4 results about "Metabolic network model" patented technology

The application provides an insulin resistance dynamic intervention method and system based on a metabolic flow graph network and reinforcement learning, and belongs to the technical field of insulin resistance intervention. The method comprises the following steps: acquiring first time series data and performing preprocessing; based on a feature matrix obtained through the preprocessing, performing individualized parameter learning through a heterogeneous graph neural network, constructing an individualized metabolic flow graph network model to generate a metabolic state vector; inputting the metabolic state vector into a reinforcement learning intelligent agent to generate an individualized intervention strategy vector; outputting and executing the strategy, and simultaneously performing online co-evolution updating on the metabolic network model and the reinforcement learning intelligent agent based on new data and feedback after the execution. The application solves the technical problems of the static, universal and unable-to-adapt-to-individual-metabolic-dynamic-fluctuation and multi-objective-weighting of existing intervention schemes, and realizes precise, real-time, adaptive and interpretable dynamic individualized intervention.

This invention discloses a metabolic network modeling and optimization method based on enzyme cost and metabolic stress analysis, belonging to the interdisciplinary field of bioinformatics and metabolic engineering. This method incorporates enzyme unit throughput protein cost, metabolite pressure, and expression data into an enzyme constraint model, proposing an expression-efficiency bias index (EEP+) that integrates metabolic stress, enabling precise identification and prioritization of metabolic engineering targets. The core steps are: flux balance analysis of the enzyme constraint model to obtain stress and construct an enzyme stress factor; calculation of standardized expression-cost bias within the pathway; formation of the EEP+ index; sorting enzyme targets in descending order and suggesting knockout, downregulation, or overexpression based on thresholds. This invention constructs an index system integrating enzyme cost, metabolite shadow price, and expression data, achieving multi-dimensional mechanistic evaluation of metabolic engineering targets, avoiding the limitations of single indicators, and providing efficient guidance for strain modification and pathway optimization.

The invention discloses a genome scale metabolic network model deletion reaction prediction and filling method and system, and the method comprises the steps: obtaining a plurality of genome scale metabolic network models from a metabolic network database, and constructing a data set with a supervision label; extracting and fusing the molecular sequence and molecular map characteristics of the metabolite to obtain an initial characteristic vector; constructing a metabolic directed graph and a metabolic reaction hypergraph based on a positive reaction sample, performing feature directivity enhancement by using the directed graph, and extracting high-order topological information through a hypergraph convolutional neural network to obtain final feature representation of metabolites; on the basis of the feature representation, adopting an attention mechanism to predict candidate reaction confidence and training a model; and finally, screening a high-confidence reaction from the candidate reaction pool and filling the target model with the high-confidence reaction. According to the method, the metabolite multi-dimensional molecular characteristics and the network high-order topological information are deeply fused, the prediction accuracy is remarkably improved, the method does not depend on experimental data, and the method is suitable for efficient metabolic network model correction and optimization.

This invention discloses a method for simulating and optimizing fermentation processes based on multi-scale model coupling. The method includes: constructing a multi-scale model consisting of three sub-modules: physicochemical environment, cell physiological state, and cell metabolic network model; establishing a global feedback coupling mechanism for bidirectional variable transfer and real-time feedback among the three sub-modules; constructing a three-dimensional reactor model based on computational fluid dynamics software, and after initialization of the sub-modules, spatially discretizing it using the finite volume method and iteratively solving the equations of each sub-module using a coupled solver to obtain dynamic data of the fermentation process; and conducting optimization applications such as reactor design, process parameters, and strain modification based on the simulation results. This invention achieves cross-scale dynamic coupling simulation, reflects the interaction between the environment and cells during fermentation, improves simulation accuracy and prediction capability, and effectively solves the problems of decreased production efficiency and unstable product yield during industrial fermentation scale-up.