## 3 Results
### Conversion of the Flux Variables to a Classification Model Based on QPAML
pFBA optima reactions encompass the biosynthesis pathways of tryptophan and its precursors, aligning with strategies involving gene overexpression in metabolic pathways [6; 7; 8; 9; 12; 13; 14; 15; 16]. FSEOF categorizes reactions into promoting, hindering, and without discernible effects on tryptophan production [30]. When perturbations are introduced, some reactions that impede the production of tryptophan exhibit the same response pattern, regardless of the perturbation introduced (See Supplementary Figure S5a). The individual fluxes for each perturbation correspond to holistic-growth-coupled (hGC) reactions proposed by [PERSON] and [PERSON] [18]. Although there are 299 reactions with this behavior, more than silence and attenuation candidates together, no modifications of these reactions have been reported in the literature. Reactions showing a negative correlation between their fluxes and tryptophan production and not categorized as biomass-related were identified as candidates for disruption. A literature review on \(pheA\) and \(tyrA\) genes reveals that knocking out these genes neces  
Figure 4: The relative importance of qualitative variables for the QPAML classification model. The charts delineate the relative importance of variables within the trained classification model; variables with higher scores contribute more significantly to the model’s decision-making process, indicating a more significant influence on predictions. Conversely, variables with lower scores have a diminished impact. It is important to note that the collective importance of all variables governs the final classification of the model. The sum of these scores is normalized to 100.0%, providing a relative measure of each variable’s contribution to the overall model: (a) Illustrates the relative importance of each variable within the trained model. (b) Presents a cumulative representation of these variables. s. The results are shown in Figure 5.  
The QPAML classification model, trained for tryptophan overproduction, was utilized to categorize 322 genetic modifications reported in the literature about the overproduction of tryptophan and 30 other endogenous metabolites using glucose as a carbon source (See Supplementary Table S2). As delineated in Figure 6, the model manifested a weighted precision of 95.17%, a sensitivity of 90.68%, and an F1-Score of 92.34%. Notably, the knockout class exhibited the lowest sensitivity at 82.09%. This lesser sensitivity likely derives from false negatives, predominantly within the low flux category, yielding a precision of only 50.00% for this class. Figure 5: Classification of reactions based on their effect on tryptophan production. Blue bars delineate the number of reactions predicted by the model, whereas yellow bars signify reactions supported by experimental evidence. Figure 6: Confusion Matrix of Classified Reactions. The references pertain to genetic modifications in _E. coli_ documented in the literature to enhance the production of 31 metabolites. In contrast, predicted  
modifications denote classifications derived from the QPAML model. In the matrix, the diagonal signifies the correctly classified reactions, the horizontal misclassified elements indicate false positives, and the vertically misclassified components represent false negatives.