Static simulation method for metabolic process of pollutants coupled with fish intestinal microorganisms and digestive enzymes

Abstract

The invention relates to the technical field of metabolism simulation, in particular to a static simulation method for the metabolic process of pollutants coupling fish enteric microorganisms and digestive enzymes, which comprises the following steps: acquiring a fish enteric microorganism enrichment culture and an enteric enzyme extract, and analyzing the abundance of a fish enteric microorganism community and the variety of digestive enzymes; a static model composed of a gastric phase compartment, a midgut compartment and a hindgut compartment is constructed, and key incubation parameters such as culture medium volume, pH value, temperature, digestive enzyme activity and bile salt concentration of each compartment are determined. In the invention, the preparation and pre-stabilization method of the intestinal microbial liquid is particularly optimized, the authenticity of microbial metabolic activity is ensured, and the pairing relationship between pollution conversion and different digestion stages of fish is established by constructing a pollutant static metabolic conversion method of microbial community and digestive enzyme. The evaluation accuracy of the first-pass metabolic reaction of the pollutants is improved, and structural information of microorganisms, enzymes, the pollutants and substrates is integrated.

Description

Technical Field

[0001] This invention relates to the field of metabolic simulation technology, and in particular to a static simulation method for pollutant metabolism processes coupled with fish gut microbiota and digestive enzymes. Background Technology

[0002] The field of metabolic simulation technology involves the modeling, simulation, and predictive analysis of material transformation processes within organisms. Core aspects include the metabolic pathways of pollutants in organisms, enzymatic reaction processes, microbial metabolic behavior, and their metabolic kinetics under changing environmental conditions. This field typically employs methods such as bioreaction kinetic modeling, omics data-based model construction, and multi-factor static parameter settings to quantitatively model and mathematically simulate the metabolic processes of target organisms or systems, achieving a structural understanding and process prediction of pollutant transformation behavior within the body. Among these methods, the traditional static simulation method of pollutant metabolism coupled with fish gut microbiota and digestive enzymes involves establishing a data matrix of microbial population structure and its metabolites, combined with enzyme activity parameters of key digestive enzymes in the fish gut. Based on the degradation capacity and enzymatic reaction rate of specific pollutants by the microbial population, and using static factor settings and metabolic flux analysis under steady-state conditions, it structurally models and parametrically simulates the decomposition, transformation, and re-release processes of pollutants in the gut. This method typically involves compiling species composition data of the microecological community, determining the reaction rate constants of pollutants with single or mixed enzyme systems, and setting boundary conditions such as fish feeding and intestinal flux.

[0003] Existing technologies, by statically setting microbial community structures and key digestive enzyme activity parameters, lack the construction of pairing relationships based on the actual coexistence structure within the micro-ecosystem. This leads to uncertainty in identifying the initial metabolic reaction units of pollutants and easily causes biases in the construction of metabolic pathway initiation points. During reaction chain construction, the structural compatibility between pollutant metabolites and downstream enzyme parameters in continuous reactions is not fully considered, making the determination of pathway continuity dependent on manual settings or empirical values, limiting the accuracy of judging the true continuity of reactions. Regarding flux parameter expression, single enzymatic reaction rates are typically used as pathway-driving indicators, lacking a reactive expression of the coupling relationship between substrate consumption intensity and maximum reaction rate, which is not conducive to reflecting flux hierarchy differences in complex pathways. In the final simulation structure construction, enzymes or microorganisms are often used only as unit carriers of metabolic processes, without integrating pollutant categories and metabolite structures. This results in deficiencies in the simulation results' biological specificity and pathway visualization capabilities, making it difficult to support complete structural analysis and dynamic evolution prediction of pollutant decomposition, transformation, and fate trends. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a static simulation method for pollutant metabolism processes coupled with fish gut microbiota and digestive enzymes, comprising the following steps: To achieve the above objectives, the present invention employs the following technical solution: a static simulation method for pollutant metabolism processes coupled with fish gut microbiota and digestive enzymes, comprising the following steps: S1: Extract dominant microorganisms with metabolic functions and corresponding digestive enzymes from fish intestinal segments, pair them based on coexistence relationships, obtain substrate consumption rates and Michaelis constants, and generate microbial enzyme reaction pairing information; S2: Based on the microbial enzyme reaction pairing information, obtain the initial concentration of pollutants, calculate the initial intensity of the path by combining the substrate initial reaction rate, extract the structure of metabolites, determine whether they are enzyme substrates, screen fragments that can enter the next reaction node, and generate a pollutant transformation node set. S3: Call the pollutant transformation node set, obtain the product LogP value and the downstream enzyme Km value, screen for fragments that can form continuous reactions, and generate enzyme-microbe co-pathway sequences in combination with microbial abundance response conditions. S4: Based on the enzyme-microbe co-pathway sequence, extract the substrate consumption rate and maximum reaction rate in the path, calculate the flux input parameters, and construct the path flux input hierarchy set; S5: Based on the path flux input hierarchy set, extract the microbial genus, enzyme EC number, pollutant type and product SMILES expression in the path, arrange them in the path order to construct the path framework, and integrate them to generate a static mapping structure of pollutant metabolism.

[0005] As a further aspect of the present invention, the microbial enzyme reaction pairing information includes microbial community abundance, target digestive enzyme species, substrate consumption rate, and Michaelis constant; the pollutant transformation node set includes the primary distribution concentration of pollutants in the intestinal segment, the substrate reaction initiation rate of microorganisms, metabolite structure, and node fragments; the enzyme-microbial co-pathway sequence includes product structure, LogP value, downstream enzyme Michaelis constant, node fragments, and response conditions of microbial community abundance; the path flux input hierarchy set includes substrate consumption rate, maximum reaction rate, path flux input parameters, and path classification; and the pollutant metabolism static mapping structure includes microbial genus-level classification, enzyme EC number, pollutant category, SMILES expression of metabolites, and path order.

[0006] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Obtain the abundance of metabolic microbial communities in fish intestinal segments and the types of digestive enzymes in the corresponding segments. Based on the coexistence relationship between microorganisms and enzymes, organize the combination information of microbial species and enzyme names, and establish a list of microbial enzyme combinations. S102: Based on the correspondence between microorganisms and enzymes in the combination items of the microbial enzyme combination list, extract the substrate change data of each combination in its respective segment, calculate the substrate consumption rate value, and generate a substrate consumption rate record table. S103: Call the data in the substrate consumption rate record table, combine the corresponding substrate concentration change information and reaction rate parameters, calculate the Michaelis constant value of each pair of combinations, summarize the associated parameters, and generate microbial enzyme reaction pairing information.

