This invention discloses a method for constructing a dynamic
enzyme constraint model based on a temporal neural network for unconventional
yeast, aiming to solve the technical problems of staticity, rigid
enzyme constraint updates, and poor
numerical stability in traditional metabolic models. The method first constructs the framework of a static
enzyme constraint model for unconventional
yeast, integrates and completes enzyme kinetic parameters through
deep learning, and establishes enzyme capacity constraints. It then constructs a dual-timescale
coupling framework of macroscopic culture and microscopic
metabolism, using enzyme constraint flux balance analysis to solve for instantaneous metabolic flux. An attention-enhanced long short-
term memory network is designed to learn the temporal patterns of enzyme concentration, achieving dynamic allocation of enzyme resources. Physical laws are embedded into the
loss function to
train the network, and an adaptive
time step ensures computational stability and efficiency. The dynamic metabolic trajectories of substrate,
biomass, and products are output through iterative loops. This method is adaptable to various unconventional yeasts, can accurately simulate metabolic dynamics, and provides a general computational tool for the
metabolic engineering modification of industrial strains.