This study develops and validates a systems biology framework for assessing the biosafety of genetically modified organisms (GMOs), focusing on Roundup®-Ready Soy (glyphosate-resistant GMO Soy, or RRS). The authors use an advanced computational platform (CytoSolve®) to expand existing in-silico models of plant C1 metabolism by incorporating pathways for glutathione biosynthesis and glyphosate catabolism. C1 metabolism is a central molecular system in plants that connects essential biochemical processes such as one-carbon metabolism and detoxification. By simulating these integrated molecular networks, the model predicted that Organic Soy (non-GMO) would maintain a substantially higher ratio of reduced versus oxidized glutathione (GSH/GSSG), a key biomarker of oxidative health, than glyphosate-resistant Soy subjected to glyphosate treatment. These predictions were then compared with existing in-vivo experimental data showing similar patterns of glutathione depletion in glyphosate-treated RRS, indicating strong concordance between computational and empirical results. The findings suggest that in-silico systems biology modeling can reliably reflect complex biochemical perturbations induced by genetic modification and herbicide exposure, offering a potential quantitative basis for GMO safety assessment. Specifically, the observed lower GSH/GSSG ratio in RRS compared to Organic Soy implies that glyphosate-resistant GMOs exhibit perturbed redox homeostasis, calling into question claims of “substantial equivalence” with non-GMO counterparts. The authors argue that incorporating molecular biomarkers such as glutathione into safety criteria could improve objectivity and transparency in regulatory evaluations. More broadly, the study demonstrates the scalability of this computational framework to integrate multiple molecular systems, which could inform future research and development of improved GMO assessment standards.