[0007] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the microbial enzyme reaction pairing information, obtain the primary distribution concentration of pollutants in the intestinal segment of each pairing combination, call the substrate reaction initiation rate of the corresponding microorganism, perform parameter calculations based on the numerical relationship between the primary concentration and initiation rate of the combined pollutants, record the intensity performance of the initial stage of the path, and generate the path initiation intensity parameter value. S202: Call the pairing combination corresponding to the initial strength parameter value of the path, extract the structural information of the metabolites generated in the combination, detect the degree of matching between the metabolite structure and the original enzyme structure action site, and mark the identifiable structure type according to whether there is an action response result, and obtain the enzyme recognition structure label quantity. S203: Based on the amount of enzyme recognition structure markers, screen metabolite fragments that meet the enzyme action conditions, determine whether they have the continuity characteristic of entering the next level reaction, record all node fragments with the ability to continue the reaction and their corresponding path source information, and establish a pollutant transformation node set.

[0008] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Call the pollutant transformation node set, extract the product structure information in the node fragments, obtain the LogP value corresponding to each structure, record the correspondence between the LogP value and the node fragment source information, and generate the structure polarity distribution value. S302: Based on the structural polarity distribution value, obtain the Michaelis constant of the corresponding downstream enzyme, call the two types of parameters to perform numerical difference calculation, determine whether each node fragment meets the concentration required to form a continuous reaction, use the difference condition to screen fragments that meet the condition, and obtain the number of continuous reaction screening fragments. S303: Based on the number of fragments screened by the continuous reaction, merge available node fragments with the same path source, detect whether the merged sequence corresponds to the response condition of microbial community abundance, record the path sequence that meets the response condition and combine it with the corresponding enzyme to establish an enzyme-microbe synergistic path sequence.

[0009] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the enzyme-microbe co-pathway sequence, extract the substrate consumption rate and maximum reaction rate corresponding to each path segment, calculate the product of the substrate consumption rate and the maximum reaction rate, record the corresponding values ​​according to the path segment, establish a path segment rate product dataset, and obtain the flux input parameter value. S402: Based on the flux input parameter value, map the rate product value corresponding to the path segment to the original path sequence structure, divide the input intensity level of the difference by comparing the numerical differences between the path segments, arrange the path segments in order of flux input intensity from large to small, and obtain the flux hierarchy arrangement sequence. S403: Based on the flux hierarchy arrangement sequence, integrate the original path segment numbers and flux input parameter values ​​of the hierarchical path segments, establish a hierarchical relationship structure table, record the hierarchical number and sorting position of the path segment, and establish a path flux input hierarchy set.

[0010] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the path flux input hierarchy set, call the SMILES expressions of the microbial genus classification, enzyme EC number, pollutant category and metabolite recorded in each path, combine the elements in order according to the arrangement order of the path segments in the hierarchy set, and construct an arrangement structure including the above four information for each path to generate flux path construction sequence value. S502: Construct sequence values ​​based on the throughput path, extract all element information in each path, connect them in the order of path segments to form structural segments, calculate the information redundancy value between adjacent elements in each structural segment, filter path segments with redundancy values ​​greater than the redundancy judgment threshold, and obtain the throughput path purification structural value set. S503: Call the structural content and path number of the path segment in the flux path purification structure numerical set, perform a merging operation in the entire path hierarchy, and connect the purification structure segments of all paths according to the path number to construct a static structure connection relationship table and establish a static mapping structure for pollutant metabolism.

[0011] As a further aspect of the present invention, the fish intestinal segment refers to the different parts of the fish digestive system or the intestines of different species of fish. The dominant microorganisms in metabolic activity refer to the microbial community that has a high abundance in the intestinal segment of the target fish and participates in metabolic reactions. The digestive enzymes mentioned refer to enzymes that participate in digestive reactions in the intestines of fish, including amylase, lipase, and protease, and digestive enzymes that are associated with microbial metabolism. The coexistence relationship refers to the interaction or synergistic effect between microorganisms and digestive enzymes in the fish gut. The substrate consumption rate and Michaelis constant refer to the substrate consumption rate and the corresponding Michaelis constant in the microbial-digestive enzyme catalytic reaction.

[0012] As a further aspect of the present invention, the initial concentration of pollutants refers to the initial distribution concentration of pollutants in different segments of the fish intestine, and the pollutants may include environmental pollutants and food residues; The structure of the metabolites refers to the molecular structure of the metabolites produced by the catalytic reaction between microorganisms and digestive enzymes, including the molecular formula and stereostructure. The node segment refers to the intermediate product or transformation node in the target reaction step during the pollutant transformation process; The LogP value refers to the partition coefficient of the compound in the aqueous phase and the lipid phase, reflecting the hydrophilicity or hydrophobicity of the compound. The enzyme-microbe synergistic pathway sequence refers to the reaction sequence or reaction chain of the target metabolic pathway formed by the interaction between enzymes and microorganisms.

[0013] As a further aspect of the present invention, the flux input parameters refer to the substrate consumption rate and reaction rate input parameters of each reaction step in the path; EC refers to the Enzyme Commission Number, a standard numbering system used in enzymology to identify and classify enzymes based on the type of chemical reaction in which the enzyme reacts.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the accuracy of the initial metabolic reaction of pollutants is improved by constructing a pairing relationship between microbial communities and digestive enzymes. Furthermore, the formation and continuity of metabolic pathways are optimized by using substrate reaction rate and product structure information, thereby strengthening the screening of pathway continuity and the sustainability of reaction chains. The accuracy of flux expression between different pathways is also improved. By integrating the structural information of microorganisms, enzymes, pollutants, and products, the dynamic prediction and overall assessment capabilities of pollutant metabolic processes are enhanced. Attached Figure Description

