Construction and analysis method of paracoccus denitrificans genome scale metabolic network model

Abstract

The invention discloses a construction and analysis method of a paracoccus denitrificans genome scale metabolic network model, and belongs to the technical field of system biology. The method comprises the following steps: annotating a whole genome; acquiring global metabolic reaction data of paracoccus denitrificans; the method comprises the following steps: automatically retrieving genome information and constructing a model 1 on the basis of species codes and genome annotation results of paracoccus denitrificans in a KEGG database; on the basis of a pre-trained hidden Markov model, homology search is carried out on protein of a target organism, homologous protein in a paracoccus denitrificans genome is identified, additional metabolic reaction is added, and a model 2 is constructed; and integrating the model 1 and the model 2. According to the method, denitrification engineering can be efficiently designed and modified, accurate regulation and control of the nitrogen degradation process are achieved, compared with an existing metabolic engineering modification method, the workload of an exploration experiment can be effectively reduced, and the method has a great promotion effect on deepening the nitrogen degradation characteristic of paracoccus denitrificans.

Description

Technical Field

[0001] The invention relates to a construction and analysis method of a genome-scale metabolic network model of Paracoccus denitrificans, and belongs to the technical field of systems biology. Background Art

[0003] Paracoccus denitrificans is a Gram-negative bacterium that is widely found in soil and sludge. It can use nitrate to denitrify under anaerobic or aerobic conditions to generate gaseous nitrogen. Because of its good nitrogen degradation and environmentally friendly characteristics, it can be used as a potential biological denitrification strain for bioremediation and sewage treatment. However, naturally occurring Paracoccus denitrificans usually grows slowly, and the nitrogen degradation rate is affected by many factors, which cannot meet the needs of industrial denitrification. With the development of biotechnology, researchers have focused on traditional metabolic engineering to improve the denitrification capacity of Paracoccus denitrificans, using genetic engineering methods to locally knock out or overexpress genes in the nitrogen degradation pathway. However, local-level transformation has a certain degree of blindness, and it is impossible to achieve precise regulation and efficient nitrogen degradation, and its transformation results are still difficult to meet actual needs. Therefore, it is necessary to have a systematic and comprehensive understanding of Paracoccus denitrificans, and on this basis, to efficiently design and transform the denitrification function. Summary of the invention

[0004] In order to improve the efficiency of denitrification engineering design and transformation and realize the precise regulation of the nitrogen degradation process of Paracoccus denitrificans, the present invention provides a method for constructing and analyzing a genome-scale metabolic network model of Paracoccus denitrificans. The technical scheme is as follows:

[0005] The method for constructing the genome-scale metabolic network model of Paracoccus denitrificans of the present invention comprises:

[0006] Step 1: Annotate the whole genome based on the published genome sequencing results of Paracoccus denitrificans;

[0007] Step 2: Obtain the global metabolic reaction data of Paracoccus denitrificans, extract the key information of metabolic reactions, and standardize the metabolic reactions and metabolite names;

[0008] Step 3: Using the RAVEN toolbox on the MATLAB platform, based on the species code and genome annotation results of Paracoccus denitrificans in the KEGG database, the genome information was automatically retrieved and model 1 was constructed;

[0009] 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 Denitrifying Paracoccus in the KEGG database, homology searches were performed on the proteins of the target organism to identify homologous proteins in the Denitrifying Paracoccus genome, and additional metabolic reactions were added to construct Model 2.

[0010] Step 5: Integrate the two automatically constructed models 1 and 2 to obtain the genome-scale metabolic network model of the denitrifying Paracoccus.

[0011] Optionally, the method further comprises manually refining and optimizing the model, including: adding exchange reactions with reference to existing metabolic models, adding transport reactions according to transporter identification results, deleting duplicate reactions, mass balance checks and reversibility settings.

[0012] Optionally, the step 1 uses the Prokka annotation tool.

[0013] Optionally, in step 1, a complete genome file of Paracoccus denitrificans is downloaded from the NCBI database.

[0014] Optionally, step 2 uses an API provided by the KEGG database to download the KGML file.

