Quantitative heterologous pathway design method and system based on BiGG database

Abstract

The present invention provides a quantitative heterologous pathway design method and system based on the BiGG database, which relates to the field of product synthesis technology. The method comprises: step S1: using a composite model obtained from the BiGG database to construct a high-quality cross-species metabolic network model CSMN; step S2: using the FBA algorithm to calculate the maximum yield V of the product pathway through the CSMN model. product_max Step S3: Calculate the minimum number of heterologous reactions Nmin required to synthesize the non-origin product; Step S4: Calculate the maximum yield V product_max The minimum number of heterologous reactions required to be introduced is Nmax; step S5: gradually introducing heterologous reactions to obtain multiple optimized pathways; the present invention constructs a high-quality cross-species metabolic network model CSMN by quality control of the composite model derived from the BiGG database, and then uses the heterologous reaction gradual introduction algorithm to calculate the optimized pathways for the synthesis of multiple products.

Description

Technical Field

[0001] The present invention relates to the technical field of product synthesis, and in particular to a quantitative heterologous pathway design method and system based on the BiGG database. Background Art

[0002] The production of high-value-added chemicals from renewable biomass has become an effective solution to the environmental challenges associated with fossil resources; microbial cell factories are engineered microorganisms equipped with efficient biosynthetic pathways that enable them to produce desired chemicals from renewable carbon sources; over the past few decades, microorganisms have been used as cell factories to produce a variety of compounds including biofuel platform chemicals, pharmaceuticals and food additives; the synthesis of these chemicals is not limited to natural chemicals that can be produced within the host strain, but also includes the introduction of heterologous pathways to produce non-native chemicals; the introduction of heterologous pathways not only enables the host bacteria to synthesize non-native products beyond their natural capabilities; but it can also reshape the metabolic flux distribution and increase product yield.

[0003] Among the existing technologies, a variety of methods have been developed for heterologous pathway design; some heterologous pathway design methods, such as PATHcre8, MRE, FMM, XTMS and novoPathFinder, usually use graph theory or retrosynthetic methods to predict product pathways; they tend to predict linear pathways from a single substrate to a single product without considering stoichiometric constraints and yields; in metabolic engineering pathways, additional metabolites need to be recruited for the recycling of carbon and cofactors (such as NAD(P)H and ATP); these methods will ignore branches or circular pathways with high yields during the initial pathway search; therefore, quantitative heterologous pathway design methods and systems based on the BiGG database are needed to solve the above problems. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the purpose of the present invention is to provide a quantitative heterologous pathway design method and system based on the BiGG database. By quality controlling the composite model derived from the BiGG database, a high-quality cross-species metabolic network model CSMN is constructed. Then, using heterologous reactions, the algorithm is gradually introduced to calculate the optimized pathways for the synthesis of multiple products, thereby solving the problem that existing methods often ignore high-yield branching or loop pathways during the initial pathway search.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions: In a first aspect, the present invention provides a quantitative heterologous pathway design method based on the BiGG database, comprising the following steps:

[0006] Step S1: Use the composite model obtained from the BiGG database to construct a high-quality cross-species metabolic network model CSMN;

[0007] Step S2: Calculate the maximum yield V of the product pathway using the FBA algorithm through the CSMN model product_max ;

[0008] Step S3: Calculate the minimum number of heterologous reactions Nmin required to synthesize the non-native product;

[0009] Step S4: Calculate the maximum yield V product_max The minimum number of heterologous reactions required to be introduced, Nmax;

[0010] Step S5: Gradually introduce heterologous reactions to obtain multiple optimization pathways.

[0011] Furthermore, the step S1 includes the following sub-steps:

[0012] Step S101: pre-processing the initial composite model, integrating the charge and chemical formula information of the metabolites into the model, and determining the reaction direction;

[0013] Step S102: Penalizing the reaction, including a three-point penalty for reactions that do not conserve charge and mass, except for exchange and transport reactions;

[0014] One point penalty was imposed for responses without annotation information;

[0015] One point was penalized for responses that appeared in fewer than 10 GEMs;

[0016] Step S103: performing pFBA calculations and setting corresponding objective functions and thresholds for different types of errors;

[0017] If the threshold is not met, the reaction with the highest score in the pFBA result is closed, and the pFBA calculation is iterated until the constraint is met and a model without the corresponding error is generated. R ; and add all closed reactions to the set closed middle;

[0018] Step S104: Set closed Add the response back to the Model R , and repeat the pFBA calculation in step S103;

[0019] If the result after adding the reaction meets the threshold, the reaction is returned to the Model R ;

[0020] If the result after the addition reaction does not meet the above threshold, it is determined whether the reaction causing the error is charge and mass non-conservation;

[0021] If so, delete it; if not, correct its direction.

