Bernhard Ørn Palsson

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.

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

The paper uses pangenome analysis of Escherichia coli to reveal the genetic basis of metabolic reactions and their evolutionary dynamics.

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.

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

The paper identifies an aerobicity stimulon in E. coli through computational analysis of evolved respiratory variants, showing gene groups co-regulated with aerobic capacity and proteome reallocation.

The paper defines the Band 3 N-terminus interactome in red blood cells, revealing its role in hypoxic adaptation through BLVRB-dependent S-nitroso transfer and metabolic remodeling.

The paper investigates how G6PD variants affect red blood cell storage, transfusion performance, and recipient physiology using multiple datasets and experimental models.

The paper analyzes scWAT transcriptomes of long-term trained and untrained adults, finding sex-specific molecular adaptations linked to metabolic health.

QSProteome provides a platform for modeling, evaluating, and refining predicted protein complex structures, enabling community-driven exploration and refinement of over 42,000 genes.

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

The paper updates the iModulon decomposition of Bacillus subtilis transcriptomes using 782 RNAseq samples, revealing 142 iModulons that capture 80% of expression variance and provide new insights into gene regulation.

The paper investigates high cell density physiology in Escherichia coli, revealing a trade-off between resistance and persistence through transcriptional analysis and machine learning.

This paper presents a multi-scale systems approach to design biomanufacturing systems for CO2 fixation, showing the reductive glycine pathway can fix CO2 at higher rates than photosynthetic organisms.

This study uses adaptive laboratory evolution and rational engineering to improve Pseudomonas putida's tolerance to isoprenol, enhancing its production of a sustainable aviation fuel precursor.

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

The paper presents a computational workflow to design enzyme selection systems (ESSs) that couple enzyme activity to microbial metabolism, creating a database of 25,505 ESS designs for Escherichia coli.

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

PanKB is a microbial pangenome knowledgebase that integrates pangenomic data and literature to support research and biotechnological applications.

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.

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.

The paper reconstructs pangenomes of Lactobacillaceae to generate metabolic models for 26 species, enabling species-specific metabolic trait predictions.

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

The paper provides over 1,000 high-quality Actinomycetia genomes, revealing genomic structures and biosynthetic gene cluster organization with ecological and biotechnological implications.

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 develops a workflow to model E. coli transcriptional regulation using machine learning and gene expression data, showing it can capture multi-scale regulation and resolve regulatory paradoxes.

The paper introduces RBC-GEM, a comprehensive metabolic reconstruction of human red blood cells, curated from proteomic data and expanded significantly over previous models.

The paper develops QSPACE, a computational platform to annotate quaternary structural proteomes at genome-scale using structural resources and mutation data, demonstrating its application on E. coli.

BGCFlow is a systematic workflow for analyzing biosynthetic gene clusters across large genomic datasets, demonstrating improved dereplication and comparative analysis of RiPPs in Saccharopolyspora genomes.