[0015] Optionally, step 3 uses the getKEGGModelForOrganism function of the RAVEN toolbox to construct model 1.

[0016] The present invention provides a method for analyzing key metabolic pathways that affect nitrogen metabolism, comprising: converting a genome-scale metabolic network model of Paracoccus denitrifyingis constructed by the method described in any one of the above items into a mathematical coefficient matrix that can be recognized by a computer, using a flow balance analysis tool in a RAVEN toolbox on a MATLAB platform, taking maximization of nitrogen synthesis as an objective function, calculating the metabolic flux distribution of other metabolic pathways, and identifying key metabolic pathways.

[0017] The present invention provides a method for analyzing bottleneck metabolic reactions that affect nitrogen metabolism, comprising: converting a genome-scale metabolic network model of Paracoccus denitrifyingis constructed by the method described in any one of the above items into a mathematical coefficient matrix that can be recognized by a computer, using a flux variation analysis tool in a RAVEN toolbox on a MATLAB platform to calculate minimum and maximum fluxes of other metabolic reactions, and identifying bottleneck reactions by comparing the widths of these flux variation intervals.

[0018] The present invention provides an electronic device, comprising a memory and a processor;

[0019] The memory is used to store computer programs;

[0020] The processor is configured to implement any of the methods described above when executing the computer program.

[0021] The present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any one of the above items is implemented.

[0022] The beneficial effects of the present invention are:

[0023] The present invention is based on the big data of the genome of Paracoccus denitrificans, starts from the annotation of the genome sequence, combines various reactions and enzymology databases, obtains the relationship between gene-protein-reaction, and then constructs a genome-scale metabolic network of Paracoccus denitrificans. Based on the constructed genome-scale metabolic network model of Paracoccus denitrificans, a systematic, whole-cell level metabolic simulation of Paracoccus denitrificans is performed to identify potential metabolic reaction pathways for enhanced denitrification, provide a reference basis for experimental design, and combine with analysis methods such as flow balance and flow variation to efficiently design and transform denitrification projects and achieve precise regulation of the nitrogen degradation process. Compared with the existing metabolic engineering transformation methods, the present invention can effectively reduce the workload of exploratory experiments and has a great promoting effect on in-depth understanding of the nitrogen degradation characteristics of Paracoccus denitrificans. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 The present invention is a flow chart of the method for constructing a genome-scale metabolic network model of Paracoccus denitrificans.

[0026] Figure 2 Mapping of target metabolic pathways for enhanced nitrogen degradation for flux balance analysis.

[0027] Figure 3 Schematic diagram of the degree of variation in bottleneck metabolic responses to improve nitrogen degradation for flow variation analysis. DETAILED DESCRIPTION

[0028] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0029] The implementation process of the present invention mainly includes the following contents:

[0030] (1) Gene function annotation of Paracoccus denitrificans.

[0031] In order to perform functional annotation on the genome of Paracoccus denitrificans (pde), this example uses the Prokka annotation tool for analysis.

[0032] First, from the NCBI database ( https: / / www.ncbi.nlm.nih.gov / datasets / genome / ) Download the complete genome file of Paracoccus denitrificans GCA_000203895.1, which is a FASTA format file. Make sure the file name and content meet the input requirements of Prokka. The file name is pde_genome.fasta. At the same time, install Prokka on the Linux system. The Prokka software package comes from GitHub. The download link is https: / / github.com / tseemann / prokka.git After successful installation, enter the command in the terminal:

[0033] prokka --outdir annotation_output --prefix DenitrifyingParacoccus\pde_genome.fasta

[0034] The tool will automatically predict and annotate protein-coding genes, rRNA, tRNA and other functional genes, and assign functional labels to genes based on sequence similarity and conserved region characteristics in combination with database information. Prokka will generate annotation results in multiple file formats, including .gff (general feature format file), .gbk (GenBank format file), .tsv (label 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.

[0035] (2) Collection of global metabolic reaction data of Paracoccus denitrificans.

