dAMN: a genome scale neural-mechanistic hybrid model to predict bacterial growth dynamics

Abstract

SummaryThis study presents dAMN, a hybrid neural-mechanistic model that integrates neural networks with genome-scale dynamic flux balance analysis (dFBA) to predict bacterial growth curves across diverse nutrient environments. dAMN uses neural networks to infer dynamic behavior from initial metabolite concentrations, while mechanistic constraints ensure stoichiometric and thermodynamic consistency based on genome scale metabolic models. dAMN is trained on E. coli and P. putida experimental growth data from media containing various combinations of sugars, amino acids, and nucleobases, and evaluated on two test sets: one for forecasting over time and another for predicting growth dynamics on unseen media. dAMN achieved high predictive power (R2 [≥] 0.9), successfully reproducing growth and substrate depletion dynamics including acetate overflow and glucose-acetate consumption shift for E. coli. An interesting innovation of dAMN is the treatment of the lag phase, enabling realistic adaptation dynamics absent from standard dFBA models. dAMN stands out for its ability to generalize across combinatorial nutrient inputs and produce full growth-curve predictions from minimal input data. Availability and implementationThe dAMN software, along with the associated models and data, is available at https://github.com/brsynth/dAMN-main-release and via DOI 10.5281/zenodo.17908125