[0015] 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.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] Please see Figure 1This invention provides a static simulation method for pollutant metabolism processes coupled with fish gut microbiota and digestive enzymes, comprising the following steps: S1: Obtain the abundance of dominant microbial communities with metabolic functions and the types of target digestive enzymes in the contents of fish intestinal segments. Based on the coexistence relationship in the same segment, match the two. Extract the substrate consumption rate and Michaelis constant for each pair of combinations, and arrange the parameters to form a corresponding data table that can reflect the pairing relationship between microorganisms and digestive enzymes, and generate microbial enzyme reaction pairing information. Fish intestinal segments refer to the different parts of the fish digestive system or the different species of fish intestines, including the stomach, upper intestine, and lower intestine; The dominant microbial community in metabolic function refers to the microbial community that has a high abundance in the intestinal segment of the target fish and participates in metabolic reactions. Target digestive enzymes refer to enzymes that participate in digestive reactions in the intestines of fish, including amylase, lipase, and protease, and digestive enzymes that are associated with microbial metabolism. Coexistence relationship refers to the interaction or synergistic effect between microorganisms and digestive enzymes in the intestines of fish; Substrate consumption rate and Michaelis constant refer to the substrate consumption rate and the corresponding Michaelis constant in microbial and digestive enzyme-catalyzed reactions. S2: Based on the microbial enzyme reaction pairing information, obtain the primary distribution concentration of pollutants in the intestinal segment of the paired combination, and calculate the concentration with the substrate reaction initiation rate of the microorganism to form the path initiation strength parameter. At the same time, extract the structure of the metabolites generated by the paired combination, perform matching judgment based on whether the structure can be acted on by enzymes, and record the node fragments that can continue to the next level of reaction to generate a pollutant transformation node set. The primary distribution concentration of pollutants in the intestinal segment refers to the initial distribution concentration of pollutants in different segments of the fish's intestine. Pollutants may include environmental pollutants and food residues. Metabolic product structure refers to the molecular structure of metabolites produced by the reaction catalyzed by microorganisms and digestive enzymes, including molecular formula and stereostructure. A node segment refers to an intermediate product or transformation node in the target reaction step during the pollutant transformation process; S3: Call the pollutant transformation node set, extract the product structure from the node fragments and obtain the LogP value, and at the same time obtain the Michaelis constant of the downstream enzyme. Determine whether a continuous reaction can be formed based on the degree of difference between the two. Merge the node fragments that can be further utilized by the downstream enzyme into a sequence, and combine the sequence with the response condition of whether it corresponds to the abundance of the microbial community to generate the enzyme-microbe co-pathway sequence. LogP value refers to the partition coefficient of a compound in the aqueous phase and the lipid phase, reflecting the hydrophilicity or hydrophobicity of the compound; Enzyme-microbe co-pathway sequence refers to the reaction sequence or reaction chain of the target metabolic pathway formed by the interaction between enzymes and microorganisms; S4: Based on the enzyme-microbe co-pathway sequence, extract the substrate consumption rate and maximum reaction rate of each segment, calculate the path flux input parameters, map the obtained flux parameters to the path segments to form a flux input hierarchy structure, and complete the path classification according to the hierarchy arrangement to generate a path flux input hierarchy set. Path flux input parameters refer to the substrate consumption rate and reaction rate input parameters for each reaction step in the path; S5: Based on the path flux input hierarchy set, call the SMILES expressions of microbial genus classification, enzyme EC number, pollutant category and metabolite appearing in each path, arrange the elements in the path order to construct the flux path framework, and integrate the entire path framework into a static simulation structure system to generate a static mapping structure of pollutant metabolism. EC stands for Enzyme Commission Number, a standard numbering system used in enzymology to identify and classify enzymes based on the type of chemical reaction the enzyme is reacting. Static simulation refers to a structural system that reflects the metabolic process of pollutants, generated through model calculations or experimental data.

[0023] The microbial enzyme reaction pairing information includes microbial community abundance, target digestive enzyme species, substrate consumption rate, and Michaelis constant. The pollutant transformation node set includes the primary distribution concentration of pollutants in the intestinal segment, the substrate reaction initiation rate of microorganisms, metabolite structure, and node fragments. The enzyme-microbe co-pathway sequence includes product structure, LogP value, downstream enzyme Michaelis constant, node fragments, and response conditions of microbial community abundance. The path flux input hierarchy set includes substrate consumption rate, maximum reaction rate, path flux input parameters, and path hierarchy. The pollutant metabolism static mapping structure includes microbial genus-level classification, enzyme EC number, pollutant category, SMILES expression of metabolites, and path order.

[0024] Please see Figure 2 The specific steps of S1 are as follows: S101: Obtain the abundance of metabolic microbial communities in fish intestinal segments and the types of digestive enzymes in the corresponding segments. Based on the coexistence relationship between microorganisms and enzymes, organize the combination information of microbial species and enzyme names, and establish a list of microbial enzyme combinations. To obtain the abundance of metabolically active microorganisms and corresponding enzyme types in different intestinal segments of fish, the samples must first be systematically segmented into three sections: the anterior segment (stomach and foregut), the middle segment (midgut), and the posterior segment (hindgut). Intestinal contents are extracted from each segment, and high-throughput sequencing is used to perform 16S rRNA sequencing to identify the microbial community composition. Metagenomic sequencing is then used to analyze metabolic function, and the abundance data of each microorganism in different segments are recorded. For example, in grass carp, the abundance of Firmicutes is 22.3% in the anterior segment, 17.8% in the middle segment, and 12.5% ​​in the posterior segment. Simultaneously, colorimetric or enzyme-linked immunosorbent assay (ELISA) methods are used to determine the types and activities of major digestive enzymes in each segment. For instance, pepsin activity was detected at 3.2 U / mg in the anterior segment and amylase activity at 4.5 U / mg in the middle segment. The lipase activity in the later stage was 2.8 U / mg. Based on the obtained data, the co-occurrence relationship between microorganisms and enzymes in the same stage was analyzed. Spearman correlation analysis or SparCC network modeling was used to screen for combinations of microbial genera and enzymes that co-occurred in the same stage and whose abundance was significantly positively correlated. If Clostridium and amylase were detected to coexist in the middle stage and the correlation coefficient was 0.82 with a significance p value of less than 0.05, then the combination could be included in the list construction scope. Subsequently, the combination list was built according to the structure of "microbial genus + enzyme name", with examples including "Lactobacillus + pepsin", "Clostridium + amylase", "Bacteroides + lipase", etc., forming a list of clearly structured combination items.

