine learning, offering curated gene sets and their activity levels across three bacterial species.