Chiara Damiani

The paper develops spFBA to analyze spatial metabolic differences in cancer, revealing metastatic mimicking and lactate-consuming cells in colorectal tumors.

This study uses a computational framework to analyze non-canonical TCA cycle activity in cancer cell lines, linking it to Warburg metabolism and cancer hallmarks.

INTEGRATE integrates metabolomics and transcriptomics data to identify metabolic flux regulation at the enzyme expression and metabolic levels in cancer cell lines.

GPRuler automatically reconstructs gene-protein-reaction rules for metabolic networks using biological databases, achieving high accuracy in both small-scale and genome-scale models.

PMCE infers probabilistic graphical models of cancer evolution from mutational profiles, showing a significant correlation between predicted evolutionary paths and patient survival.

The paper reconstructs the first genome-scale metabolic model for Zygosaccharomyces parabailii, demonstrating its potential for metabolic engineering and industrial applications.

This paper integrates single-cell RNA-seq data into metabolic models to characterize tumour cell populations and identifies metabolic heterogeneity and interactions.

MaREA identifies metabolic reactions significantly up-/down-regulated in cancer subgroups using RNAseq data and visualizes results on metabolic maps.