[0025] S102: Based on the correspondence between microorganisms and enzymes in the microbial enzyme combination list, extract the substrate change data of each combination in its respective segment, calculate the substrate consumption rate value, and generate a substrate consumption rate record table. Based on the established list of microbial enzyme combinations, it is necessary to associate the microorganisms and enzymes involved in each combination with the substrate types of their respective intestinal segments. Experimental data or references should be collected to obtain the main substrates and their initial concentrations for each intestinal segment. For example, the initial concentration of the substrate in the foreground segment might be 18.2 mg / mL (protein substrate), the concentration in the middle segment might be 25.7 mg / mL (amylase substrate), and the concentration in the rear segment might be 10.4 mg / mL (lipid substrate). Multiple time points (e.g., 30 minutes, 60 minutes, 90 minutes) should be set to collect reaction samples. The remaining substrate concentration should be measured using colorimetry to estimate the substrate reduction per unit time. For example, the substrate reduction per unit time for a combination "Clostridium + amylase" within 60 minutes... The substrate concentration dropped to 16.3 mg / mL, meaning the substrate consumption per unit time was 9.4 mg / mL·h. This value was recorded as the substrate consumption rate. During the statistical process, the substrate type, initial concentration, reaction time, final concentration, and substrate consumption rate for each combination at the corresponding stage were uniformly organized. For example, the consumption rate of the protein substrate in the first stage for "Lactobacillus + pepsin" was 5.6 mg / mL·h, and the consumption rate of the lipid substrate in the second stage for "Bacteroides + lipase" was 4.2 mg / mL·h. The above information was uniformly recorded by combination, stage, substrate, and rate to form a complete rate record table for subsequent correlation analysis.

[0026] S103: Call the data in the substrate consumption rate record table, combine the corresponding substrate concentration change information and reaction rate parameters, calculate the Michaelis constant value of each pair of combinations, summarize the related parameters, and generate microbial enzyme reaction pairing information; Based on the substrate consumption rate recording table, and combining the substrate concentration change data and substrate consumption rate data corresponding to each combination, the Michaelis constant value of each combination is calculated. In practice, multiple concentration level data points need to be set to observe the changes in consumption rate under different substrate concentration conditions. Concentration levels such as 10 mg / mL, 15 mg / mL, and 20 mg / mL are recorded, along with the corresponding consumption rates such as 2.1 mg / mL·h, 3.2 mg / mL·h, and 4.3 mg / mL·h. The reciprocals at different points are calculated, and the corresponding concentration reciprocal versus rate reciprocal plots are drawn. The results are then analyzed using linear regression analysis. The fitting method obtains the slope and intercept, and further calculates the Km value and maximum rate value of the reaction combination. When judging the effectiveness of the fitting, the coefficient of determination R² is used as a reference. If the condition is met, the Km value of the combination is valid. For example, a combination "Bacteroides + pepsin" corresponds to the first protein substrate, and the calculated Km is 7.3 mg / mL and the maximum reaction rate is 4.6 mg / mL·h. This data is used as a reaction characteristic index and summarized in the combination parameter table to form a reaction pairing information set with "combination name, segment, substrate type, Km value, and maximum reaction rate" as the main structure.

[0027] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the microbial enzyme reaction pairing information, obtain the primary distribution concentration of pollutants in the intestinal segment of each pairing combination, call the substrate reaction initiation rate of the corresponding microorganism, perform parameter calculations based on the numerical relationship between the primary concentration and initiation rate of the combined pollutants, record the intensity performance of the initial stage of the path, and generate the path initiation intensity parameter value. Based on microbial enzyme reaction pairing information, it is necessary to first identify the types of pollutants and their primary concentration distribution in the intestinal segment of the fish where each pair is located. Pollutants include common environmental residues such as organophosphorus pesticides, heavy metal ions, or antibiotic residues. After sampling, the concentration of pollutants in each segment is quantitatively analyzed using liquid chromatography or atomic absorption spectrometry. For example, chloramphenicol was detected at a primary concentration of 5.8 mg / L in the middle segment and bisphenol A at a concentration of 3.1 mg / L in the later segment. Subsequently, based on the substrate reaction initiation rate value of the corresponding microorganism in the pairing information, the initiation rate is extracted from the previously generated substrate consumption rate record. Assuming the initiation rate of the "Lactobacillus + amylase" combination in the middle segment is 4.2 mg / L·h, a proportional relationship between pollutant concentration and initiation rate is established. This relationship is processed using a parametric expression, requiring the setting of a pollutant-enzyme reaction intensity coefficient K, where K is determined by the primary pollutant concentration. The product of the initial rate of the microbial enzyme indicates the strength of the pollutant in this combined reaction pathway. For example, if the K value is 5.8 × 4.2 = 24.36, K is divided into three intensity ranges according to empirical rules: K < 10 is low intensity, 10 ≤ K < 30 is medium intensity, and K ≥ 30 is high intensity. The current K value is calculated to be at a medium intensity level, which is recorded as the intensity performance of this combination at the beginning of the pathway. At the same time, it is necessary to consider whether this parameter exceeds the set upper limit threshold of the intensity. The threshold is set according to the maximum carrying rate of the enzyme reaction. If the maximum enzyme rate is 8.0 mg / L·h, the intensity threshold is set to 8.0 × 10 = 80. The current K value of 24.36 does not exceed the threshold and is marked as "carryable". The final value of the initial intensity parameter is formed as follows: "Lactobacillus + amylase", "Middle section", "Chloramphenicol", "K = 24.36", "Medium intensity", "Carryable".

[0028] S202: Call the pairing combination corresponding to the initial strength parameter value of the call path, extract the structural information of the metabolites generated in the combination, detect the degree of matching between the metabolite structure and the original enzyme structure action site, mark the identifiable structure type according to whether there is an action response result, and obtain the enzyme recognition structure label quantity; After the microbial enzyme pairing corresponding to the initial intensity parameter value of the calling path is completed, the structural information of the metabolites produced within the combination needs to be extracted. Structural data is obtained through mass spectrometry (e.g., LC-MS / MS) and nuclear magnetic resonance (NMR) methods after metabolite extraction. For example, the combination of "Clostridium + amylase" can produce metabolites such as glucose derivatives and short-chain fatty acids. After standardizing and transforming the product structure, it is analyzed whether there is conformational complementarity between the product and the activation site region in the original enzyme's three-dimensional structural model. Using structural alignment tools such as AutoDock or PyMOL to simulate intermolecular spatial matching, it is determined whether a certain structural site of the metabolite forms a docking relationship with the enzyme's active site or auxiliary recognition region. If a certain... If a product structure can form hydrogen bonds or hydrophobic interactions with the enzyme's active pocket, it is marked as "recognizable structure"; otherwise, it is marked as "unrecognizable." The recognition status of each structure is quantified using binary labels. When calculating the recognition count, the recognition label is set to 1, and the non-recognizable label is set to 0. The count is accumulated. For example, if a combination produces 5 metabolites, and 3 of them react with the original enzyme structure, the recognition structure label count is 3. The recognition ratio is 60% based on the total number of structures. A structure-recognition correspondence table is further established for the recognition structure count, such as "Metabolite 1: Recognized", "Metabolite 2: Unrecognized", and "Metabolite 3: Recognized", to indicate the recognition ability of metabolites within the combination. The recognition structure label count is output for subsequent node reaction screening.

