Integration of Metabolic Network Models and Machine Learning Reveals Bacterial Metabolism under Antibiotic Stress

Along with the rapid scale-up of biological knowledge bases, mechanistic models, especially metabolic network models, are becoming more accurate. On the other hand, machine learning has been widely applied in biomedical researches as a large amount of omics data becomes available in recent years. Thus, it is worth to conduct a study on integration of metabolic network models and machine learning, and the method may result in some biological discoveries. In 2019, MIT researchers proposed an approach called 'White-Box Machine Learning' when they used fluxomics data derived from in silico simulation of a genome-scale metabolic (GEM) model and experimental antibiotic lethality measurements (IC50 values) of E. coli under hundreds of screening conditions to train a linear regression-based machine learning model, and they extracted coefficients of the model to discover some metabolic mechanism involving in antibiotic lethality. In this thesis, we propose a new approach based on the framework of the 'White-Box Machine Learning'. We replace the GEM model with another state-of-the-art metabolic network model -- the expression and thermodynamics flux (ETFL) formulation. We also replace the linear regression-based machine learning model with a novel nonlinear regression model – multi-task elastic net multilayer perceptron (MTENMLP). We apply the approach on the same experimental antibiotic lethality measurements (IC50 values) of E. coli from the 'White-Box Machine Learning' study. Finally, we validate their conclusions and make some new discoveries. Specially, our results show the ppGpp metabolism is active under antibiotic stress, which is supported by some literature. This implies that our approach has potential to make a biological discovery even if we don't know a possible conclusion