This invention discloses a method for designing optimal strains for product synthesis using an
enzyme-constrained model combined with an
evolutionary algorithm, comprising the following steps: Step S1, solving the
enzyme-constrained model with the objective function of maximizing the
specific growth rate of the strain to obtain the simulated metabolic flux of the wild-type strain; Step S2, performing
dimensionality reduction analysis and labeling on genes directly related to
enzyme synthesis within the model by solving a series of flux balance analysis problems with fixed
biomass synthesis rates; Step S3, predicting the yield of single-target editing; Step S4, using a
genetic algorithm to search for combined targets after
dimensionality reduction, adjusting relevant parameters to obtain the optimal
gene editing
combination strategy; Step S5, repeating the experiment to verify the stability of the
algorithm. This invention utilizes genetic algorithms and enzyme-constrained models to discover non-intuitive
gene editing combination strategies, providing a new method for constructing efficient microbial
cell factories for synthetic chemicals, and demonstrating the potential of applying
heuristic algorithms to strain optimization design.