Reihaneh Mostolizadeh

The paper constructs the first genome-scale metabolic model for HMPV, identifies antiviral targets, and highlights UDP-GlcNAc as a critical metabolic hub for viral replication.

This study compares deep learning models for generating antimicrobial peptides, finding that auto-encoders, especially Wasserstein auto-encoders, produce more diverse sequences than other models.

The paper introduces MCC, a Python package for automating mass and charge balancing in genome-scale metabolic models, and applies it to create a high-quality model for Corynebacterium tuberculostearicum.

The paper introduces FROG analysis to standardize reproducibility assessments of genome-scale metabolic models, resulting in full reproducibility of all tested models.