Miguel Ponce-de-León

PhysiCelldFBA links single-cell metabolism to multicellular spatial dynamics by integrating genome-scale metabolic models with agent-based simulations.

This study uses metabolic modeling and transcriptomics to analyze drug-induced metabolic changes in gastric cancer cells, revealing synergistic effects and potential therapeutic targets.

PhysiBoSS 2.0 integrates stochastic Boolean and agent-based models for multiscale cancer simulations, improving modularity and functionality.

The paper presents a computational framework for analyzing SARS-CoV-2-host interactions to identify drug targets and model disease mechanisms.

The paper develops an HPC-optimized workflow to explore treatment strategies for tumor growth using a multi-scale model, finding that spatial geometry and population variability affect treatment effectiveness.

The paper describes the development of a computational knowledge repository, the COVID-19 Disease Map, which curates SARS-CoV-2 virus-host interaction mechanisms and provides tools for their analysis and application.

The paper identifies common issues in how gene-protein-reaction (GPR) rules and gene expression data are handled in context-specific metabolic models and evaluates their impact on gene essentiality analysis.

The paper presents a genome-scale metabolic network for the cedar aphid's endosymbiotic consortium, confirming known metabolic complementation and identifying new pathway sharing, while exploring evolutionary mechanisms.