Marco Polo Castillo-Villalba

This paper introduces a framework combining information geometry and UMAP to model gene expression dynamics and identify stem cell-related genes in single-cell RNA sequencing data.

The paper introduces a topological framework for analyzing genetic and metabolic networks, independent of kinetic parameters, and applies it to circadian rhythm gene circuits.

The paper extends a biochemical modeling framework to large-scale genetic and metabolic networks using toric geometry and algebraic varieties.

The paper extends Power Law Modeling with monoids to predict kinetic and genomic parameters in molecular networks, specifically estimating binding site numbers in metabolic pathways and allosteric systems.