[0029] S203: Based on the amount of enzyme recognition structural markers, screen metabolite fragments that meet the enzyme action conditions, determine whether they have the continuity characteristics to enter the next level reaction, record all node fragments with the ability to continue the reaction and their corresponding path source information, and establish a pollutant transformation node set. Based on the amount of enzyme recognition structural label, metabolite fragments that meet the enzyme's catalytic conditions need to be screened. First, starting with the amount of recognition label, fragments with active site responses are searched within each product. The reaction characteristics of the enzyme's active region are then used to determine whether it contains a catalytic site, such as whether it has specific hydroxyl, carboxyl, or aromatic ring groups that can be recognized as a starting point. Simultaneously, the structural basis for further recognition by other enzymes is analyzed, such as the presence of open-chain structures or specific functional group continuation bonds. A structural fragment mapping method is used to determine whether a continuous reaction pathway exists. For example, if a certain metabolic fragment, "3-hydroxybutyric acid," can enter butyric acid after enzyme recognition, further... If a transformation pathway is identified, and the hydroxyl group can be further identified in the "later segment" through the combination of "esterase + Bacteroides", then the structure is marked as having reaction continuity. At the same time, its original source pathway and structural type are recorded. All such metabolic structures with continuous capabilities are organized item by item according to combination number, pathway source, structural name, and fragment sequence, such as "Node ID=N005", "Source combination=Clostridium + amylase", "Fragment=3-hydroxybutyric acid", "Continuity=Yes". Finally, a pollutant transformation node set is formed, which is the key basic data for the subsequent construction of a pollutant full pathway transformation map.

[0030] Please see Figure 4 The specific steps of S3 are as follows: S301: Call the pollutant transformation node set, extract the product structure information in the node fragments, obtain the LogP value corresponding to each structure, record the correspondence between the LogP value and the source information of the node fragments, and generate the structure polarity distribution value. After calling the pollutant transformation node set, the chemical structure information corresponding to each node fragment is extracted one by one. The structure is in SMILES or three-dimensional coordinate format. The core framework and functional groups are extracted by the structure analysis tool. After the structure is identified, its polarity index LogP value is estimated using the chemical calculation module. LogP reflects the molecule's ability to partition between octanol and water. The larger the value, the more hydrophobic the molecule and the lower its polarity. For example, the LogP of a certain structure "CC(=O)OC1=CC=CC=C1C(=O)O" is 1.92, the LogP of another structure "C1=CC=C(C=C1)C=O" is 2.34, and the LogP of the structure "CC(C)C(=O)O" is much higher. The LogP value is 0.99. All structures must correspond one-to-one with their source paths to form a mapping relationship between node identifiers and polarity parameters. Based on this, the structure polarity distribution value is established. Each LogP value is classified according to an empirical range: LogP less than 0 is strong polarity, 0 to 2 is medium polarity, and 2 and above is hydrophobic. According to this standard, the structure is marked as medium polarity or hydrophobic, and its path number and node number are recorded to complete the summary of the entire structure polarity distribution value. For example, the structure contained in node N07 of path number T015 is medium polarity, and the structure contained in node N12 of path number T021 is hydrophobic. Finally, the set of structure polarity distribution values ​​is obtained for subsequent path construction modules to call.

[0031] S302: Based on the structural polarity distribution value, obtain the Michaelis constant of the corresponding downstream enzyme, call the two types of parameters to perform numerical difference calculation, determine whether each node fragment meets the concentration required to form a continuous reaction, use the difference condition to screen fragments that meet the condition, and obtain the number of continuous reaction screening fragments. Based on the structural polarity distribution values, the downstream enzyme information for each structure is extracted, and its corresponding Michaelis constant value is retrieved. This value describes the enzyme's affinity for the substrate and is obtained through fitting data provided by experimental databases or established models. For example, the Km for esterase is 12.5 μM, for β-glucosidase it is 8.3 μM, and for butyrate it is 25.0 μM. The current concentration information of the corresponding structural fragments is provided by the preceding reaction module. For example, the concentration of structure A is 20 μM, structure B is 6.6 μM, and structure C is 30 μM. The ratio of concentration to Km is calculated to determine whether the concentration utilization difference condition is met. When the ratio of concentration to Km is not less than 1, it indicates that the structure has the ability to be followed by the next reaction. The ability to continue the reaction is assessed. If the ratio is less than 1, the condition is not met. Based on this standard, the ratio for structure A is 1.6, for structure B it is 0.8, and for structure C it is 1.2, which are respectively labeled as capable of continuing the reaction, interrupted reaction, and capable of continuing the reaction. Among all structures, fragments with a ratio of not less than 1 are selected and accumulated to obtain the number of fragments selected for continuous reaction. For example, if 3 out of 5 fragments meet the condition, the number of selections is 3. The selection ratios are classified into levels: a ratio less than 0.5 indicates a significant risk of interruption, 0.5 to 1 is a critical state, 1 to 3 is moderate, and more than 3 is an efficient state. Based on this standard, an efficiency label is further attached to each fragment to provide a structural basis for subsequent selection.

[0032] S303: Based on the continuous reaction screening of fragment quantity, available node fragments with the same path source are merged, and it is detected whether the merged sequence corresponds to the response condition of microbial community abundance. The path sequence that meets the response condition is recorded and combined with the corresponding enzyme to establish the enzyme-microbe synergistic path sequence. Fragment quantity screening for continuous reactions is processed by path classification. Fragments from the same source are aggregated into single path sequences. For example, nodes N01, N05, and N07 all originate from path number T011. After merging, a single path sequence is generated, containing structures such as "acetic acid", "ethyl butyrate", and "3-hydroxybutyric acid". Then, the response conditions of this path combination in the microbial community are detected. The response data is obtained through community abundance sequencing. For example, the abundance data of "Clostridium" and "Bacteroides" are 1.2%, 0.8%, and 1.9%, respectively. The arithmetic mean of the abundance of each microorganism in the combination is calculated. If the average value is higher than 1%, the response requirement is met. If the average value is lower than 1%, the path combination is excluded. The average abundance of microorganisms corresponding to path T011 is 1.3%, which is higher than the threshold and is marked as a valid response. The number, composition structure, microbial combination, and abundance value of the valid path sequence are recorded to form a usable co-reaction path. This path is further combined with the corresponding enzyme to construct an enzyme-microbe co-reaction path sequence, which serves as the basic data for the complete reaction network and is used by the global path layout and optimization module.

