115 results about "Metabolic network" patented technology

The invention relates to a method for identifying a disease microorganism-metabolite combined marker based on a host-flora co-metabolism model. The method comprises the following steps: processing microbiome data; setting diet constraint conditions; determining classification composition of intestinal microorganisms of each individual, mapping abundance of each microorganism to a human microorganism genome scale metabolism model, and constructing an individualized flora metabolism network model; carrying out metabolic flow simulation on the constructed flora metabolism network model; integrating the constructed flora metabolism network model with the host metabolism network model, and predicting the metabolic flow and metabolite change of the whole system; and establishing a disease classifier based on a random forest machine learning algorithm to identify the microorganism-metabolite combined marker. According to the method, the complex metabolic process between the human body and the intestinal flora is restored in a metabolic network modeling and metabolic flow simulation mode, the method has the non-invasive characteristic, the time and economic cost is remarkably reduced, and meanwhile, the method has a great advantage in accuracy.

The invention relates to a construction method of escherichia coli engineering bacteria with high yield of N-acetylglucosamine. According to the method, the metabolic network of escherichia coli is directionally modified through metabolic engineering, the problems of'product re-decomposition ', by-product accumulation, poor genetic stability and the like of natural strains are solved, and efficient industrial production of GlcNAc is realized.

The invention relates to the technical field of bacterium enrichment nutrient solutions, in particular to a bacterium enrichment nutrient solution for fermentation and a preparation method of the bacterium enrichment nutrient solution. The culture medium is prepared from the following raw materials in parts by weight: maltodextrin, crystalline glucose, low-pigment yeast extract powder, refined soy peptone, dipotassium phosphate, monopotassium phosphate, magnesium sulfate heptahydrate, sodium chloride, phytic acid, L-ascorbic acid, manganese sulfate monohydrate, biotin, thiamine hydrochloride and choline chloride. Through a double-carbon-source structure, maltodextrin and crystalline glucose cooperate, metabolic oscillation is avoided, and the energy utilization rate is increased; the low-pigment yeast extract powder and the refined soy peptone are complementary, so that the rapid growth requirement of thalli is met; phytic acid is chelated with metal ions to prevent precipitation, L-ascorbic acid is used for resisting oxidation, and biotin and thiamine activate a coenzyme pathway to realize accurate regulation and control of a metabolic network.

The invention discloses a prediction method for identifying a protein-protein interaction site. According to the method, a deep learning framework is constructed based on protein chain hierarchical graph construction and double-branch graph neural network learning. The method comprises the following steps: firstly, constructing a corresponding hierarchical graph for each protein chain, including a residue-level graph and an atomic-level graph; next, the method constructs an isotropic graph neural network (EGNN) module to extract residue-level embedding with isotropic features. After a GraphSAGE module is further used for extracting atomic-scale embedding, the method integrates layered graph features by using a comparative learning strategy so as to learn residue-scale and atomic-scale consistency embedding. Finally, an improved gated multi-head attention mechanism is used for fusion embedding weighting, and residue-level interaction classification prediction is performed through a multi-layer perceptron. A comprehensive evaluation result on a plurality of data sets shows that the method is remarkably superior to an existing method. In conclusion, according to the method, various advanced technologies such as graph structure denaturation, multi-scale modeling and comparative learning are fused, and an accurate and steady solution with expansion potential is provided for protein interaction prediction. The method can assist researchers in deeply recognizing key protein acting sites and promoting analysis work of cell signal channels and metabolic networks.

The invention relates to the technical field of bioinformatics, particularly discloses a metabolite target interaction prediction method and system for myocardial injury, and aims to solve the problems that complex heterogeneous data processing is insufficient, and dynamic changes of a metabolic network are difficult to capture and fine time sequence prediction is difficult in the prior art. According to the method, multi-source heterogeneous data is integrated, a myocardial injury risk factor and metabolite target knowledge base is constructed, and machine learning and deep learning technologies are utilized to realize risk factor weighting and static and sequential dynamic risk assessment. By fusing the static risk level and the dynamic adjustment factor, the system can predict the metabolite target interaction related to the individual high-risk state and generate personalized prevention and intervention strategy suggestions according to the metabolite target interaction. The system comprises a plurality of functional modules including data acquisition and preprocessing, knowledge base construction, feature engineering, risk modeling, time sequence optimization, target prediction and the like, so that early, dynamic and accurate prediction of myocardial injury risks and recognition of personalized intervention targets are realized.