[0036] In order to collect the global metabolic reaction data of Paracoccus denitrificans, the API provided by the KEGG database was used to download KGML (files in KEGG markup format). KGML files contain structured information about metabolic pathways and serve as a reference data source for subsequent metabolic network construction. Use Python to write an API request script and call KEGG's REST API to batch obtain KEGML files. The API request format is http: / / rest.kegg.jp / get / <pde> / kgml.

[0037] The KGML file was parsed and the key information of metabolic reactions, including reactants, products, enzymes, genes and corresponding KEGG IDs, was extracted using Python's xml.etree.ElementTree script. The extracted metabolic reaction data was saved as a csv file and retrieved from the MetaCyc and BioCyc databases to supplement the information of the metabolic reaction pathway.

[0038] (3) Use RAVEN Toolbox to semi-automatically construct a preliminary metabolic network model.

[0039] The RAVEN toolbox was used on the MATLAB platform to construct a preliminary metabolic model of Paracoccus denitrificans. First, a preliminary model was constructed based on the KEGG database. The getKEGGModelForOrganism function of RAVEN (command, model = getKEGGModelForOrganism ('pde')) was used to automatically retrieve genome information from the KEGG database based on the species code pde of Paracoccus denitrificans and construct a preliminary metabolic model. This step will generate a model containing preliminary metabolic reactions and gene association information.

[0040] After that, a hidden Markov model (HMM) was used to identify the supplementary enzyme information, and the protein sequence file of Paracoccus denitrificans (pd1222.faa, download link) was used by the HMM method. https: / / www.uniprot.org ) and the pre-trained HMM model library (prok90_kegg94), run the python script model2 = getKEGGModelForOrganism ('pde', 'Pd1222.faa', 'prok90_kegg94'), prok90_kegg94 contains HMM models for various bacterial enzymes and functions. Based on this, HMM will evaluate the similarity between the protein sequence of Paracoccus denitrificans and the sequence in the model, determine whether there are similar proteins in the HMM model, and supplement enzymes without annotated functions. Finally, a preliminary metabolic model containing enzymes and reactions will be constructed based on the KEGG database and the HMM model. The structure of this metabolic network on the MATLAB platform is that the metabolic reactions and metabolites are composed of a stoichiometric matrix (S matrix), the rows of the matrix represent metabolites, the columns represent metabolic reactions, and the values ​​represent stoichiometric coefficients.

[0041] (4) Flow balance analysis.

[0042] Flux 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 is referred to as metabolic flux. Since nitrogen is the final product of nitrogen degradation by Paracoccus denitrificans, maximizing nitrogen synthesis is used as the objective function.

[0043] Load the model on the MATLAB platform to set the objective function, use the optimizeCbModel function of the RAVEN toolbox to perform flow balance analysis, and determine the metabolic flux distribution of other metabolic pathways. This calculation process assumes that the cell is in a steady state, that is, the generation rate and consumption rate of all metabolites in the cell are balanced. Reaction pathways with high metabolic flux may be pathways that contribute highly to the target product, and vice versa, they may not be the key metabolic pathways to generate the target product.

[0044] (5) Traffic variation analysis.

[0045] The flux variation analysis method was used to study the flux variation range of each reaction under the condition that nitrogen synthesis reached the optimal value. Flow variation analysis is used to analyze the variation range of reaction flux in the metabolic network, identify the flexibility or bottleneck of the metabolic pathway, set the nitrogen synthesis maximization as the objective function, and further analyze the flux variation range of each reaction. For each reaction flux, the maximum and minimum values ​​of the reaction flux are optimized respectively, so that the objective function remains in a certain range of the maximum value, usually 90% to 100%. By optimizing these two values, the variation range of other metabolic reactions is solved. This flux variation range can be calculated by "maximum value minus minimum value". The larger the variation range, the higher the flexibility, the larger the flux variation range, the less restrictive the reaction is on the production of the target product, and it is usually not a key metabolic reaction; on the contrary, the smaller the flux variation range, the more important it is for the synthesis of the target product, and it can be regarded as a key metabolic reaction. The constraint on the objective function is high, and it is suitable as an optimization target for metabolic engineering.

