Roger Casals-Franch

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 benchmarks methods for inferring gene regulatory networks from multimodal single-cell data and introduces a framework called GRETA to evaluate their performance and limitations.