[0022] Furthermore, in step S101, preprocessing includes: downloading the initial compound model from the BiGG database to obtain reactions from 108 genome-scale metabolic models (GEMs); extracting the charge and molecular formula of metabolites from the 108 GEMs; determining the reaction direction based on the maximum counts in the forward, reverse, and reversible directions of the GEMs. The frequency of the reaction direction is expressed as F and calculated using the following formula: Among them, N f , N b , N r represent the counts of the same reaction appearing in different GEMs in the forward, reverse, and reversible directions, respectively;

[0023] Thermodynamic evaluation was performed for reactions that appeared in less than ten GEMs or reactions with a reaction direction frequency F lower than 0.7;

[0024] Thermodynamic evaluation includes: setting metabolic concentration thresholds. If the upper bound of the Gibbs free energy of the reaction is less than 0 kJ / mol or the lower bound is greater than 0 kJ / mol, the reaction is classified as irreversible.

[0025] If the reaction does not have a Gibbs free energy, use the biochemical reaction rules to determine the direction of the reaction.

[0026] Furthermore, step S2 includes the following sub-steps:

[0027] Step S201: Set the responses from different sources in the CSMN model as heterogeneous responses, and integrate them to obtain the heterogeneous response set R hetr ;

[0028] Step S202: Obtain heterogeneous reaction set R hetr In each reaction, the inflow, outflow, storage, conversion and exchange of the reactants are calculated by the FBA algorithm to obtain the flux V of the target product. product ;

[0029] Step S203: Obtain the maximum flux of the target product and set it as V product_max .

[0030] Furthermore, step S3 includes the following sub-steps:

[0031] Step S301: Obtain all heterologous reactions from step S2 whose yields reach the first ratio of the maximum flux of the target product, numbered as j, where j is a positive integer;

[0032] Step 302: Obtain the minimized number of heterologous reactions Nmin using Formulas 1 to 4;

[0033] Wherein, Formula 1 is Formula 2 is S ij ·v j =0; Formula 3 is Formula 4 is 0.1v product_max ≤v product ;

[0034] In the above formula, the set R hetr represents the heterologous reaction set, Y j represents a binary variable, V product is the flux of the target product, S ij Represents the reaction coefficient measurement matrix of the CSMN model, v j is the flux of the reaction in the model CSMN, v j lb and v j ub represent the lower and upper limits of the reaction flux, respectively;

[0035] Formula 1 is to minimize the number of heterologous reactions; Formula 2 describes the mass balance constraint under steady state; Formula 3 indicates whether the heterologous reaction is introduced into the chassis; Formula 4 represents the flux V of the target product product Not less than the maximum flux V of the product pathway product_max 10% of.

[0036] Furthermore, step S4 includes:

[0037] The maximum yield V can be calculated by using formulas 5 to 8. product_max The minimum number of heterologous reactions required to be introduced, Nmax;

[0038] Among them, Formula 5 is Formula 6 is S ij ·v j =0; Formula 7 is Formula 8 is v product =v product_max ;

[0039] In the above formula, formula 5 is to maximize the number of heterologous reactions; formula 8 represents the flux V of the target product product The maximum flux V of the product pathway is reached product_max .

[0040] Furthermore, step S5 includes gradually introducing heterologous reactions from Nmin+1 to Nmax-1 using formulas 9 to 13, and displaying the optimization process and obtaining multiple optimization pathways;

[0041] Wherein, formula 9 is max v product ; Formula 10 is S ij ·v j =0; Formula 11 is Formula 12 is 0.1v product_max <v product <v product_max Formula 13 is

[0042] In the above formula, formula 9 represents maximizing the flux of the target product; formula 12 represents that the flux of the target product must not be less than the maximum flux V of the product pathway. product_max 10% and must not exceed the maximum flux V of the product pathway product_max ; Formula 13 indicates that heterologous reactions are introduced gradually, and the number of heterologous reactions ranges from the number of heterologous reactions Nmin+1 to the number of heterologous reactions Nmax+1.