The invention provides a comprehensive energy efficiency evaluation method and system for a smart park, and the method comprises the steps: constructing a nonlinear metabolism matrix reflecting the energy incidence relation between equipment, and solving the optimal metabolic flux distribution through mixed integer nonlinear programming; defining an interaction rule base with a metabolic network, dynamically triggering rules according to real-time sensor data, and adjusting real-time metabolic flux; calculating the quantized value of each current energy efficiency index according to the real-time metabolic flux, quantifying the uncertainty through a fuzzy entropy theory, dynamically distributing multi-target weights, and calculating a comprehensive energy efficiency score; and calculating the quantized value of each energy efficiency index in the optimal state according to the optimal metabolic flux distribution, comparing the fuzzy entropy, determining a to-be-optimized energy efficiency index, and identifying high risks and potential risks to formulate an optimization strategy. According to the method, various equipment and energy flow conditions in the park are considered, the energy efficiency level of the system is accurately reflected, dynamic adjustment and evaluation are carried out according to real-time data, uncertainty factors of the energy system are identified and solved, and efficient and stable operation of the system is guaranteed.

The invention discloses a saccharomyces cerevisiae recombinant strain based on a uracil metabolism gene URA3 as well as a construction method and application of the saccharomyces cerevisiae recombinant strain. The construction method comprises the following steps: taking saccharomyces cerevisiae rich in farnesyl pyrophosphate as an original strain, introducing a uracil metabolism gene URA3, synchronously overexpressing a ribosome biosynthetic gene UTP10 and an iron metabolism gene FIT3, and utilizing the uracil metabolism gene URA3 to express up-regulation correlation with the ribosome biosynthetic gene UTP10 and the iron metabolism gene FIT3, so as to obtain the farnesyl pyrophosphate-rich saccharomyces cerevisiae. The metabolic flux of the terpenoids is enhanced, and the saccharomyces cerevisiae recombinant strain for efficiently producing the terpenoids is constructed. According to the method, a metabolic network of yeast is systematically optimized, and combined regulation and control on precursor flux distribution, protein translation capability and metal ion steady state are realized, so that the microbial synthesis efficiency of sesquiterpenoids such as patchouli alcohol and beta-elemene is remarkably improved.

The invention provides a preparation method of 11-seed fermentation oil and fermentation liquor with multiple effects, and belongs to the technical field of fermentation. The method comprises the following steps: S1, preparing recombinant saccharomycetes; s2, jointly fermenting lactobacillus, recombinant saccharomycetes and 11 seed oil; s3, separating an oil phase and a water phase, and taking the oil phase; and S4, dehydrating the oil phase to obtain the 11-seed fermentation oil. And S5, further refining the water phase to obtain the 11-seed fermentation liquor. The 11-seed fermented oil prepared by transforming a microbial metabolic network is rich in dihydroquercetin, and has multiple effects of resisting oxidation, resisting wrinkles, tightening, repairing cells and the like. In the fermentation process of the 11-seed fermentation oil, 11-seed fermentation liquor can be obtained, and the fermentation liquor has multiple effects of resisting oxidation, relieving, moisturizing, resisting wrinkles, tightening and the like.

The invention discloses a method for improving the self-luminous intensity of a plant, which is characterized in that an AroGL175Q gene or an RgTAL gene and a PtC3H gene or the AroGL175Q gene, the RgTAL gene and the PtC3H gene are expressed in a plant containing a fungus bioluminescence system FBP, so that a transgenic plant of which the caffeic acid content, the milk tree alkali content and the luminous intensity are obviously increased is obtained. According to the invention, through metabolic network optimization and transformation, phenylpropane metabolites required by middle and upper streams of a fungus bioluminescence pathway are increased, and synthesis of caffeic acid and milk tree alkali in plants is increased, so that bioluminescence enhancement is realized. The precursor supply bottleneck of a fungus bioluminescence system is broken through, and an innovative solution is provided for creating efficient self-luminous plants.

