Analysing metabolic changes in a gastric adenocarcinoma cell line under different drug treatments: a constraint-based modeling approach

Abstract

Gastric carcinoma (GC) is one of the leading causes of cancer death globally. Managing advanced stages of GC necessitates a multifaceted approach, encompassing surgical interventions and comprehensive multidisciplinary strategies such as combinatorial drug treatments, recently emerging as promising avenues. Recent advances in computational biology have facilitated the development of genome-scale metabolic models (GEMs), which offer a comprehensive, mathematical framework for understanding the intricate metabolic dynamics. Integrating these GEMs with high-throughput omics data, GEMs provide a representations of cellular metabolism through metabolic tasks, allowing for the elucidation of complex metabolic phenotypes associated with the effects of combinatorial treatments. Herein, an approach to take advantage of metabolic tasks and high-throughput omics data using the previously described Tasks Inferred from Differential Expression (TIDEs) approach in conjunction with vital genes to metabolic tasks (anchor genes) is presented as ag-TIDEs. The aim of this work is to explore aberrant metabolic phenotypes using different approaches to generate hypotheses on the mechanisms underlying the observed synergies in the combinatorial drug treatments of TAK1, MEK, and PI3K inhibitors on cells from the AGS cell line. Preliminary results propose distinct metabolic alterations induced by the drug treatments as a hypothesis to be further explored.