Using Contraint-Based Metabolic Modelling to Elucidate Drug-Induced Metabolic Changes in a Cancer Cell Line

Presented poster at the 23rd European Conference on Computational Biology by Xavier Benedicto Molina (P177).

Gastric carcinoma (GC) stands as a significant contributor to global cancer mortality. In this study, we unveil a methodology for evaluating the impact of drug interventions on the metabolic pathways of GC cell lines, particularly the AGS cell line. Recent advancements in computational biology have paved the way for the development of genome-scale metabolic models (GEMs), as a comprehensive mathematical framework for deciphering metabolic dynamics. Specifically, we have expanded upon a preceding framework known as Tasks Inferred from Differential Expression (TIDEs), now accommodating interventions targeting pivotal genes associated with specific metabolic functions (designated as TIDE-essential or TIDE-e).