The disclosed multiscale framework includes innovatively coupling genome-scale metabolic models with an agent-based model and an adapted continuum model of the biofilm; employing a systematic bottom-up approach to identify interrelationships between local substrate and mediator transport and the dynamic biofilm characteristics; and elucidating the interdependence of genomic data and microscale biofilm properties, thereby enabling a deeper understanding of the behavior of species within the biofilm.

This invention provides a method for hypertension risk assessment, relating to the field of epigenetic detection technology. It includes: performing event alignment and residual modeling on a qualified signal set to establish a base probability at the locus level and forming baseline features through neighborhood consistency screening; constructing a tunneling sensitization model and contextual attention, fusing discriminative and generative evidence to obtain enhanced features; quantifying IGF2BP3 and aligning and fusing it with the enhanced features; constructing an IGF2BP3-mediated metabolic network based on this, performing time-series modeling and stability assessment, and extracting final-state features; fusing the final-state risk vector with multi-gene scores to complete adaptive grading and compliant report generation. This method is robust, interpretable, and easy to deploy.

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 invention discloses a preparation method and application of high-activity recombinant collagen, and relates to the field of biological medicine, and the preparation method comprises the following steps: S1, providing engineering bacteria containing a recombinant collagen expression vector, and performing seed culture; s2, inoculating the seed solution into a fermentation culture medium for fermentation culture; s3, in the fermentation process, when the thallus density reaches a preset value, a signal factor without an animal source is added into a fermentation system, and the signal factor is bionic polypeptide; s4, after the signal factors are added, the fermentation conditions are adjusted, and target collagen expression is induced; according to the preparation method and the application of the high-activity recombinant collagen, an endogenous metabolic network of bacteria is actively regulated and controlled by accurately adding animal-source-free bionic polypeptide signal factors in the fermentation process of engineering bacteria, and correct folding and specific post-translational modification of target collagen are induced; therefore, the hydroxylation rate and the biological activity of the recombinant collagen are improved, and the structure and the function of the recombinant collagen are closer to those of natural collagen of a human body.

The invention discloses a substance basis and a detection method for the drug effect of liver-bean jelly, and belongs to the technical field of medicines. In-vitro chemical components of liver-bean jelly are combined to track blood entering components of liver-bean jelly and carry out attribution analysis; obtaining an action target spot of the liver-bean linging for intervening the WD hepatic fibrosis; screening metabolic markers for significant callback of liver-bean linging, and performing enrichment analysis to obtain a significant metabolic pathway; constructing a'small molecule metabolite-reaction-enzyme-target 'composite metabolic network by the action target and the significant callback metabolic marker, and screening a core action target, a core metabolic marker and a core metabolic pathway; a molecular docking technology is used for screening a liver-bean drug effect material basis, and a multi-dimensional network of the liver-bean drug effect material basis, a core action target, a core metabolic marker, a core metabolic pathway and WD liver fibrosis is visualized. The invention discloses a pharmacodynamic material basis and a metabolic regulation mechanism for treating WD hepatic fibrosis by using liver bean paste.

The embodiment of the invention discloses a construction method of a multi-source organic solid waste whole-process substance flow and energy flow metabolic network diagram and a related device. A basic theoretical analysis and research framework is set up; constructing an urban typical organic solid waste material flow model according to the basic theoretical analysis and research framework; and according to the urban typical organic solid waste material flow model, constructing a Yifen city typical organic solid waste metabolic network. It can be seen that the embodiment of the invention is beneficial for solving the following problems: 1, composition and characteristics of the organic solid waste can be better understood by establishing a substance flow and energy flow metabolic network diagram, so that a more efficient and more economical treatment process is designed; 2, through the metabolic network diagram, the influence of the organic solid waste treatment process on the environment can be better known, so that measures are taken to reduce the influence of the organic solid waste on the environment.