[0033] Please see Figure 5The specific steps of S4 are as follows: S401: Based on the enzyme-microbe co-pathway sequence, extract the corresponding substrate consumption rate and maximum reaction rate in each path segment, calculate the product of the substrate consumption rate and the maximum reaction rate, record the corresponding values ​​according to the path segment, establish the rate product dataset of the path segment, and obtain the flux input parameter value. Based on the enzyme-microbe co-pathway sequence, the substrate consumption rate and maximum reaction rate corresponding to each path segment are extracted. The substrate consumption rate can be obtained through experimental determination or simulation models; for example, the slope of substrate concentration change over time is used as the rate value, in μM / min. The maximum reaction rate is the catalytic rate of the corresponding enzyme under saturated substrate conditions, which can generally be obtained by referring to literature or the Brenda database. For example, in a certain path segment, the substrate is lactate, the corresponding enzyme is lactate dehydrogenase, the substrate consumption rate is 5.2 μM / min, and the maximum reaction rate is 12.0 μM / min. The two need to be multiplied to obtain the rate product, which is 62.4 (μM / min)². The same calculation is performed for each path segment to obtain multiple rate product values. For example, path The values ​​for segments P001, P002, and P003 are 62.4, 48.9, and 83.6, respectively. All path segment numbers and their corresponding rate product values ​​are recorded in the dataset. Each record includes the path segment number, substrate information, enzyme information, consumption rate, maximum rate, and their product value. The product operation is a simple numerical multiplication operation. All original values ​​must be in the same unit to ensure the comparability of the product values. For example, if the substrate consumption rate in a set of data is in mg / L / min, it needs to be converted to μM / min according to the molecular weight before the product calculation is performed. After unifying the results, a path segment rate product dataset is constructed. This dataset serves as the basis for the subsequent construction of flux input parameters, where the flux input parameter value of any path segment is its rate product value.

[0034] S402: Based on the flux input parameter value, map the rate product value corresponding to the path segment to the original path sequence structure. By comparing the numerical differences between path segments, classify the input intensity level of the difference, arrange the path segments in descending order of flux input intensity, and obtain the flux hierarchy arrangement sequence. Based on the flux input parameter values ​​obtained in the previous section, the rate product value of each path segment is remapped back to the original path sequence structure, establishing a one-to-one correspondence between path segment numbers and rate product values. By traversing the rate product differences between each pair of path segments, the absolute value difference is calculated. For example, if path segment P001 has a rate product of 62.4 and P002 has a rate product of 48.9, the difference is 13.5. A comparison matrix is ​​constructed based on the rate product values ​​of all path segments. The differences between any two path segments are categorized, and the difference values ​​are divided into multiple input intensity levels. The categorization criteria are set as follows: a difference value less than 10 is low input intensity, 10 to 30 is medium input intensity, and a difference value greater than 30 is low input intensity. For high input intensity, according to the above rules, path segments P001 and P002 are classified as medium input intensity, while P003 and P002 differ by 34.7, which is considered high input intensity. All path segments are then sorted according to their rate product values ​​to construct a flux input intensity ranking sequence. For example, path segment P003 has a rate product of 83.6, ranking first; P001 is second with 62.4; and P002 is third with 48.9. The path segment numbers are arranged in this order to construct a flux hierarchy ranking sequence. This sequence retains the path segment number, rate product value, difference level, and ranking position as the basis for judging flux input difference, and is uniformly output as a path segment intensity ranking table.

[0035] S403: Based on the flux hierarchy arrangement sequence, integrate the original path segment numbers and flux input parameter values ​​of the hierarchical path segments, establish a hierarchical relationship structure table, record the hierarchical number and sorting position of the path segment, and establish a path flux input hierarchy set; Based on the flux hierarchy sequence, the sorted path segments are integrated, and the original number and corresponding flux input parameter value of each path segment are extracted to establish a mapping structure record table. This table uses the hierarchy number as the index field and the sorting position as the auxiliary field, and summarizes the path segment number, rate product value, and sorting order. The hierarchy numbering starts from L1, with the largest flux input value corresponding to L1, and the numbers increase sequentially. For example, path segment P003 has a flux input value of 83.6, corresponding to L1; P001 has 62.4, corresponding to L2; and P002 has 48.9, corresponding to L3, forming the structure: L1→P0 L3→83.6, L2→P001→62.4, L3→P002→48.9. These structures are recorded in a unified hierarchical relationship structure table, and the position of each path segment in the global sort is marked. Each row in the table includes four items: "hierarchical number", "path segment number", "flux input parameter value", and "sort position". A complete set of path flux input hierarchies is constructed. This set of hierarchies serves as the input structure basis for subsequent multi-path flux allocation optimization and metabolic network modeling. The structural prototype and corresponding rate information of each path segment remain unchanged to ensure that the structure is clear and has numerical support.

[0036] Please see Figure 6The specific steps of S5 are as follows: S501: Based on the path flux input hierarchy set, call the SMILES expressions of the microbial genus classification, enzyme EC number, pollutant category and metabolite recorded in each path, combine the elements in order according to the arrangement order of the path segments in the hierarchy set, and construct an arrangement structure including the above four items for each path to generate flux path construction sequence value. Based on the path throughput input hierarchy set, the microbial genus classification, enzyme EC number, contaminant category, and SMILES expression of the metabolite recorded for each path segment are retrieved sequentially. These elements are extracted and combined one by one according to the path segment order, organizing them into a complete expression fragment in the structure of "genus name-EC number-contaminant category-SMILES". For example, in path segment P001, the microbial genus classification is Bacillus, the enzyme EC number is 2.7.1.1, the contaminant category is phenols, and the product SMILES is C6H5OH. After combination, the expression sequence value is "Bacillus-2.7.1.1-phenols-C6H5OH". Similarly, in P002, the genus classification is Acinetobacter, the EC number is 3.1.1.1, and the contaminant category is C6H5OH. The product SMILES is an aliphatic compound, and its expression is CC(=O)OC1=CC=CC=C1C(=O)O, which is combined and expressed as "Acinetobacter-3.1.1.1-aliphatic-CC(=O)OC1=CC=CC=C1C(=O)O". All path segments are collected and spliced ​​according to the hierarchical set arrangement to form a continuous set of flux path construction sequence values. Each sequence value has a unique structural identifier in the subsequent processing and there should be no duplicate numbering. When combining, the format should be uniform, the delimiters should be consistent, and missing fields are not allowed. The extracted SMILES expression fields need to be validated for legality, such as judging that the character length is between 5 and 100 and that it does not contain illegal symbols. All fields are stored in a unified field sequence structure as the basis for the unique structural expression of the path segments.