[0043] In a second aspect, the present invention also provides a quantitative heterologous pathway design system based on the BiGG database, the system comprising a model building module, an error elimination module and a pathway generation module;

[0044] The model building module is used to obtain a composite model from the BiGG database to construct a high-quality cross-species metabolic network model CSMN;

[0045] The error elimination module is used to identify and correct errors in the model CSMN;

[0046] The pathway generation module is used to gradually introduce heterologous reactions through the CSMN model to obtain multiple optimized pathways.

[0047] The beneficial effects of the present invention are as follows: the present invention first obtains a composite model from the BiGG database to construct a high-quality cross-species metabolic network model CSMN. The CSMN can be effectively used to accurately calculate product yields and ensure that the yields of all metabolites do not exceed the maximum theoretical yield. If the BiGG database may be updated in the future, the CSMN can also be seamlessly and automatically updated through this workflow; then, an automated error elimination algorithm identifies and corrects errors in the model CAMN. Different objective functions and thresholds are set for different error types, and various types of errors are effectively corrected, thereby improving the efficiency of error correction and ensuring the accuracy of the model CSMN; finally, an algorithm that gradually introduces heterologous reactions is used to use the model CSMN to calculate multiple optimized pathways with higher yields, which can directly distinguish the minimum heterologous reactions used for product synthesis and optimization, thereby improving the efficiency of obtaining optimized pathways.

[0048] Advantages of additional aspects of the present invention will be given in part in the description of the following specific embodiments, and in part will become apparent from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0050] Figure 1 Design a method step diagram for the approach of the present invention;

[0051] Figure 2 Schematic diagram of the system for designing the approach of the present invention. DETAILED DESCRIPTION

[0052] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0053] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0054] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0055] Example 1, in a first aspect, a quantitative heterologous pathway design method based on the BiGG database comprises the following steps:

[0056] Step S1: Use the composite model obtained from the BiGG database to construct a high-quality cross-species metabolic network model CSMN; Step S1 includes the following sub-steps:

[0057] Step S101: pre-processing the initial composite model, integrating the charge and chemical formula information of the metabolites into the model, and determining the reaction direction;

[0058] In step S101, preprocessing includes: downloading the initial compound model from the BiGG database to obtain reactions from 108 genome-scale metabolic models (GEMs); extracting the charge and molecular formula of metabolites from the 108 GEMs; determining the reaction direction based on the maximum counts in the forward, reverse, and reversible directions of the GEMs. The frequency of the reaction direction is expressed as F and calculated using the following formula: Among them, N f , N b , N r represent the counts of the same reaction appearing in different GEMs in the forward, reverse, and reversible directions, respectively;

[0059] During implementation, the model obtained after preliminary processing contained various errors, preventing the model from calculating the correct product yields through flux balance analysis. These errors included the net production of metabolites; for example, incorrect pathways resulting from mass-imbalance reactions led to the net production of the metabolite glcr (D-gluconic acid). Other errors included the unlimited generation of reducing power and energy, and pathway errors where the product yield exceeded the maximum theoretical yield calculated based on the degree of substrate and product reduction. Due to the complexity of the metabolic network, manual identification and correction of these errors were overly cumbersome, so the following method was used to correct the model errors:

[0060] Thermodynamic evaluation was performed for reactions that appeared in less than ten GEMs or reactions with a reaction direction frequency F lower than 0.7;

[0061] Thermodynamic evaluation includes: setting metabolic concentration thresholds. If the upper bound of the Gibbs free energy of the reaction is less than 0 kJ / mol or the lower bound is greater than 0 kJ / mol, the reaction is classified as irreversible.

