The integration of multi-omics data and advanced computational modeling is enhancing our ability to predict and optimize metabolic engineering outcomes. This systems biology approach is transforming the field by providing a more comprehensive understanding of plant metabolism and guiding engineering strategies. Genome-scale metabolic models have become powerful tools, offering comprehensive in silico representations of plant metabolism that can inform engineering decisions and predict the outcomes of genetic modifications . These models are continually refined with experimental data, improving their accuracy and predictive power. Metabolic flux analysis, using isotope labeling and sophisticated computational tools, allows researchers to quantify metabolic fluxes and identify rate-limiting steps in pathways of interest . This information is crucial for identifying targets for metabolic engineering and optimizing pathway performance.