[0037] S502: Construct sequence values ​​based on the throughput path, extract all element information in each path, connect them in the order of path segments to form structural segments, calculate the information redundancy value between adjacent elements in each structural segment, filter path segments with redundancy values ​​greater than the redundancy judgment threshold, and obtain the throughput path purification structural numerical set. A sequence value set is constructed based on the flux path. The combined structural elements in each path segment are connected to form a structural fragment sequence. Each structural fragment consists of four pieces of information connected end-to-end, with fixed connectors between adjacent pieces of information to form a standard format string, such as "Bacillus→2.7.1.1→phenols→C6H5OH". After completing the structural connections, the information redundancy between each group of adjacent elements is calculated sequentially. Redundancy is understood as the content repetition rate or information overlap between adjacent pieces of information. Analysis is completed through statistical frequency and comparison frequency. Information frequency can be obtained by the frequency of the same item appearing in the dataset. For example, "Bacillus" appears 15% of the time in all path segments, corresponding to the enzyme "2.7". The frequency of ".1.1" is 10%, and the combined frequency of the two is 6%. Based on this ratio, the redundancy is assessed by difference. If the frequency difference between two adjacent items is greater than 10%, it is considered low redundancy; a difference between 5% and 10% is considered medium redundancy; and less than 5% is considered high redundancy. The redundancy threshold is set to 5%, meaning that redundancy is only considered to exist when the frequency difference between adjacent elements is less than this value. For example, in a certain path segment, the frequency difference between "Acinetobacter" and "3.1.1.1" is 3%, and it is marked as a redundant item. All structural fragments that meet the redundancy threshold are filtered out and compiled into a clean structural value set. The path number, original structural value, redundancy judgment label, etc. are recorded uniformly.

[0038] S503: Call the structural content and path number of the path segment in the flux path purification structure numerical set, perform a merging operation in the entire path hierarchy, and connect the purification structure segments of all paths according to the path number to build a static structure connection relationship table and establish a static mapping structure for pollutant metabolism. Based on the existing numerical set of flux path purification structures, each selected structural segment and its corresponding path number are extracted. Within the entire path segment sorting range, the structures are connected in numerical order. Each structural segment is treated as an indivisible unit and horizontally spliced ​​sequentially to form a path structure chain relationship. During the splicing process, the path segment number is used as the primary key, and the connector is used to identify the continuity of the structure. For example, the purification structural segments numbered P001, P002, and P003 are sequentially spliced ​​to form the format structure "P001 structural segment → P002 structural segment → P003 structural segment". After outputting the structural content and number, they are uniformly recorded in the static structural connection relationship table. This table contains fields such as path segment number, purified structural content, and sorting position. Through this structure table, a complete static mapping structure of pollutant metabolism can be formed to show the structural logic and static distribution pattern between different path segments. When recording, it is necessary to ensure that the numbering is continuous and non-repeating, the structural content is consistent, and the splicing order strictly follows the path segment arrangement order of the original hierarchical set.

[0039] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A static simulation method of contaminant metabolism processes coupled with fish intestinal microorganisms and digestive enzymes, characterized in that, The method comprises the following steps: S1: extracting the dominant microorganisms with metabolic effects and corresponding digestive enzymes in the fish intestinal segment, pairing based on the coexistence relationship, obtaining the substrate consumption rate and Michaelis constant, and generating microorganism enzyme reaction pairing information; S2: obtaining the initial concentration of the pollutant according to the microorganism enzyme reaction pairing information, calculating the path starting intensity combined with the initial reaction rate of the substrate, extracting the structure of the metabolite, judging whether it is an enzyme substrate, screening the next reaction node segment, and generating a pollutant conversion node set; S3: calling the pollutant conversion node set, obtaining the product LogP value and the Km value of the downstream enzyme, screening the continuous reaction segment that can be formed, and generating an enzyme-microorganism cooperative path sequence combined with the microorganism abundance response condition; S4: according to the enzyme-microorganism cooperative path sequence, extracting the substrate consumption rate and maximum reaction rate in the path, calculating the flux input parameter, and constructing a path flux input hierarchy set; S5: based on the path flux input hierarchy set, extracting the microorganism genus, enzyme EC number, pollutant type and product SMILES expression in the path, arranging and constructing a path framework in the order of the path, and integrating to generate a pollutant metabolism static mapping structure.

2. The static simulation method of contaminant metabolism processes coupling fish intestinal microorganisms and digestive enzymes according to claim 1, characterized in that, The microorganism enzyme reaction pairing information includes microorganism community abundance, target digestive enzyme species, substrate consumption rate, Michaelis constant, the pollutant conversion node set includes the initial distribution concentration of the pollutant in the intestinal segment, the substrate reaction starting rate of the microorganism, the metabolite structure, and the node segment, the enzyme-microorganism cooperative path sequence includes the product structure, the LogP value, the downstream enzyme Michaelis constant, the node segment, and the response condition of the microorganism community abundance, the path flux input hierarchy set includes the substrate consumption rate, the maximum reaction rate, the path flux input parameter, and the path classification, and the pollutant metabolism static mapping structure includes the microorganism genus classification, the enzyme EC number, the pollutant category, the SMILES expression of the metabolite, and the path order.

3. The static simulation method of contaminant metabolism processes coupling fish intestinal microorganisms and digestive enzymes according to claim 1, characterized in that, The specific steps of S1 are: S101: obtaining the microorganism community abundance with metabolic effects in the fish intestinal segment and the digestive enzyme species in the corresponding segment, arranging the combination information of microorganism species and enzyme name according to the coexistence relationship of microorganisms and enzymes, and establishing a microorganism enzyme combination list; S102: according to the corresponding relationship between the microorganisms and enzymes in the combination item in the microorganism enzyme combination list, extracting the substrate change data of the combination in the corresponding segment, calculating the substrate consumption rate value, and generating a substrate consumption rate record table; S103: calling the data in the substrate consumption rate record table, combining the corresponding substrate concentration change information and reaction rate parameters, calculating the Michaelis constant value of each pair of combinations, summarizing the associated parameters, and generating microorganism enzyme reaction pairing information.