[0062] If the reaction does not have a Gibbs free energy, use the biochemical reaction rules to determine the direction of the reaction;

[0063] Step S102: Penalizing the reaction, including a three-point penalty for reactions that do not conserve charge and mass, except for exchange and transport reactions;

[0064] One point penalty was imposed for responses without annotation information;

[0065] One point was penalized for responses that appeared in fewer than 10 GEMs;

[0066] Step S103: performing pFBA calculations and setting corresponding objective functions and thresholds for different types of errors;

[0067] If the threshold is not met, the reaction with the highest score in the pFBA result is closed, and the pFBA calculation is iterated until the constraint is met and a model without the corresponding error is generated. R ; and add all closed reactions to the set closed middle;

[0068] Step S104: Set closed Add the response back to the Model R , and repeat the pFBA calculation in step S103;

[0069] If the result after adding the reaction meets the threshold, the reaction is returned to the Model R ;

[0070] If the result after the addition reaction does not meet the above threshold, it is determined whether the reaction causing the error is charge and mass non-conservation;

[0071] If so, delete it; if not, correct its direction;

[0072] During the implementation, 45 reactions with mass imbalances were corrected, 12 reactions with incorrect directions were corrected, and 175 reactions with mass imbalances containing large molecules, biomass, or no annotation information were deleted. After eliminating all errors, the final constructed CSMN model included 15,638 metabolites and 28,110 reactions. CSMN can be effectively used to accurately calculate product yields, ensuring that the production of all metabolites does not exceed the maximum theoretical yield. Furthermore, if the BiGG database is updated in the future, CSMN can also be seamlessly and automatically updated through this workflow.

[0073] Step S2: Calculate the maximum yield V of the product pathway using the FBA algorithm through the CSMN model product_max ; Step S2 includes the following sub-steps:

[0074] Step S201: Set the responses from different sources in the CSMN model as heterogeneous responses, and integrate them to obtain the heterogeneous response set R hetr ;

[0075] Step S202: Obtain heterogeneous reaction set R hetr In each reaction, the inflow, outflow, storage, conversion and exchange of the reactants are calculated by the FBA algorithm to obtain the flux V of the target product. product ;

[0076] Step S203: Obtain the maximum flux of the target product and set it as V product_max .

[0077] Step S3: Calculate the minimum number of heterologous reactions Nmin required to synthesize the non-native product; Step S3 includes the following sub-steps:

[0078] Step S301: Obtain all heterologous reactions from step S2 whose yields reach the first ratio of the maximum flux of the target product, numbered as j, where j is a positive integer;

[0079] Step 302: Obtain the minimized number of heterologous reactions Nmin using Formulas 1 to 4;

[0080]

[0081] S ij ·v j =0 (Formula 2)

[0082]

[0083] 0.1v product_max ≤v product (Formula 4)

[0084] In the above formula, the set R hetr Represents a heterologous reaction set. j represents a binary variable, V product is the flux of the target product, S ij Represents the reaction coefficient measurement matrix of the CSMN model, v j is the flux of the reaction in the model CSMN, v j lb and v j ub represent the lower and upper limits of the reaction flux, respectively;

[0085] Formula 1 is to minimize the number of heterologous reactions; Formula 2 describes the mass balance constraint under steady state; Formula 3 indicates whether the heterologous reaction is introduced into the chassis; Formula 4 represents the flux V of the target product product Not less than the maximum flux V of the product pathway product_max 10% of.

[0086] Step S4: Calculate the maximum yield V product_max The minimum number of heterologous reactions required to be introduced is Nmax; Step S4 includes:

[0087] The maximum yield V can be calculated by using formulas 5 to 8. product_max The minimum number of heterologous reactions required to be introduced, Nmax;

[0088]

[0089] S ij ·v j =0 (Formula 6)

[0090]

[0091] v product =v product_max (Formula 8)

[0092] In the above formula, formula 5 is to maximize the number of heterologous reactions; formula 8 represents the flux V of the target product product The maximum flux V of the product pathway is reached product_max .

[0093] Step S5: gradually introduce heterologous reactions to obtain multiple optimized pathways;

[0094] Step S5 includes gradually introducing heterologous reactions from Nmin+1 to Nmax-1 using Formula 9 to Formula 13, and displaying the optimization process and obtaining multiple optimization pathways;

[0095] max v product (Formula 9)

[0096] S ij ·v j =0 (Formula 10)

[0097]

[0098] 0.1v product_max <v product <v product_max (Formula 12)

[0099]

[0100] In the above formula, formula 9 represents maximizing the flux of the target product; formula 12 represents that the flux of the target product must not be less than the maximum flux V of the product pathway. product_max 10% and must not exceed the maximum flux V of the product pathway product_max Formula 13 represents the gradual introduction of heterologous reactions, with the number of heterologous reactions ranging from Nmin+1 to Nmax+1. In a specific implementation, for example, the yield of spermidine in the endogenous pathway of Escherichia coli is low due to the loss of 5-methylthio-D-ribose (5mtr). This method was used to optimize the spermidine pathway using Escherichia coli as the chassis and glucose as the substrate. Four optimized pathways were obtained, all of which had higher yields than the endogenous pathway P0. With the continuous introduction of heterologous pathways, the product yield continued to increase.

