James T. Yurkovich

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.

APRILE uses explainable graph neural networks to identify molecular mechanisms of polypharmacy side effects, generating explanations for over 900,000 side effect-drug pairs.

The paper formulates abiotic constraints that shape the quantitative metabolome and validates them using Escherichia coli data.

The paper shows that gut microbiome uniqueness increases with age and predicts survival in humans.

MASSpy provides a framework for dynamic metabolic modeling using mass action kinetics, integrating with existing tools and supporting simulation and uncertainty quantification.

The paper identifies challenges in metabolic modeling standards and proposes a gold standard framework through community feedback.

This paper introduces a visualization method for time-course metabolomics data integrated into metabolic network maps, demonstrating its utility in analyzing platelet and erythrocyte storage.

The paper identifies a six-state genetic program in Mycobacterium tuberculosis that enables entry into and exit from hypoxia-induced dormancy through high-resolution transcriptional time-course analysis.

The paper develops a multi-scale computational model of human red blood cells integrating metabolic networks, proteomics, and macromolecular mechanisms to predict physiological quantities.

The paper identifies differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease by analyzing transcriptomes and metabolomics data from brain samples.

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

The paper characterizes the catalytic potential of glycolytic kinases in human red blood cells and shows how their dynamic interactions contribute to disturbance rejection and homeostasis.

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