Construction and Analysis Methods of Genome-Scale Metabolic Network Model of Paranitrogenous Denitrifying Cocci

Abstract

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*.

Description

Technical Field

[0001] This invention relates to the construction and analysis methods of genome-scale metabolic network models of denitrifying paracocci, belonging to the field of systems biology technology. Background Technology

[0002] Paranitrogenous bacteria (P. denitrifyingus) are Gram-negative bacteria widely found in soil and sludge. They can denitrify nitrates under anaerobic or aerobic conditions to produce gaseous nitrogen. Due to their excellent nitrogen degradation and environmentally friendly characteristics, they are potential biological denitrifying strains for bioremediation and wastewater treatment. However, naturally occurring Paranitrogenous bacteria typically grow slowly, and their nitrogen degradation rate is affected by various factors, failing to meet industrial denitrification requirements. With the development of biotechnology, researchers have focused on traditional metabolic engineering to improve the denitrification capacity of Paranitrogenous bacteria, using genetic engineering techniques to locally knock out or overexpress genes involved in nitrogen degradation pathways. However, localized modifications are somewhat indiscriminate, failing to achieve precise control over efficient nitrogen degradation, and the results still fall short of practical needs. Therefore, a systematic and comprehensive understanding of Paranitrogenous bacteria is needed to efficiently design and modify their denitrification function. Summary of the Invention

[0003] To improve the efficiency of denitrification engineering design and modification, and to achieve precise control over the nitrogen degradation process of *Paracococcus denitrifyingans*, this invention provides a method for constructing and analyzing a genome-scale metabolic network model of *Paracococcus denitrifyingans*. The technical solution is as follows:

[0004] The method for constructing the genome-scale metabolic network model of denitrifying paracocci of the present invention includes:

[0005] Step 1: Perform whole-genome annotation based on the published genome sequencing results of *Paracoccus denitrificans*;

[0006] Step 2: Obtain global metabolic response data of Paracoccus denitrifyingus, extract key information of metabolic responses, and standardize the names of metabolic responses and metabolites;

[0007] Step 3: Using the RAVEN toolbox of the MATLAB platform, based on the species code and genome annotation results of Paracoccus denitrifying in the KEGG database, automatically retrieve genome information and construct Model 1;

[0008] Step 4: Using the RAVEN toolbox on the MATLAB platform, based on the species code, genome annotation results and pre-trained Hidden Markov model of Paracoccus denitrification in the KEGG database, homology search is performed on the proteins of the target organism to identify homologous proteins in the genome of Paracoccus denitrification, and additional metabolic reactions are added to construct Model 2.

[0009] Step 5: Integrate the two automatically constructed Model 1 and Model 2 to obtain the genome-scale metabolic network model of Paracoccus denitrification.

[0010] Optionally, the method further includes manually refining and optimizing the model, including: adding exchange reactions based on existing metabolic models, adding transport reactions based on transporter protein identification results, deleting duplicate reactions, performing mass balance checks, and setting reversibility.

[0011] Optionally, step 1 may employ the Prokka annotation tool.

[0012] Optionally, step 1 involves downloading the complete genome file of *Paragonimus denitrificans* from the NCBI database.

[0013] Optionally, step 2 uses the API provided by the KEGG database to download the KGML file.

[0014] Optionally, step 3 uses the getKEGGModelForOrganism function from the RAVEN toolbox to construct model 1.

[0015] This invention provides a method for analyzing key metabolic pathways affecting nitrogen metabolism, comprising: converting a genome-scale metabolic network model of denitrifying paracoccus constructed by any of the methods described above into a computer-recognizable mathematical coefficient matrix; using the flow balance analysis tool in the RAVEN toolbox of the MATLAB platform, with the maximization of nitrogen synthesis as the objective function, calculating the metabolic flux distribution of other metabolic pathways, and identifying key metabolic pathways.

