Elizabeth Brunk

The paper provides an open resource for automated ecDNA quantification, comparing methods and introducing ecCount to reduce count bias.

This study shows vandetanib enhances tamoxifen sensitivity in ER+ breast cancer by reprogramming ER gene regulation and kinase networks, identifying adaptive resistance pathways and response signatures.

STARMAP maps protein structural features to regulatory and cellular phenotypes using natural genetic variation across cancer cell lines.

This paper uses AI/ML to annotate genomic variants in breast cancer, identifying new actionable mutations and functional consequences beyond known drivers.

The paper shows that ecDNA allows tumors to adapt rapidly by maintaining a spectrum of gene expression levels across cells, rather than relying on clonal expansion.

This paper introduces a multimodal framework to identify drug-responsive subpopulations in triple-negative breast cancer by integrating bulk and single-cell transcriptomics data.

This study introduces an automated pipeline for detecting and quantifying ecDNA in FISH images to analyze drug response mechanisms in cancer cells.

This paper develops a machine learning method using protein structures to predict the functional impacts of genetic variants on transcriptional pathways of Nrf2 and c-MYC.

CytoCellDB is a database that expands the number of cancer cell lines with ecDNA annotations, enabling analysis of ecDNA in relation to molecular data and drug responses.

This paper presents a standardized protocol for visualizing extrachromosomal DNA (ecDNA) using scanning electron microscopy (SEM) in human cancer cell lines.

The paper investigates how post-translational modifications in Escherichia coli regulate metabolic adaptation to environmental changes, showing their impact on fitness and pathway usage.

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 introduces ssbio, a Python framework for integrating structural biology data into genome-scale systems biology workflows.