Graph Neural Network Representation of State Space Models of Metabolic Pathways

A novel Metabolic Graph Neural Network (MGNN) model is proposed for simulating the dynamic behavior of metabolites involved in oxidative stress metabolic pathways in a bacterial cell culture. The developed MGNN model is trained and validated with in-silico data generated from the mechanistic model. By using the a priori known metabolic network, the proposed MGNN model effectively reduces the overfitting issue as compared to a fully connected network that does not uses the metabolic network knowledge. The MGNN exhibits a superior fit for both training and testing datasets. The proposed MGNN is highly interpretable since it efficiently computes the relevance of each metabolite on any other metabolite by applying gradient computation and back-propagation operations to the neural network. The proposed model is also shown to be useful for fault detection. Copyright (C) 2024 The Authors.