In this work, a genome-scale metabolic model of Synechococcus sp. PCC 7002 which utilizes flux balance analysis across multiple layers is analyzed to observe flux response between 23 growth conditions. This is achieved by setting reactions involved in biomass accumulation and energy production as objectives for bi-level linear optimization, thus serving to improve the characterization of mechanisms underlying these processes in photoautotrophic microalgae. Additionally, the incorporation of statistical techniques such as k-means clustering and principal component analysis (PCA) contribute to reducing dimensionality and inferring latent patterns.