The invention relates to the field of food biotechnology and fermentation engineering, and discloses lipid-lowering health-preserving vinegar capable of strengthening an amino acid metabolic pathway and a preparation process thereof, and the lipid-lowering health-preserving vinegar capable of strengthening the amino acid metabolic pathway is prepared by fermenting soybean protein isolate, silkworm chrysalis protein powder, broken rice, oat bran, a metabolism anchoring regulation composite emulsion and a plant extract. According to the metabolism anchoring regulation composite emulsion, carrier oligopeptide, pyridoxal-5-phosphoric acid and metal ions are utilized to construct a coordination system, and the microbial amino acid metabolism flow direction is directionally regulated. The preparation process is completed by adopting lactobacillus plantarum and acetobacter pasteurianus through anaerobic-aerobic variable-temperature variable-dissolved-oxygen coupling fermentation. Through a metabolic network intervention strategy, the enrichment efficiency of gamma-aminobutyric acid and hydrophobic active oligopeptide in the finished product is remarkably improved, and the flavor of the product is improved, so that the table vinegar is endowed with stronger auxiliary lipid-lowering and pressure-lowering effects.

The invention relates to a method for efficiently synthesizing lysine by corynebacterium glutamicum based on multi-level metabolism reconstruction, and belongs to the technical field of metabolic engineering. The method is not limited to single gene or single path transformation, but carries out system integrated regulation and control from multiple levels of lysine main synthesis pathway, central carbon metabolism redistribution, by-product synthesis inhibition and lysine transport and tolerance mechanism, and constructs a ''flux-energy-tolerance'' synergistically matched integral metabolism network; the method is a fundamental technical framework for realizing 240g / L high yield. On the premise that genetic stability of engineering bacteria and controllability of the fermentation process are guaranteed, stable and efficient synthesis of lysine under the high-density fermentation condition is achieved, the final fermentation concentration of lysine stably reaches 240 g / L or above, meanwhile, high carbon source conversion efficiency and low by-product generation level are both considered, and the method is suitable for industrial production. Therefore, the technical bottleneck that the existing lysine biological manufacturing technology is limited to the platform stage of yield of about 200 g / L for a long time is broken through.

The invention belongs to the technical field of biological medicines, and particularly relates to an application of LAP3 and an agonist thereof in macrophage immune inflammation mediated diseases. The invention provides application of leucine aminopeptidase 3 or leucine aminopeptidase 3 agonist substances in preparation of drugs for prevention, treatment and / or adjuvant treatment of macrophage immune inflammation mediated diseases. According to the invention, it is found for the first time that the expression level of LAP3 in macrophages is increased under an acute stress condition, and the limiting effect of LAP3 on generation and development of sepsis and acute hepatitis is disclosed; the invention discloses that the knockout of the LAP3 can remodel a macrophage metabolic network, reduce oxidative phosphorylation, promote ROS (reactive oxygen species) production and activate TAK1, thereby promoting the activation of a downstream inflammation signal channel and finally promoting macrophage inflammation. A new target spot and a theoretical basis can be provided for clinical diagnosis and treatment of macrophage-mediated inflammation by illuminating the effect of LAP3 on regulating and controlling macrophage inflammation and a molecular mechanism of LAP3.

The application discloses a kind of based on multi-omics deep learning colorectal cancer liver metastasis detection method and system, comprising: extracting the feature gene group with CRC transfer-metabolic dual function, and construct CRC transfer risk prediction model foundation, based on analysis CRC transfer risk probability to screen the molecular marker with CRC transfer nature and CRLM specific characteristics as initial candidate biomarker;Meanwhile, from the metabolome to obtain the differential expression metabolite corresponding to CRLM patient sample and LCRC patient sample metabolome, and the enrichment pathway analysis of transcriptome and metabolome is combined to construct CRLM multi-layer core metabolic network, for the screening core biomarker of colorectal cancer liver metastasis accurate detection in accordance with, entire method can effectively screen the biomarker for detecting colorectal cancer liver metastasis.