[0101] When two heterologous reactions were introduced, the yield of spermidine reached 97% of the maximum yield. Compared with the original pathway P0, the yield of the optimized pathway P2 increased by 118%.

[0102] Pathway P2 effectively reduced carbon loss and increased yield by introducing these two heterologous reactions.

[0103] Example 2, second aspect, a quantitative heterologous pathway design system based on the BiGG database, the system comprising a model building module, an error elimination module and a pathway generation module;

[0104] The model building module is used to obtain composite models from the BiGG database to construct a high-quality cross-species metabolic network model CSMN. The model building module is configured with a preprocessing strategy, which includes: downloading the initial composite model from the BiGG database to obtain reactions from 108 genome-scale metabolic models (GEMs); extracting the charge and molecular formula of metabolites from the 108 GEMs; determining the reaction direction based on the maximum counts in the forward, reverse, and reversible directions of the GEMs. The frequency of the reaction direction is expressed as F and calculated using the following formula: Among them, N f , N b , N r represent the counts of the same reaction appearing in different GEMs in the forward, reverse, and reversible directions, respectively;

[0105] The error elimination module is used to identify and correct errors in the model;

[0106] The error elimination module is configured with an error elimination strategy, which includes thermodynamic evaluation of reactions that appear in less than ten GEMs or reactions with a reaction direction frequency F lower than 0.7: setting a metabolic concentration threshold, if the upper bound of the Gibbs free energy of the reaction is less than 0 kJ / mol or its lower bound is greater than 0 kJ / mol, it is considered an irreversible reaction;

[0107] If the reaction does not have a Gibbs free energy, use the biochemical reaction rules to determine the direction of the reaction;

[0108] The reactions are then penalized, including a three-point penalty for reactions that do not conserve charge and mass except for exchange and transport reactions;

[0109] One point penalty was imposed for responses without annotation information;

[0110] One point was penalized for responses that appeared in fewer than 10 GEMs;

[0111] Perform pFBA calculations and set corresponding objective functions and thresholds for different types of errors;

[0112] If the threshold is not met, the reaction with the highest score in the pFBA result is closed, and the pFBA calculation is iterated until the constraint is met and a model without the corresponding error is generated. R ; and add all closed reactions to the set closed middle;

[0113] Finally, set closed Add the response back to the Model R , and repeat the pFBA calculation in step S103;

[0114] If the result after adding the reaction meets the threshold, the reaction is returned to the Model R ;

[0115] If the result after the addition reaction does not meet the above threshold, it is determined whether the reaction causing the error is charge and mass non-conservation;

[0116] If so, delete it; if not, correct its direction;

[0117] The pathway generation module is used to gradually introduce heterologous reactions through the CSMN model to obtain multiple optimized pathways. The pathway generation module is configured with a pathway generation strategy, which includes: using the FBA algorithm to calculate the maximum yield V of the product pathway through the CSMN model product_max ; Calculate the minimum number of heterologous reactions Nmin required to synthesize non-native products; Calculate the maximum yield V product_max The minimum number of heterologous reactions required to be introduced is Nmax; heterologous reactions are gradually introduced from the number of heterologous reactions Nmin+1 to the number of heterologous reactions Nmax+1 to obtain multiple optimization paths.

