Using Constraint-Based Metabolic Modeling to Elucidate Drug-Induced Metabolic Changes in a Cancer Cell Line

Abstract

Cancer cells frequently reprogram their metabolism to support growth and survival, making metabolic pathways attractive targets for therapy. In this study, we investigated the metabolic effects of three kinase inhibitors and their synergistic combinations in the gastric cancer cell line AGS using genome-scale metabolic models and transcriptomic profiling. We applied the TIDE (Tasks Inferred from Differential Expression) algorithm to infer pathway activity changes and introduced TIDE-essential, a new variant that focuses on task-essential genes, enhancing results robustness. Our results revealed widespread down-regulation of biosynthetic pathways, particularly in amino acid and nucleotide metabolism. Combinatorial treatments induced condition-specific metabolic alterations, including strong synergistic effects in the PI3Ki-MEKi condition affecting ornithine and polyamine biosynthesis. These metabolic shifts provide insight into drug synergy mechanisms and highlight potential therapeutic vulnerabilities. To support reproducibility, we developed an open-source Python package, MTEApy, implementing both TIDE frameworks.