## 1 Introduction
### Research Overview  
This study begins with an assumption and a straightforward question: If we start from scratch to design genetic modifications on a metabolic pathway to optimize the production of a target metabolite, can we make a model that helps us to predict the reactions more relevant to modification? In this sense, tryptophan production is a suitable master pathway to comparison, given the extensive investigations that provide information on over 40 genetic modifications made by several research groups to increase its production. Few algorithms are capable of simultaneously predicting activation, inactivation, and attenuation. OptReg [2] is one of these algorithms; however, based on mixed-integer programming (MIP), proprietary software is required to solve MIP problems efficiently. When multiple reaction modifications are allowed, the computational demand grows exponentially, so it is not practical to obtain a general overview of the fluxes in a genome-scale metabolic network [28]. Furthermore, it has been found that algorithms based on optimization approaches, such as OptKnock, often fail to identify pathways that compete with metabolite production [21]. Most algorithms to identify candidate reactions that affect the production of metabolites have been developed based on mathematical optimization models. Our classification model is the first trained with experimentally tested genetic modifications. The model is also fed with information generated by COBRA methods, such as pFBA and FSEOF, combining its characteristics and extending their separated functionality. Surprisingly, being trained with only 40 validated reactions to overproduce tryptophan, the qualitative variables compile and fuse to build the QPAML model integer the reactions fluxes for optimizing the production of metabolites in a more general way, yielding classification of 322 reactions that improve the production of 30 other endogenous metabolites without the need to retrain the model. The robustness of the QPAML model is based on using FSEOF to introduce perturbations on optima reactions and observing the resulting changes in the flux distribution, identifying potential bottlenecks and competing reactions [32], and extracting information about the optimal structure of the involved metabolic network [33]. To select the optimal reactions to be perturbed, we used the pFBA algorithm to define an optimal pathway to synthesize tryptophan from glucose. However, the information currently available to relate the production of metabolites with genetic modifications is dispersed and needs to be improved in terms of quantity and quality. To deal with this problem, we translate quantitative to qualitative variables by implementing Qualitative Perturbation Analysis (QPA) [34]. An advantage of employing QPA lies in its ability to predict system responses to defined perturbations and, conversely, to identify perturbations that cause a system response [35]. This approach allows the computational demand to be low when classifying all the reactions of the metabolic network. Perturbation theory has been synergistically incorporated with machine learning (ML), for instance, to predict medication regimes and calculate metabolic network properties [33]. Notably, the Gradient-Boosting Decision Tree (GBDT) stands out as an ML algorithm recognized for its high accuracy in real-world classification problems [36]. Thus, GBDT was used to make final reaction classifications of the qualitative variables. Consequently, the result of coupling Qualitative Perturbation Analysis with Machine Learning (QPAML) introduced here is the first classification model that reveals biochemical reaction fluxes when a metabolite overproduction is intended [37]. The results of the QPAML model are congruent with 322 experimentally reported reactions in the overproduction of 31 metabolites. Particularly notable is the model's proficiency in identifying competing pathways. Furthermore, the model demonstrates robustness to culture conditions and different _E. coli_ genetic backgrounds.