The invention relates to the technical field of neonatal inherited metabolic disease screening, and discloses a neonatal inherited metabolic disease screening auxiliary system. The system comprises a multi-source information acquisition module, an information standardization module, a dynamic metabolism image construction module, an anomaly detection and analysis module and a screening management optimization module. The system integrates blood metabolites, gene variation, clinical signs and other multi-source information of newborns, constructs a metabolic function image showing dynamic changes and interaction relations of the metabolites, recognizes metabolic abnormality areas and development situations according to the metabolic function image, and generates a personalized screening management plan. Metabolic network visualization analysis is realized through a dynamic image, and an anomaly detection algorithm is continuously optimized by utilizing planned execution feedback data. According to the system, the trend evaluation of the metabolic state and the self-adaptive iteration of the screening strategy are realized, and the screening accuracy and the management individualization level are improved.

The application relates to a whole-body organ metabolism network analysis method, device, equipment and storage medium. The method comprises the following steps: collecting a PET image of a subject, obtaining SUV images, Ki images and TAC data according to the PET image, and collecting physiological information, clinical pathology information and daily habit information of the subject; metabolism characteristics are extracted based on the PET images, SUV images and Ki images corresponding to the region of interest; the TAC data of each organ is curve-fitted by using the PET image, and the metabolism disturbance under different organs is represented by using the fitting residual error; a whole-body organ metabolism network is constructed according to the physiological information, clinical pathology information, daily habit information, metabolism characteristics and metabolism disturbance; and the whole-body organ metabolism of the subject is analyzed through the whole-body organ metabolism network. The application avoids individual differences, can more comprehensively capture the metabolism correlation among whole-body organs of a specific disease, and is helpful for early diagnosis, accurate evaluation and individualized treatment of clinical diseases.

The invention provides a medical image classification model training method and system, a medical image classification method, equipment and a medium, and the method comprises the steps: carrying out the time-sequence feature extraction of a time sequence of a resting-state functional medical image through an encoder, so as to obtain the multi-scale features of the resting-state functional medical image; performing feature extraction processing on the metabolic network of the functional state medical image to obtain features of the functional state medical image; processing the multi-scale features and the features of the functional state medical image by using an adapter to obtain feature pairs of the resting state functional medical image and the functional metabolism medical image; performing constraint processing on each feature pair by using comparative learning to obtain an aligned feature pair; and training the initial model by using the aligned features, and performing group-level modal distillation processing on the initial model to obtain a medical image classification model. According to the medical image classification model training method, the flexibility and robustness of cross-modal feature fusion can be improved.

This invention, based on the screening and identification of environmental exocomics and metabolomics biomarkers associated with ischemic stroke, discloses an ischemic stroke risk assessment model and model construction method that integrates environmental exocomics and metabolomics biomarkers, improving the comprehensiveness, accuracy, and convenience of prediction. This invention systematically evaluates the association between exposure to mixed organic pollutants and the risk of ischemic stroke, identifying key pollutants; quantifies the impact of pollutant mixtures on the systemic metabolic network and stroke risk, revealing the mediating role of metabolic pathway disorders in pollutant-induced stroke risk; explores the potential of incorporating pollutant exposure information into clinical prediction models to improve the efficacy of ischemic stroke risk stratification, ultimately providing systematic multi-omics evidence for the causal relationship between environmental pollution and ischemic stroke, and offering new technical tools for precision disease prevention.

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.

The invention belongs to the technical field of gene therapy and biological medicine, and particularly provides application of leucine in preparation of a medicine for preventing and / or treating aortic aneurysm. The leucine is independently used or is combined with other medicines for preventing and / or treating aortic aneurysm. The activity of GLUD1 is enhanced through leucine, so that conversion of Glu to alpha-KG is promoted, degradation of Glu is promoted, namely, the Glu level is reduced by enhancing degradation of Glu, and aortic aneurysm is improved. In a Glu metabolic network, Gln can be converted into Glu under the action of glutaminase 1 (GLS1), so that the generation of Glu is inhibited by inhibiting the activity of GLS1, and the effect of preventing and / or treating aortic aneurysm is achieved.