[0016] This invention provides a method for analyzing bottleneck metabolic reactions affecting nitrogen metabolism, comprising: converting a genome-scale metabolic network model of denitrifying Paracoccus denitrifying bacteria constructed by any of the methods described above into a computer-recognizable mathematical coefficient matrix; using the flow variation analysis tool in the RAVEN toolbox of the MATLAB platform to calculate the minimum and maximum fluxes of other metabolic reactions; and identifying bottleneck reactions by comparing the width of these flux variation intervals.

[0017] This invention provides an electronic device, including a memory and a processor;

[0018] The memory is used to store computer programs;

[0019] The processor is configured to, when executing the computer program, implement the method as described in any of the preceding methods.

[0020] The present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the preceding claims.

[0021] The beneficial effects of this invention are:

[0022] This invention, based on the large-scale genome data of *Paragonimus denitrifyingus*, begins with genome sequence annotation, combines various reaction and enzymatic databases to obtain gene-protein-reaction relationships, and then constructs a genome-scale metabolic network of *Paragonimus denitrifyingus*. Based on this constructed genome-scale metabolic network model, systematic, whole-cell-level metabolic simulations of *Paragonimus denitrifyingus* are performed to identify potential metabolic pathways for enhanced denitrification, providing a reference for experimental design. Combined with flow balance and flow variability analysis methods, denitrification engineering can be efficiently designed and modified, achieving precise control of the nitrogen degradation process. 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*. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating the method for constructing the genome-scale metabolic network model of denitrifying paracoccus according to the present invention.

[0025] Figure 2 To improve the distribution of target metabolic pathways for nitrogen degradation in flow balance analysis.

[0026] Figure 3 A schematic diagram illustrating the degree of variability in bottleneck metabolic responses for nitrogen degradation in order to improve flow variability analysis. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0028] The implementation process of this invention mainly includes the following:

[0029] (1) Gene function annotation of denitrifying paracocci.

[0030] In order to control denitrifying paracocci ( Paracoccus denitrificans Functional annotation of the genome of pde was performed, and the Prokka annotation tool was used for analysis in this embodiment.

[0031] First, from the NCBI database ( https: / / www.ncbi.nlm.nih.gov / datasets / genome / Download the complete Paracoccus denitrificans genome file GCA_000203895.1 in FASTA format. Ensure the filename and content meet Prokka's input requirements; name the file pde_genome.fasta. Simultaneously, install Prokka on your Linux system. The Prokka package is available from GitHub; the download link is [link missing]. https: / / github.com / tseemann / prokka.git After successful installation, enter the following command in the terminal:

[0032] prokka --outdir annotation_output --prefix DenitrifyingParacoccus\pde_genome.fasta

[0033] This tool automatically predicts and annotates functional genes such as protein-coding genes, rRNA, and tRNA. Combining database information, it assigns functional tags to genes based on sequence similarity and conserved region characteristics. Prokka generates annotation results in various file formats, including .gff (general feature format file), .gbk (GenBank format file), .tsv (tag file), and .faa (protein sequence file). These files record information such as gene function, gene category, protein name, and sequence location, which can be used for subsequent gene function analysis, metabolic pathway inference, and gene network construction.

[0034] (2) Collection of global metabolic response data of denitrifying paracocci.

[0035] To collect global metabolic response data for *Parasitic Paracoccus denitrifyingus*, KGML (KEGG-tagged files) was downloaded using the API provided by the KEGG database. KGML files contain structured information about metabolic pathways and serve as a reference data source for subsequent metabolic network construction. A Python script was used to write the API request script, calling the KEGG REST API to retrieve KEGML files in batches. The API request format is http: / / rest.kegg.jp / get / <pde> / kgml.

[0036] The KGML file was parsed, and the Python script xml.etree.ElementTree was used to extract key information about the metabolic reactions, including reactants, products, enzymes, genes, and their corresponding KEGG IDs. The extracted metabolic reaction data was saved to a CSV file, and information on the exact metabolic pathways was retrieved from the MetaCyc and BioCyc databases.

[0037] (3) Use RAVEN Toolbox to semi-automatically construct a preliminary metabolic network model.