[0046] Example 1: Method for constructing a genome-scale metabolic network model of Paracoccus denitrificans

[0047] This embodiment includes the following steps:

[0048] Step 1: Download the complete genome sequence of Paracoccus denitrificans GCA_000203895.1 from the NCBI database, use the gene annotation tool Prokka to perform functional annotation on the genome, and obtain the annotation results (faa format).

[0049] Step 2: Metabolic reaction data collection: collect metabolic reaction pathways and related information from databases (such as KEGG, MetaCyc, and BioCyc).

[0050] Standardized metabolic reaction and metabolite name information was collected, the BiGG database was used to find a list of all reactions and metabolites, and Python was used to automatically create a BiGG reaction and metabolite mapping table corresponding to the KEGG reaction.

[0051] Step 3: Construction of the preliminary model, including the following steps:

[0052] Step 31: Use the getKEGGModelForOrganism function of the RAVEN toolbox on the MATLAB platform to construct model 1 based on the species code (pde) and genome annotation results of Paracoccus denitrificans in the KEGG database;

[0053] Step 32: Use the getKEGGModelForOrganism function of the RAVEN toolbox on the MATLAB platform, based on the species code (pde) of Paracoccus denitrificans in the KEGG database, the genome annotation results and the pre-trained hidden Markov model HMM model (prok90_kegg94), identify homologous proteins in the genome of Paracoccus denitrificans by performing homology searches on the proteins of the target organism, add additional metabolic reactions, and construct model 2;

[0054] Step 33: Integrate the two automatically constructed models 1 and 2 to obtain a preliminary model of Paracoccus denitrifyingus.

[0055] Step 4: Manually refine and optimize the preliminary model, including:

[0056] Step 41: Unify and standardize the formats of metabolic reaction and metabolite names, replace the KEGG reaction names and metabolite names in the model with the reaction and metabolite names corresponding to the BiGG database, and increase the universality of the model;

[0057] Step 42: Manually fill in the missing chemical formula and name information of the metabolites, prepare a table containing the chemical formula and name of each metabolite of Paracoccus denitrificans, use MATLAB script to read the file, and update the metabolite information in the model. Set the chemical formula of recycled complex cofactors, such as tRNAs, coenzyme A, and acyl carrier proteins, to "R";

[0058] Step 43: Referring to the existing metabolic model (E. coli model iML1515), 155 exchange reactions suitable for the metabolic needs of Paracoccus denitrificans were added to the model to allow substances to enter and exit the environment;

[0059] Step 44: Add 156 transport reactions based on the transporter identification results;

[0060] Step 45: Delete 119 inappropriate responses;

[0061] Step 46: Perform mass balance checks on all reactions;

[0062] Step 47: Set the reversibility of each reaction in the model, and specify that the upper and lower limits of reversible metabolic reactions are 1,000 to -1,000 mmol g -1 h -1 , irreversible reaction is set to 0~+1000mmol 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 Paracoccus denitrificans was obtained, which included 2236 genes, 2365 metabolites and 2502 biochemical reactions.

[0063] The metabolites and metabolic reaction information included in the model are shown in Tables 1 and 2. The model construction process is as follows Figure 1 The database and software used are shown in Table 3.

[0064] Table 1: Metabolic reaction information included in the model

[0065]

[0066]

[0067] Table 2: Metabolite information included in the model

[0068] abbreviation name Molecular formula Subcellular localization h2o_c H2O H2O Cytoplasm atp_c ATP C10H16N5O13P3 Cytoplasm nad_c NAD+ R Cytoplasm nadh_c NADH R Cytoplasm nadphx__S_c NADPH R Cytoplasm nadp_c NADP+ R Cytoplasm o2_c Oxygen O2 Cytoplasm adp_c ADP C10H15N5O10P2 Cytoplasm pi_c Orthophosphate H3PO4 Cytoplasm coa_c CoA R Cytoplasm co2_c CO2 CO2 Cytoplasm

