15 results about "Metabolic network model" patented technology

The invention discloses a method for improving yield of branched chain aldehyde of lactococcus lactis based on regulation and control of a luxS gene and application, and belongs to the field of system biology. Gene function verification and genome scale metabolic network model analysis are creatively combined, the phenotypic effect of the luxS gene is confirmed, and more importantly, the internal action mechanism of the luxS gene is disclosed from the system level, that is, the whole metabolic network flow is influenced by regulating and controlling multiple key enzymes such as transaminase, decarboxylase, dehydrogenase and decarboxylase. The invention reveals that the quorum sensing core gene luxS has a new function of regulating and controlling synthesis of branched chain aldehyde in lactococcus lactis for the first time, and an intercellular communication system is directly linked with production of specific flavor metabolites. The invention provides a brand-new and higher-level regulation target and thought for producing flavor substances by microbial fermentation, and gets rid of the limitation that only metabolic pathway terminal enzyme is singly modified traditionally.

The application discloses a high-sensitivity parameter determination and optimization method of an enzyme-constrained metabolic network model. First, a sensitivity analysis method is used to sort parameters in the model according to contributions to comparative growth rate prediction results from high to low, and parameters with higher contributions are selected for optimization. Then, a differential evolution algorithm with an adaptive mutation strategy is used for parameter optimization. The algorithm can adaptively select a mutation strategy to improve optimization efficiency. Comparative growth rate data under different growth conditions are obtained from a database, and part of the training set is selected from the data. Finally, a performance evaluation result of the optimized model is obtained through a flux variability and phase plane analysis method. The high-sensitivity parameter is a turnover number kcat parameter of an enzyme.

The invention belongs to the field of microbial engineering, and provides a gentamicin C1a high-yield biological manufacturing method based on metabolic network modeling and reverse metabolic engineering renovation, which comprises the following steps: S1, constructing a genome scale metabolic model of a target production strain, predicting and screening a first target gene in positive correlation with gentamicin biosynthesis by using the model; s2, performing whole genome sequencing on the gentamicin high-yield mutant strain, and identifying a second target gene related to the high-yield character through comparative genomics analysis; s3, combining the first target gene predicted in the step S1 and the second target gene identified in the step S2, and constructing to obtain a recombinant engineering strain; s4, carrying out fermentation culture on the recombinant engineering strain, and supplementing materials in the fermentation process; by remarkably improving the production efficiency and strain performance of gentamicin C1a, important technical support and theoretical basis are provided for green biological manufacturing of antibiotics.

This invention discloses a method for constructing and analyzing a genome-scale metabolic network model of *Paragonimus denitrifyingus*, belonging to the field of systems biology. The method includes: whole-genome annotation; obtaining global metabolic response data of *Paragonimus denitrifyingus*; automatically retrieving genomic information and constructing Model 1 based on the species code and genome annotation results of *Paragonimus denitrifyingus* in the KEGG database; constructing Model 2 by identifying homologous proteins in the *Paragonimus denitrifyingus* genome through homology searching of proteins in the target organism based on a pre-trained Hidden Markov Model; and integrating Model 1 and Model 2. This invention allows for the efficient design and modification of denitrification engineering, achieving precise control of nitrogen degradation processes. Compared to existing metabolic engineering methods, this invention effectively reduces the workload of exploratory experiments and greatly advances a deeper understanding of the nitrogen degradation characteristics of *Paragonimus denitrifyingus*.

The embodiment of the invention provides a metabolic flux detection method and system based on a genome scale metabolic network model, and belongs to the technical field of biological metabolic flux detection. The method comprises the following steps: acquiring a DCW sequence, a gas inlet change rate, a tail gas change rate and a glucose concentration sequence of fermentation liquor to be measured; determining the apparent specific growth rate of the fermentation liquor based on a multi-dimensional kinetic model; determining global metabolic flux distribution of the fermentation liquor according to the DCW sequence, the inlet gas concentration sequence, the tail gas concentration sequence, the glucose concentration sequence and the apparent specific growth rate by adopting a QP-pFBA algorithm; and carrying out metabolic flux detection according to the global metabolic flux distribution. The method and the system can predict the metabolic flux of the fermentation liquor.

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.

The application discloses a kind of dynamic enzyme constraint genome scale metabolic network model construction methods, comprising the following steps: step S1, determine kinetic model, obtain the specific numerical value of reaction flux and the change of metabolite with time;Step S2, according to the same reaction in ecGEMs upper and lower bound constraint of different reaction flux, the flux change condition of all reactions of organism is obtained by FBA solution with time change;Step S3, the model is verified.The ecGEMs can be made dynamic, the problem that ecGEMs cannot obtain metabolite concentration and intracellular reaction flux change with time is solved.And greatly improve the accuracy of ecGEMs model, it has important significance to the research system biology and synthetic biology research.