[0038] A preliminary metabolic model of *Paragonimus denitrifyingus* was constructed using the RAVEN toolbox on the MATLAB platform. First, a preliminary model was built based on the KEGG database. The `getKEGGModelForOrganism` function in RAVEN (command: `model = getKEGGModelForOrganism('pde')`) automatically retrieved genomic information from the KEGG database based on the species code `pde` of *Paragonimus denitrifyingus* and constructed the preliminary metabolic model. This step will generate a model containing preliminary metabolic responses and gene association information.

[0039] Then, a Hidden Markov Model (HMM) was used to identify supplemental enzyme information from the protein sequence file of *Paragonimus denitrificans* (pd1222.faa, download link) obtained via the HMM method. https: / / www.uniprot.org The system uses a pre-trained Hidden Markov Model (HMM) library (prok90_kegg94) and runs the Python script `model2 = getKEGGModelForOrganism('pde', 'Pd1222.faa', 'prok90_kegg4')`. `prok90_kegg94` contains HMM models of various bacterial enzymes and functions. Based on this, the HMM evaluates the similarity between the protein sequences of *Paragonimus denitrificans* and the sequences in the model, determines whether similar proteins exist in the HMM model, and adds enzymes whose functions are not annotated. Finally, a preliminary metabolic model containing enzymes and reactions is constructed by merging the KEGG database and the HMM model. The structure of this metabolic network on the MATLAB platform is as follows: metabolic reactions and metabolites are composed of a stoichiometric matrix (S-matrix), where rows represent metabolites, columns represent metabolic reactions, and values ​​represent stoichiometric coefficients.

[0040] (4) Flow balance analysis.

[0041] Flow balance analysis is a metabolic network analysis method based on linear programming. The amount of each metabolite consumed or generated per unit time in the metabolic model refers to the metabolic flux. Since nitrogen is the final product of nitrogen degradation by Paranitrogenous bacteria, maximizing nitrogen synthesis is used as the objective function.

[0042] The model was loaded onto the MATLAB platform, and the objective function was set. The `optimizeCbModel` function from the RAVEN toolbox was used to perform flux balance analysis to determine the metabolic flux distribution of other metabolic pathways. This calculation process assumes that the cell is in a steady state, meaning that the rates of production and consumption of all metabolites in the cell are in equilibrium. Pathways with high metabolic flux are likely to contribute significantly to the target product, while those with low flux may not be key metabolic pathways for generating the target product.

[0043] (5) Flow variation analysis.

[0044] Flux variation analysis (FLVA) was used to study the flux variation range of each reaction under the condition of achieving optimal nitrogen synthesis. FLVA is used to analyze the flux variation range of reactions in a metabolic network, identify the flexibility or bottlenecks of metabolic pathways, and set maximizing nitrogen synthesis as the objective function. Further analysis of the flux variation range of each reaction was conducted. For each reaction flux, the maximum and minimum values ​​of the flux were optimized to keep the objective function within a certain range of the maximum value, typically 90% to 100%. By optimizing these two values, the variation range of other metabolic reactions was solved. This flux variation range can be calculated by subtracting the minimum value from the maximum value. A larger variation range indicates higher flexibility, a larger flux variation range, and less constraint on the formation of the target product; it is usually not a critical metabolic reaction. Conversely, a smaller flux variation range indicates a significant impact on the synthesis of the target product and can be considered a critical metabolic reaction. FLVA imposes high constraints on the objective function and is suitable as an optimization target for metabolic engineering.

[0045] Example 1: Method for constructing a genome-scale metabolic network model of Paranitrogenous denitrifying cocci.

[0046] This embodiment includes the following steps:

[0047] Step 1: Download the complete genome sequence of Paracoccus denitrificans GCA_000203895.1 from the NCBI database, and use the gene annotation tool Prokka to perform functional annotation on the genome to obtain the annotation results (faa format).

[0048] Step 2: Collect metabolic response data. Collect relevant information on metabolic response pathways from databases such as KEGG, MetaCyc, and BioCyc.

