Ziwei Dai

The paper develops and validates a genome-scale metabolic model for Cryptococcus neoformans strain H99, showing high precision in essential gene prediction and capturing metabolic adaptations relevant to drug tolerance and host survival.

The paper identifies three economic strategies underlying amino acid composition patterns in human metabolic enzymes, revealing a multi-layered proteome-level economic system in multicellular organisms.

The paper derives and validates a universal law of thermodynamics-kinetics coupling that governs glycolytic enzyme allocation across species under optimal efficiency.

This paper introduces a graph neural network model to predict Gibbs free energy for metabolic reactions and identifies thermodynamic driver reactions in human metabolic networks.

This study maps amino acid content in human diets, identifies health trade-offs, and constructs a Pareto front for optimal dietary protein intake.

MCIBox is a toolkit for analyzing single-molecule chromatin interactions and identifying micro-domains through visualization and clustering algorithms.

The paper quantitatively analyzes the physiological contributions of glucose to the TCA cycle using isotope-labeling data and metabolic flux analysis, showing glucose is the major nutrient source across tissues and conditions.

This study evolves resistance to GAPDH inhibition and shows that cells lose the Warburg Effect but maintain a distinct glycolytic state with different metabolic dependencies.

This study develops a multi-objective metabolic model to identify cancer-associated metabolic vulnerabilities by analyzing Pareto surfaces and validating predictions with experimental data.

The paper develops a computational pipeline to analyze single-cell metabolic gene expression in human tumors, revealing higher metabolic activity and variation in malignant cells compared to bulk tumor studies.

The paper shows that methionine metabolism affects H3K4me3 peak width, which encodes gene expression and cell identity, despite stable peak locations.

This study shows that environmental factors like nutrition and tissue type have a greater impact on methionine metabolism in p16/MTAP deleted cells than the genetic deletion itself.