The application discloses a bacillus amyloliquefaciens genome-scale metabolic network model, a construction method and application, and belongs to the field of system biology. The method comprises the following steps: firstly, a rough model of bacillus amyloliquefaciens is automatically constructed according to protein sequencing results of bacillus amyloliquefaciens in a Uniport database; secondly, the rough model is manually refined and the synthesis reaction of acetoin is added according to a literature database, a metabolic pathway diagram and a biochemical database, so as to construct a genome-scale metabolic network model; thirdly, the genome-scale metabolic network model is converted into a computer-readable mathematical model; and finally, the mathematical model is analyzed by using a flux balance analysis method, and compared with experimental values in the literature. The method has the advantages of short construction time, simple operation and high accuracy, can systematically predict the nutritional conditions for the growth of bacillus amyloliquefaciens, and provides a feasible basis for improving the yield of acetoin and industrial production.

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 belongs to the technical field of system biology, and discloses a genome scale metabolic network model construction method, which comprises the following steps: constructing a biochemical reaction data set, a metabolite data set and a gene-protein-reaction rule data set; performing gene function annotation and screening; performing biochemical reaction mapping, constructing a biochemical reaction set, and adding related metabolite data set information; mapping and adding local biomass equations corresponding to similar species into a biochemical reaction set to generate an initial genome scale metabolic network model; and sequentially carrying out vacancy filling, redundant reaction removal and annotation information addition processing on the initial genome scale metabolic network model to obtain a final genome scale metabolic network model. According to the method, the defects of an existing GEMs construction tool in the aspects of model coverage and prediction precision are overcome, the automatic de novo construction process from the genome sequence to the GEMs is developed, the time-consuming manual correction process is reduced, and the construction efficiency is improved.

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.

The invention relates to a gene editing cell metabolic flow dynamic analysis method, system, equipment and medium, and the method comprises the following steps: carrying out mass spectrometry on a gene editing cell sample, identifying a metabolic intermediate in the gene editing cell sample, and obtaining dynamic change data; carrying out derivatization detection on the gene editing cell sample, and obtaining organic acid concentration data in a semi-quantitative manner; and processing the dynamic change data and the organic acid concentration data through a dynamic metabolism network model, and outputting metabolic flow dynamic data for quantitatively representing the state of a metabolic pathway, so as to achieve the purposes of dynamically and quantitatively analyzing the metabolic function of the gene editing cell and directly guiding the optimization of a gene editing strategy.

The invention relates to the technical field of fermentation engineering, and particularly discloses a collaborative optimization system for fusing a metabolic network model and fermentation process mechanism analysis based on data driving, which comprises the following steps: S1, collecting and constructing process data; s2, metabolic flux simulation and analysis; s3, multi-source data fusion is carried out; s4, machine learning modeling is carried out; s5, identifying key metabolic nodes; s6, optimization strategy design and implementation; by constructing a GEM-ML collaborative optimization framework, systematic fusion and analysis of multi-source data in the fermentation process are realized, and the limitation of a single method is overcome, so that key metabolic nodes influencing product synthesis can be identified more efficiently and more accurately, and a targeted optimization strategy is generated according to the key metabolic nodes.

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.

The invention discloses a hepatic failure short-term prognosis prediction method fusing multi-scale features, and relates to the technical field of medical prognosis prediction.The method comprises the steps that serum proteome and clinical data of a patient after admission and 24 hours are collected; constructing a hepatocyte core metabolism network calculation model; converting the serum protein concentration change into a protein characteristic energy value; disturbing the metabolism model by using a protein energy value and extracting virtual metabolism flux characteristics; extracting multi-scale features from the clinical time series data; carrying out nonlinear fusion on the metabolic flux characteristic and the clinical characteristic to generate a prognosis index; and outputting a death risk classification result according to the prognosis index. According to the method, the liver cell specific metabolic network model is constructed, the serum proteomics time sequence data is mapped into the quantitative flux characteristics reflecting the liver core metabolic function state, nonlinear coupling is carried out on the quantitative flux characteristics and the clinical time sequence indexes, and quantitative evaluation on the liver dynamic metabolic disorder can be achieved; therefore, comprehensive judgment of short-term prognosis of the patient is completed.