[0049] Standardize the collection of metabolic reaction and metabolite name information, use the BiGG database to find a list of all reactions and metabolites, and use Python to automatically create a BiGG reaction and metabolite mapping table corresponding to KEGG reactions.

[0050] Step 3: Construction of the preliminary model, specifically including the following steps:

[0051] Step 31: Using the getKEGGModelForOrganism function in the RAVEN toolbox on the MATLAB platform, construct Model 1 based on the species code (pde) and genome annotation results of Paracoccus denitrifying in the KEGG database;

[0052] Step 32: Using the getKEGGModelForOrganism function in the RAVEN toolbox on the MATLAB platform, based on the species code (pde) of Paracoccus denitrifying in the KEGG database, genome annotation results, and the pre-trained Hidden Markov Model (HMM) (prok90_kegg94), homology search is performed on the proteins of the target organism to identify homologous proteins in the genome of Paracoccus denitrifying, and additional metabolic reactions are added to construct Model 2;

[0053] Step 33: Integrate the two automatically constructed models, Model 1 and Model 2, to obtain a preliminary model of Paranitrogenous denitrifying bacteria.

[0054] Step 4: Manually refine and optimize the initial model, including:

[0055] Step 41: Standardize and unify the format of metabolic reaction and metabolite names, and replace the KEGG reaction names and metabolite names in the model with the corresponding reaction and metabolite names in the BiGG database to increase the model's versatility.

[0056] Step 42: Manually supplement the missing chemical formulas and names of metabolites. Prepare a table containing the chemical formulas and names of each metabolite from *Parasitic Paracoccus denitrificans*. Use a MATLAB script to read this file and update the metabolite information in the model. Set the chemical formulas of recycled complex cofactors, such as tRNAs, coenzyme A, and acyl carrier proteins, to "R".

[0057] Step 43: Referring to the existing metabolic model (E. coli model iML1515), 155 exchange reactions suitable for the metabolic needs of denitrifying paracocci were added to the model to allow the entry and exit of substances with the environment.

[0058] Step 44: Add to 156 transport reactions based on the results of transport protein identification;

[0059] Step 45: Delete 119 inappropriate responses;

[0060] Step 46: Perform a mass balance check on all reactions;

[0061] Step 47: Set the reversibility of each reaction in the model, specifying the upper and lower limits of reversible metabolic reactions as 1,000 to -1,000 mmol g. -1 h -1 The irreversible reaction was set to 0 to +1000 mmol g. -1 h -1 The upper and lower limits of the exchange reaction were set to -20 to 1000 mmol g. -1 h -1 The final result was a genome-scale metabolic network model of denitrifying paracocci containing 2236 genes, 2365 metabolites, and 2502 biochemical reactions.

[0062] The metabolites and metabolic response information included in the model are shown in Tables 1 and 2. The model construction process is as follows: Figure 1 As shown in Table 3, the databases and software used are listed below.

[0063] Table 1: Metabolic response information included in the model

[0064]

[0065] Table 2: Metabolite information included in the model

[0066]

[0067] Table 3: Databases and software used for model construction

[0068]

[0069] Example 2: Flow balance analysis of key metabolic pathways affecting nitrogen metabolism

[0070] The genome-scale metabolic network model of *Paragonimella denitrifyingis* constructed in Example 1 was converted into a computer-recognizable mathematical coefficient matrix. Using the MATLAB software platform and the flow balance analysis tool in the RAVEN toolbox, with nitrogen synthesis as the target reaction, the effects of other metabolic pathways on nitrogen degradation were determined. The simulation results are as follows: Figure 2 As shown.

[0071] The results showed that a total of 178 metabolic responses had high metabolic flux (Table 4 and...). Figure 1 Nitrogen compounds are mainly distributed in the metabolic pathways of purines, pyrimidines, porphyrins, and phenylalanine. Other important metabolic pathways include glycolysis, fatty acid biosynthesis, and nitrogen metabolism. The nitrogen degradation of *Paracococcus denitrifyingans* primarily involves nitrate nitrogen, which is degraded through the denitrification metabolic pathway. This includes the reduction of nitrate nitrogen to nitrite nitrogen, then the reduction of nitrite nitrogen to nitric oxide, further reduction to nitrous oxide, and finally to nitrogen gas, completing the denitrification process. Each step of this process is accompanied by electron transfer and energy generation.