[0069] Table 3: Databases and software used in model construction

[0070] Name and Category Website KEGG https: / / www.genome.jp / kegg / MetaCyc https: / / metacyc.org / BioCyc https: / / www.biocyc.org / ChB https: / / www.ebi.ac.uk / chebi / aboutChebiForward.do Uniprot https: / / www.uniprot.org / BiGG http: / / bigg.ucsd.edu / NCBI https: / / www.ncbi.nlm.nih.gov / software Prokka https: / / github.com / tseemann / prokka MATLAB https: / / www.mathworks.com / products / matlab.html Adobe Illustrator https: / / www.adobe.com / products / illustrator / free-trial-download.html RAVEN Toolbox https: / / github.com / SysBioChalmers / RAVEN

[0071] Example 2: Flow balance analysis of key metabolic pathways affecting nitrogen metabolism

[0072] The genome-scale metabolic network model of Paracoccus denitrificans constructed in Example 1 was converted into a mathematical coefficient matrix that can be recognized by a computer. The MATLAB software platform was used with the help of the flow balance analysis tool in the RAVEN toolbox, and the nitrogen synthesis reaction was taken as the target reaction to determine the effects of other metabolic pathways on nitrogen degradation. The simulation results are shown in Figure 2. Figure 2 shown.

[0073] The results showed that there were 178 metabolic reactions with high metabolic flux (Table 4 and Figure 1 ), mainly distributed in the metabolic pathways of purine, pyrimidine, porphyrin, and phenylalanine. Other important metabolic pathways include glycolysis, fatty acid biosynthesis, and nitrogen metabolism. The nitrogen degradation of Paracoccus denitrificans is mainly nitrate nitrogen, which is degraded through the denitrification metabolic pathway, including the reduction of nitrate nitrogen to nitrite nitrogen, followed by the reduction of nitrite nitrogen to nitric oxide, further reduced to nitrous oxide, and finally to nitrogen gas, completing the denitrification process. In the whole process, each step is accompanied by electron transfer and energy generation.

[0074] Table 4: Metabolic reactions with high metabolic flux

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] The results of the flux balance analysis are directly related to the nitrogen degradation pathway, and also confirmed that nitrate reductase (Nar), nitrite reductase (Nir), nitric oxide reductase (Nor) and nitrous oxide reductase (Nos) are key enzymes required for the denitrification process. Subsequent studies can overexpress these key genes.

[0088] In addition, the results also revealed that the reactions with flux close to the upper limit mainly involved NAD + and NADH production pathways (nicotinic acid and nicotinamide metabolic pathways), and these two electron donors are critical to the denitrification process. The high flux indicates that these pathways are very active under the premise of high nitrogen production, and a large amount of NADH is generated to support the reduction reaction of nitrogen. Approaching the upper limit means that the NADH production of this pathway has reached its limit and cannot meet the demand for nitrogen degradation, which limits the rate of nitrogen reduction and may be the bottleneck pathway for nitrogen degradation. In addition, active amino acid metabolism and amino transfer pathways (such as lysine and taurine metabolism) suggest that these pathways may be crucial in providing nitrogen sources and maintaining nitrogen balance. Appropriate regulation of these reactions can improve the efficiency of nitrogen degradation. The results also show that there are some metabolic pathways with high flux that may be redundant, such as glycolysis, TCA cycle and amino acid metabolism.

[0089] Therefore, possible strategies to enhance the denitrification capacity of Paracoccus denitrificans are: 1) Enhance the expression of specific enzymes (such as Nar, Nir) in the denitrification metabolic pathway to promote nitrate reduction; 2) Alleviate the limitations of bottleneck reactions to optimize electron transfer efficiency, overexpress enzymes related to the electron transport chain, especially enzyme systems on the membrane, such as enzymes for NADH oxidation, to ensure that sufficient electrons are used to drive the activity of denitrification enzymes. In addition, increase the generation flux of electron donors and strengthen the generation of NADH and FADH2. 3) Utilize metabolic network redundancy to optimize denitrification efficiency, readjust the flux in carbon source metabolism, and direct more electron donors to denitrification-related pathways.

