Mathematical Modeling of Metabolic Reprogramming and Therapeutic Strategies in Non-Small Cell Lung Cancer: A Flux Balance and Variability Analysis Approach
Year: 2025
Type: conference-paper
Abstract: Cancer cells, including non-small cell lung cancer (NSCLC) lines like H292, A549, and PC-9, exhibit metabolic reprogramming ensuring cancer cells’ survival and tumor growth. This study investigates the metabolic responses of these cell lines to tseleniumchrysin (SeChry), a compound with anti-cancer effect, using computational tools such as COBRApy and Escher. Intracellular and extracellular metabolites’ concentrations were integrated into Flux Balance Analysis (FBA) to assess metabolic shifts under control and SeChry conditions.Results revealed that the exposure to SeChry disrupts key metabolic pathways, including glycolysis, the pentose phosphate pathway, and the tricarboxylic acid (TCA) cycle, significantly reducing metabolic flexibility and proliferation. The findings underscore the critical role of pyruvate kinase (PYK) in cancer cell metabolism and highlight potential metabolic vulnerabilities in NSCLC cells. Furthermore, network robustness was evaluated using algebraic connectivity (λ2), a key metric for assessing structural stability in metabolic networks. Our analysis demonstrates that the targeted removal of critical reactions leads to a substantial reduction in λ2, decreasing from 0.3588 to 0.0812, reϔlecting an overall network fragility of 77.37%. This suggests that NSCLC metabolic networks depend on specific key reactions for maintaining functional integrity, revealing potential metabolic weak points that could be exploited for therapeutic targeting.This research demonstrates the utility of computational modelling in elucidating cancer metabolism and paves the way for targeted metabolic therapies.
Authors: Lopes, João S., Mendes, Cindy, Gonçalves, Luís G., Rodrigues, José A., Serpa, Jacinta
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6893231532