[0072] Table 4: Metabolic responses with high metabolic flux

[0073]

[0074] The flow balance analysis results were directly related to the nitrogen degradation pathway and confirmed that nitrate reductase (Nar), nitrite reductase (Nir), nitric oxide reductase (Nor), and nitrous oxide reductase (Nos) are essential key enzymes in the denitrification process. Future research could explore overexpression of these key genes.

[0075] The results also revealed that reactions with fluxes nearing their upper limits primarily involve NAD. + The study also highlighted the NADH generation pathway (nicotinic acid and nicotinamide metabolism), two crucial electron donors for nitrogen removal. High fluxes indicate these pathways are highly active under conditions requiring high nitrogen production, generating large amounts of NADH to support nitrogen reduction. Approaching the upper limit suggests that NADH production in these pathways has reached its limit and cannot meet the demands of nitrogen degradation, thus restricting the rate of nitrogen reduction and potentially acting as a bottleneck in nitrogen degradation. Furthermore, active amino acid metabolism and aminotransfer pathways (such as lysine and taurine metabolism) suggest these pathways may be essential for providing nitrogen sources and maintaining nitrogen balance; appropriate regulation of these reactions could improve the efficiency of nitrogen degradation. The results also revealed some high-flux, potentially redundant metabolic pathways, such as glycolysis, the TCA cycle, and amino acid metabolism.

[0076] Therefore, possible strategies to enhance the denitrification capacity of *Paracococcus denitrifyingus* include: 1) enhancing the expression of specific enzymes in the denitrification metabolic pathway (such as Nar and Nir) to promote nitrate reduction; 2) alleviating bottleneck reactions to optimize electron transport efficiency by overexpressing enzymes related to the electron transport chain, especially membrane-bound enzymes, such as those used for NADH oxidation, to ensure sufficient electrons to drive the activity of denitrifying enzymes. Additionally, increasing the electron donor generation flux can enhance the production of NADH and FADH2; 3) utilizing metabolic network redundancy to optimize denitrification efficiency by readjusting the flux in carbon source metabolism to direct more electron donors to denitrification-related pathways.

[0077] Example 3: Flow variability analysis of bottleneck metabolic responses affecting nitrogen metabolism

[0078] For all genes involved in the metabolic pathways identified in Example 2, the minimum and maximum fluxes of other metabolic reactions were calculated using the MATLAB platform and the flux variation analysis tool in the RAVEN toolbox, and the flux variation range for each reaction was determined. By comparing the widths of these variation ranges, reactions with smaller flux ranges due to flux fluctuations were identified as potential bottleneck reactions. The simulation results are shown below. Figure 3 As shown, the degree of variability represents the ratio of the initial flux range to the actual flux variation range. A high degree of variability indicates that the actual flux of the reaction is highly restricted, which usually contributes significantly to the synthesis of the target product. A low degree of variability (close to 1) indicates that the flux of the reaction varies widely, which is usually a redundant reaction.

[0079] The results showed changes in the range of variation for 20 metabolic reactions. These changes primarily occurred in the purine metabolic pathway, exhibiting relatively small flux variation ranges, significantly narrowed compared to the initial settings. This limited flux variation suggests that these reactions may be potential bottleneck reactions. This is likely because the purine metabolic pathway generates a large amount of ATP and electron donors, providing energy and reducing power for nitrogen reduction reactions. Therefore, based on the flux variation analysis, purine metabolism may play a major role in the energy and reducing power supply for denitrification, and subsequent metabolic engineering to enhance denitrification could focus on increasing the activity of these reactions.