[0090] Example 3: Analysis of flow variation affecting bottleneck metabolic reactions of nitrogen metabolism

[0091] For all genes involved in the metabolic pathways determined 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 interval of each reaction was obtained. By comparing the widths of these variation intervals, those reactions with smaller flux intervals due to limited flux fluctuations can be identified as potential bottleneck reactions. The simulation results are shown in Figure 1. Figure 3 As shown, the degree of variation represents the ratio of the initial flux interval size to the actual flux change interval size. A high degree of variation means that the actual flux of the reaction is highly restricted, which usually contributes more to the synthesis of the target product. A low degree of variation (close to 1) means that the flux variation range of the reaction is wider, which is usually some redundant reactions.

[0092] The results showed that there were changes in the variation range of 20 metabolic reactions. This mainly occurred in the purine metabolic pathway, which showed a smaller flux variation range, which was significantly reduced compared to the initial setting, and the flux variation space was limited, indicating that these reactions may be potential bottleneck reactions. This may be because the purine metabolic pathway produces a large amount of ATP and electron donors, which provide energy and reducing power for nitrogen reduction reactions. Therefore, based on the results of flux variation analysis, purine metabolism may make a major contribution to the energy and reducing power supply of denitrification, and subsequent metabolic engineering to enhance denitrification can focus on enhancing the activity of these reactions.

[0093] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0094] 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 principle of the present invention should be included in 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 comprises: Step 1: Annotate the whole genome based on the published genome sequencing results of Paracoccus denitrificans; Step 2: Obtain the global metabolic reaction data of Paracoccus denitrificans, extract the key information of metabolic reactions, and standardize the metabolic reactions and metabolite names; Step 3: Using the RAVEN toolbox on the MATLAB platform, based on the species code and genome annotation results of Paracoccus denitrificans in the KEGG database, the genome information was automatically retrieved and model 1 was constructed; 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 Denitrifying Paracoccus in the KEGG database, homology searches were performed on the proteins of the target organism to identify homologous proteins in the Denitrifying Paracoccus genome, and additional metabolic reactions were added to construct Model 2. Step 5: Integrate the two automatically constructed models 1 and 2 to obtain the genome-scale metabolic network model of the denitrifying Paracoccus.

2. The method according to claim 1, characterized in that The method also includes manual refinement and optimization of the model, including: adding exchange reactions with reference to existing metabolic models, adding transport reactions based on transporter identification results, deleting duplicate reactions, mass balance checks and reversibility settings.

3. The method according to claim 1, characterized in that The step 1 uses the Prokka annotation tool.

4. The method according to claim 1, characterized in that: The step 1 downloads the complete genome file of Paracoccus denitrificans from the NCBI database.

5. The method according to claim 1, characterized in that The step 2 uses the API provided by the KEGG database to download the KGML file.

6. The method according to claim 1, characterized in that The step 3 uses the getKEGGModelForOrganism function of the RAVEN toolbox to construct model 1.

7. A method for analyzing key metabolic pathways affecting nitrogen metabolism, characterized in that: The method comprises: converting the genome-scale metabolic network model of Paracoccus denitrificans constructed by the method according to any one of claims 1 to 6 into a computer-recognizable mathematical coefficient matrix, using the flow balance analysis tool in the RAVEN toolbox of the MATLAB platform, taking maximizing nitrogen synthesis as the objective function, calculating the metabolic flux distribution of other metabolic pathways, and identifying key metabolic pathways.

8. A method for analyzing bottleneck metabolic reactions affecting nitrogen metabolism, characterized in that: The method comprises: converting the genome-scale metabolic network model of Paracoccus denitrificans constructed by the method according to any one of claims 1 to 6 into a computer-recognizable mathematical coefficient matrix, using the flux variation analysis tool in the RAVEN toolbox of the MATLAB platform to calculate the minimum and maximum fluxes of other metabolic reactions, and identifying the bottleneck reaction by comparing the widths of these flux variation intervals.

9. 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 according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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