Jordi Villà­-Freixa

This study evaluates the reliability of physics-informed neural networks (PINNs) for parameter recovery in the repressilator model, showing accurate trajectory reconstruction but challenges in parameter estimation due to noise and model assumptions.

This paper provides a computational workflow for analyzing T cell state transitions in the tumor microenvironment using single-cell RNA sequencing data, integrating trajectory inference and gene regulatory network analysis.

This study uses gene regulatory network and trajectory inference to analyze single-cell RNA-seq data from squamous cell carcinoma, revealing how PD-1 blockade reprograms T cell states and identifies key transcriptional regulators.

The paper reconstructs and assays ancient enzymes to show that losing selenocysteine in GPX6 led to functional loss and convergent evolution in mammalian oxidoreductases.

The paper shows that phosphorylated IκB binds chromatin via acetylated H4 and that proteolytic cleavage of H4 inhibits this binding, affecting intestinal cell differentiation.