María Masid

The paper introduces SIGMA, a framework that reconstructs receptor-to-transcription factor signaling by integrating prior knowledge and omics data to uncover metabolic reprogramming mechanisms.

This study introduces iMSEA, a computational framework that identifies metabolic state shifts in BRCA1-deficient cancers using omics data and validates findings with experimental assays.

This study builds kinetic models of ovarian cancer metabolism to identify metabolic vulnerabilities and drug targets in BRCA1-deficient cells.

This paper introduces a framework for rational strain design in E. coli that minimizes phenotype perturbation using mixed-integer linear programming and kinetic models to improve anthranilate production.

This study shows that a variant of IL-2 (IL-2v) induces a stem-like, metabolically active T-cell state with improved therapeutic potential in adoptive cell therapy.

The paper introduces SKiMpy, a Python package for efficiently building and analyzing large-scale kinetic models in biological systems.

This paper develops yETFL, an extended ME-model for Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics, demonstrating its predictive ability for growth rate, essential genes, and overflow metabolism.