4. The static simulation method of pollutant metabolism processes coupling fish intestinal microorganisms and digestive enzymes according to claim 3, characterized in that, The specific steps of S2 are: S201: Based on the microbial enzyme reaction pairing information, the primary distribution concentration of the pollutants in each pair of combination in the intestinal segment is obtained, the substrate reaction initial rate of the corresponding microorganism is called, and the numerical relationship between the combination pollutant primary concentration and the initial rate is executed. The parameter operation is recorded, the strength performance of the path starting stage is generated, and the path starting strength parameter value is generated; S202: The path starting strength parameter value corresponding to the pairing combination is called, the metabolic product structure information generated in the combination is extracted, the matching degree between the metabolic product structure and the enzyme structure action site is detected, and the structure type that can be identified is marked according to whether there is an action response result. The enzyme recognition structure marker amount is obtained; S203: According to the enzyme recognition structure marker amount, the metabolic product fragments that meet the enzyme action condition are screened, whether they have the continuity characteristics of entering the next reaction is judged, all node fragments with the continuation reaction ability are recorded and the path source information is recorded, and the pollutant conversion node set is established.

5. The static simulation method of pollutant metabolism process coupling fish intestinal microorganisms and digestive enzymes according to claim 4, characterized in that, The specific steps of S3 are: S301: The pollutant conversion node set is called, the product structure information in the node fragment is extracted, the LogP value corresponding to each structure is obtained, and the corresponding relationship between the LogP value and the node fragment source information is recorded. The structure polarity distribution value is generated; S302: According to the structure polarity distribution value, the Michaelis constant of the corresponding downstream enzyme is obtained, the numerical difference calculation is executed by calling two types of parameters, it is judged whether each node fragment meets the concentration utilization difference condition of forming a continuous reaction, the fragments meeting the condition are screened, and the continuous reaction screening fragment amount is obtained; S303: Based on the continuous reaction screening fragment amount, the available node fragments with the same path source are combined, whether the combined sequence corresponds to the response condition of the microbial community abundance is detected, the path sequence meeting the response condition is recorded and combined with the corresponding enzyme, and the enzyme-microbial collaborative path sequence is established.

6. The static simulation method of pollutant metabolism process coupling fish intestinal microorganisms and digestive enzymes according to claim 5, characterized in that, The specific steps of S4 are: S401: According to the enzyme-microbial collaborative path sequence, the substrate consumption rate and the maximum reaction rate corresponding to each path segment are extracted, the product of the substrate consumption rate and the maximum reaction rate is calculated, and the corresponding value is recorded according to the path segment. The rate product data set of the path segment is established, and the flux input parameter value is obtained; S402: Based on the flux input parameter value, the rate product value corresponding to the path segment is mapped to the original path sequence structure, the numerical difference between the path segments is compared, the input strength level of the difference is divided, the path segments are arranged in order from large to small according to the flux input strength, and the flux hierarchical arrangement sequence is obtained; S403: According to the flux hierarchical arrangement sequence, the original path segment number and the flux input parameter value of the hierarchical path segment are integrated, the hierarchical relationship structure table is established, the hierarchical number and the sorting position of the path segment are recorded, and the path flux input hierarchical set is established.

7. The static simulation method of pollutant metabolism processes coupling fish intestinal microorganisms and digestive enzymes according to claim 6, characterized in that, The specific steps of S5 are: S501: Based on the path flux input level set, call the recorded microbial genus level classification, enzyme EC number, pollutant category and SMILES expression of metabolic product in each path, combine the elements in order according to the arrangement order of path segment in the level set, and construct the arrangement structure including the above four information for each path to generate the flux path construction sequence value; S502: According to the flux path construction sequence value, extract all element information in each path, connect the structure fragments in order of path segment, calculate the information redundancy value between adjacent elements in each structure fragment, and select the path fragments with redundancy value greater than the redundancy judgment threshold to obtain the flux path purification structure numerical set; S503: Call the structure content and path number of the path segment in the flux path purification structure numerical set, perform merging operation in the whole path level range, and connect all path purification structure segments according to the path number to construct the static structure connection relationship table, and establish the pollutant metabolism static mapping structure.

8. The static simulation method of contaminant metabolism processes coupling fish intestinal microorganisms and digestive enzymes according to claim 1, characterized in that, The fish intestinal tract segment refers to the different parts or different types of fish intestinal tract in the fish digestive system; The dominant microorganism of metabolic action refers to the microbial community with high abundance and participating in metabolic reaction in the target fish intestinal tract segment; The digestive enzyme refers to the enzyme participating in the digestion reaction in the fish intestinal tract, including amylase, lipase, protease, and digestive enzyme associated with microbial metabolism; The coexistence relationship refers to the interaction or synergistic relationship of microorganisms and digestive enzymes in the fish intestinal tract; The substrate consumption rate and Michaelis constant refer to the substrate consumption rate and corresponding Michaelis constant in the catalytic reaction of microorganisms and digestive enzymes.

9. The static simulation method of contaminant metabolism processes coupling fish intestinal microorganisms and digestive enzymes according to claim 1, characterized in that, The initial concentration of pollutants refers to the preliminary distribution concentration of pollutants in the different segments of fish intestinal tract, and the pollutants can include environmental pollutants and food residues; The metabolic product structure refers to the molecular structure of metabolic product produced by the catalytic reaction of microorganisms and digestive enzymes, including molecular formula and stereostructure; The node fragment refers to the intermediate product or conversion node in the target reaction step in the pollutant conversion process; The LogP value refers to the distribution coefficient of compound in water phase and lipid phase, reflecting the hydrophilicity or hydrophobicity of compound; The enzyme microbial synergistic path sequence refers to the reaction order or reaction chain of the target metabolic path formed by the interaction of enzyme and microorganism.

10. The static simulation method of contaminant metabolism processes coupling fish intestinal microorganisms and digestive enzymes according to claim 1, characterized in that, The flux input parameter refers to the substrate consumption rate and reaction rate input parameter of each reaction step in the path; The EC refers to the classification number of enzyme (Enzyme Commission number), which is a standard numbering system for identifying and classifying enzymes in enzyme.

Images