[0080] Some steps in the embodiments of the present invention can be implemented using software, and the corresponding software program can be stored in a readable storage medium, such as an optical disc or a hard disk.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.< / pde>

Claims

1. A method for constructing a genome-scale metabolic network model of *Paracoccus denitrificans*, characterized in that, The method includes: Step 1: Perform whole-genome annotation based on the published genome sequencing results of *Paracoccus denitrificans*; Step 2: Obtain global metabolic response data of Paracoccus denitrifyingus, extract key information of metabolic responses, and standardize the names of metabolic responses and metabolites; Step 3: Using the RAVEN toolbox of the MATLAB platform, based on the species code and genome annotation results of Paracoccus denitrifying in the KEGG database, automatically retrieve genome information and construct Model 1; Step 4: Using the RAVEN toolbox on the MATLAB platform, based on the species code, genome annotation results and pre-trained Hidden Markov model of Paracoccus denitrification in the KEGG database, homology search is performed on the proteins of the target organism to identify homologous proteins in the genome of Paracoccus denitrification, and additional metabolic reactions are added to construct Model 2. Step 5: Integrate the two automatically constructed Model 1 and Model 2 to obtain the genome-scale metabolic network model of Paracoccus denitrification. The structure of this metabolic network model on the MATLAB platform is that the metabolic reactions and metabolites are composed of a stoichiometric matrix, where the rows of the matrix represent metabolites, the columns represent metabolic reactions, and the values ​​represent stoichiometric coefficients. The method also includes manually refining and optimizing the model, including: The format of metabolic reaction and metabolite names was unified and standardized, and the KEGG reaction names and metabolite names in the model were replaced with the corresponding reaction and metabolite names in the BiGG database to increase the model's versatility. Manually supplement the missing chemical formulas and names of metabolites, prepare a table containing the chemical formulas and names of each metabolite of Paracoccus denitrificans, use a MATLAB script to read the table and update the metabolite information in the model; set the chemical formula of the recycled complex cofactors, including tRNAs, coenzyme A and acyl carrier proteins, to R. By referencing existing metabolic models and drawing on exchange reactions, we added exchange reactions suitable for the metabolic needs of Paracoccus denitrifying to the model, allowing substances to enter and exit the environment. The transport reaction was initiated based on the identification results of the transport proteins; Delete inappropriate responses; Perform a mass balance check on all reactions; The model is designed to allow for the reversibility of each reaction, with upper and lower limits for reversible metabolic reactions defined as 1,000 to -1,000 mmol g. -1 h -1 The irreversible reaction was set to 0 to +1000 mmol g. -1 h -1 The upper and lower limits of the exchange reaction were set to -20 to 1000 mmol g. -1 h -1 Finally, a genome-scale metabolic network model of denitrifying paracocci was obtained. Step 1 uses the Prokka annotation tool; Step 1 involves downloading the complete genome file of *Paracoccus denitrificans* from the NCBI database. Step 2 uses the API provided by the KEGG database to download the KGML file; Step 3 uses the getKEGGModelForOrganism function from the RAVEN toolbox to construct model 1.

2. A method for analyzing key metabolic pathways affecting nitrogen metabolism, characterized in that, The method includes: converting the genome-scale metabolic network model of *Paragonimus denitrifyingus* constructed by the method described in claim 1 into a computer-recognizable mathematical coefficient matrix; using the flow balance analysis tool in the RAVEN toolbox of the MATLAB platform, with the maximization of nitrogen synthesis as the objective function, calculating the metabolic flux distribution of other metabolic pathways, and identifying key metabolic pathways.

3. A method for analyzing bottleneck metabolic reactions affecting nitrogen metabolism, characterized in that, The method includes: converting the genome-scale metabolic network model of *Paragonimus denitrifyingus* constructed by the method described in claim 1 into a computer-recognizable mathematical coefficient matrix; using the flow variation analysis tool in the RAVEN toolbox of the MATLAB platform to calculate the minimum and maximum fluxes of other metabolic reactions; and identifying bottleneck reactions by comparing the widths of these flux variation intervals.

4. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to implement the method as described in claim 1 when executing the computer program.

5. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the method as described in claim 1.

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