Metabolic Transformation Algorithm (MTA) is a computational method that identifies potential metabolic interventions to shift a biological system from an undesired state to a desired one by targeting specific genes or reactions within a metabolic network. The Robust Metabolic Transformation Algorithm (rMTA) enhances this capability by incorporating worst-case scenario analyses and integrating the Minimization Of Metabolic Adjustment (MOMA) approach. By transitioning these algorithms from MATLAB to Python, this work aims to enhance their accessibility and usability within the scientific community. The Python implementations were validated by successfully replicating results from the original MATLAB implementation, including gene knockouts of RRM1 and RRM2. Furthermore, the rMTA algorithm was applied to an aging-related scenario in Caenorhabditis elegans to identify candidate genes and pathways involved in the metabolic response to unc-62 knockdown. Additionally, this work integrates an Evolutionary Algorithms (EAs)-based approach to rMTA to evaluate the effects of multiple gene knockouts simultaneously. This extension optimizes the identification of metabolic targets for phenotype reversion, demonstrating the potential for more effective therapeutic strategies. In summary, this dissertation enhances the accessibility of MTA and rMTA by implementing them in Python, applying them to an aging-related scenario, and extending their capabilities through the integration of EAs.