To provide a method for determining odor of fish, especially odor deterioration and lipid oxidation degree of tilapia during cold storage.SOLUTION: The method includes the steps of: killing fresh tilapia, slicing, washing, mixing and grinding the fish meat to obtain raw materials; extracting lipids from the raw materials; dividing the obtained lipids and storing them under low-temperature conditions, and after storage, measuring the lipid oxidation degree and lipid metabolic data of tilapia at different refrigeration times using lipid metabolomics technology; constructing an odor-related lipid metabolic network and analyzing the lipid oxidation degree and metabolic data at different refrigeration times based on the lipid metabolic network to obtain metabolic markers corresponding to multiple lipids; screening the obtained metabolic markers to obtain evaluation metabolic markers; and analyzing the oxidation degree and metabolic status of multiple lipids based on the evaluation metabolic markers corresponding to multiple lipids.SELECTED DRAWING: Figure 1

The invention relates to the technical field of intelligent breeding, and discloses a sugarcane filial generation phenotype prediction and evaluation system, which comprises a genetic effect decomposition module, a metabolic pathway constraint module, a dynamic sensitivity bridging matrix construction module and a dynamic constraint optimization module which are sequentially executed and mutually coupled, wherein the metabolic pathway constraint module is used for executing flux equilibrium analysis based on a sugarcane core metabolic network and upper and lower bounds of enzyme activity mapped by parent QTL (Quantitative Trait Loci), generating an optimal flux solution of parents and constructing a filial generation flux feasible region; and solving an optimal phenotype trajectory by adopting sequential quadratic programming and calculating a comprehensive evaluation index. In the invention, a three-order epistatic tensor is constructed through a genetic effect decomposition module, tensor decomposition is carried out, an epistatic contribution vector of a parent is extracted, and a hybrid progeny flux feasible region is generated by combining flux equilibrium analysis of a metabolic pathway constraint module, so that a system can quantify a non-additive genetic effect and metabolic constraint before hybridization; and phenotype prediction is separated from field measured data dependence.

The invention discloses a colorectal cancer liver metastasis detection method and system based on multi-omics deep learning, and the method comprises the steps: extracting a characteristic gene group with CRC metastasis-metabolism dual functions, and on the basis of constructing a CRC metastasis risk prediction model, screening molecular markers with CRC metastasis general and CRLM specific characteristics based on the analysis of CRC metastasis risk probability, the biomarker is used as an initial candidate biomarker; meanwhile, a CRLM patient sample and differential expression metabolites corresponding to an LCRC patient sample metabolome are obtained from a metabolome, and a CRLM multilayer core metabolic network is constructed in combination with enrichment pathway analysis of a transcriptome and the metabolome, so that core biomarkers are screened for accurate detection of colorectal cancer liver metastasis; the whole method can effectively screen the biomarker beneficial to detection of colorectal cancer liver metastasis.

The invention relates to the field of genetic engineering and bioengineering, in particular to engineering algae capable of synthesizing alpha-farnesene and a preparation method of the engineering algae. An MEP metabolic network of synechocystis PCC 6803 is modified, through single-copy series recombination and multi-copy combination of dxs, ispA, idi and AFS genes, optimization of a farnesene MEP metabolic pathway is achieved, the carbon flux of oriented synthesis of farnesene is precisely regulated and controlled, under the optimal effect, the yield of alpha-farnesene within 9 days is 83.07 mg / L (25.82 mg / g DCW), and economic application value is achieved.

The present application relates to the technical field of bioinformatics and disease prediction, and discloses a gout risk prediction model construction method based on metabolomics data. The method comprises the following steps: constructing a metabolite space layout atlas containing metabolite coordinates and connection strength based on historical gout case data; projecting real-time metabolomics data of a target individual to the atlas, calculating the spatial deviation amount of the distribution of the target individual from the benchmark structure, and generating an individual metabolic space deviation report; extracting a metabolite cluster deviating beyond a threshold and analyzing the topological characteristics thereof to generate a metabolic abnormality topological feature set; inputting the feature set into a pre-constructed metabolic state evolution network, simulating a disturbance propagation process, and outputting a metabolic state evolution path and potential imbalance nodes; and finally integrating the above information to generate a gout risk level prediction model. The present application quantifies the overall structural deviation of the metabolic network and simulates the dynamic evolution thereof, thereby achieving earlier and more accurate mechanism-based prediction of gout risk.