Qualitative Perturbation Analysis and Machine Learning: Elucidating Bacterial Optimization of Tryptophan Production

Abstract

L-tryptophan is an essential amino acid widely used in the pharmaceutical and feed industries. Enhancing its production in microorganisms necessitates activating and inactivating specific genes to direct more resources toward its synthesis. In this study, we developed a classification model based on Qualitative Perturbation Analysis and Machine Learning (QPAML). The model uses pFBA to obtain optimal reactions for tryptophan production and FSEOF to introduce perturbations on fluxes of the optima reactions while registering all changes over the iML1515a Genome-Scale Metabolic Network model. The altered reaction fluxes and their relationship with tryptophan and biomass production are translated to qualitative variables classified with GBDT. In the end, groups of enzymatic reactions are predicted to be deleted, overexpressed, or attenuated for tryptophan and 30 other metabolites in E. coli with a 92.34% F1-Score. The QPAML model can integrate diverse data types, promising improved predictions and the discovery of complex patterns in microbial metabolic engineering. It has broad potential applications and offers valuable insights for optimizing microbial production in biotechnology.

Keywords

• machine learning
• <i>Escherichia coli</i>
• L-tryptophan
• Flux Balance Analysis
• metabolic engineering
• Qualitative Perturbation Analysis