[0118] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] The above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or perform equivalent replacements on some of the technical features thereof. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A quantitative heterologous pathway design method based on the BiGG database, characterized in that: The steps include: Step S1: Use the composite model obtained from the BiGG database to construct a high-quality cross-species metabolic network model CSMN; Step S2: Calculate the maximum yield V of the product pathway using the FBA algorithm through the CSMN model product_max ; Step S3: Calculate the minimum number of heterologous reactions Nmin required to synthesize the non-native product; Step S4: Calculate the maximum yield V product_max The minimum number of heterologous reactions required to be introduced, Nmax; Step S5: gradually introduce heterologous reactions to obtain multiple optimized pathways; The step S1 includes the following sub-steps: Step S101: pre-processing the initial composite model, integrating the charge and chemical formula information of the metabolites into the model, and determining the reaction direction; Step S102: Penalizing the reaction, including a three-point penalty for reactions that do not conserve charge and mass, except for exchange and transport reactions; One point penalty was imposed for responses without annotation information; One point was penalized for responses that appeared in fewer than 10 GEMs; Step S103: performing pFBA calculations and setting corresponding objective functions and thresholds for different types of errors; If the threshold is not met, the reaction with the highest score in the pFBA result is closed, and the pFBA calculation is iterated until the constraint is met and a model without the corresponding error is generated. R ; and add all closed reactions to the set closed middle; Step S104: Set closed Add the response back to the Model R , and repeat the pFBA calculation in step S103; If the result after adding the reaction meets the threshold, the reaction is returned to the Model R ; If the result after the addition reaction does not meet the above threshold, it is determined whether the reaction causing the error is charge and mass non-conservation; If so, delete it; if not, correct its direction.

2. The quantitative heterologous pathway design method based on the BiGG database according to claim 1, characterized in that In step S101, preprocessing includes: downloading the initial compound model from the BiGG database to obtain reactions from 108 genome-scale metabolic models (GEMs); extracting the charge and molecular formula of metabolites from the 108 GEMs; determining the reaction direction based on the maximum counts in the forward, reverse, and reversible directions of the GEMs. The frequency of the reaction direction is expressed as F and calculated using the following formula: Among them, N f , N b , N r represent the counts of the same reaction appearing in different GEMs in the forward, reverse, and reversible directions, respectively; Thermodynamic evaluation was performed for reactions that appeared in less than ten GEMs or reactions with a reaction direction frequency F lower than 0.7; Thermodynamic evaluation includes: setting metabolic concentration thresholds. If the upper bound of the Gibbs free energy of the reaction is less than 0 kJ / mol or the lower bound is greater than 0 kJ / mol, the reaction is classified as irreversible. If the reaction does not have a Gibbs free energy, use the biochemical reaction rules to determine the direction of the reaction.

3. The quantitative heterologous pathway design method based on the BiGG database according to claim 2, characterized in that Step S2 includes the following sub-steps: Step S201: Set the responses from different sources in the CSMN model as heterogeneous responses, and integrate them to obtain the heterogeneous response set R hetr ; Step S202: Obtain heterogeneous reaction set R hetr In each reaction, the inflow, outflow, storage, conversion and exchange of the reactants are calculated by the FBA algorithm to obtain the flux V of the target product. product ; Step S203: Obtain the maximum flux of the target product and set it as V product_max .

4. The quantitative heterologous pathway design method based on the BiGG database according to claim 3, characterized in that Step S3 includes the following sub-steps: Step S301: Obtain all heterologous reactions from step S2 whose yields reach the first ratio of the maximum flux of the target product, numbered as j, where j is a positive integer; Step 302: Obtain the minimized number of heterologous reactions Nmin using Formulas 1 to 4; Wherein, Formula 1 is Formula 2 is S ij ·v j =0; Formula 3 is Formula 4 is 0.1v product_max ≤v product ; In the above formula, the set R hetr represents the heterologous reaction set, Y j Represents a binary variable, V product is the flux of the target product, S ij Represents the reaction coefficient measurement matrix of the CSMN model, v j is the flux of the reaction in the model CSMN, v j lb and v j ub represent the lower and upper limits of the reaction flux, respectively; Formula 1 is to minimize the number of heterologous reactions; Formula 2 describes the mass balance constraint under steady state; Formula 3 indicates whether the heterologous reaction is introduced into the chassis; Formula 4 represents the flux V of the target product product Not less than the maximum flux V of the product pathway product_max 10% of.

5. The quantitative heterologous pathway design method based on the BiGG database according to claim 4, characterized in that Step S4 includes: The maximum yield V can be calculated by using formulas 5 to 8. product_max The minimum number of heterologous reactions required to be introduced is Nmax; where formula 5 is Formula 6 is S ij ·v j =0; Formula 7 is Formula 8 is v product =v product_max In the above formula, formula 5 is to maximize the number of heterologous reactions; formula 8 represents the flux V of the target product product The maximum flux V of the product pathway is reached product_max .

6. The quantitative heterologous pathway design method based on the BiGG database according to claim 5, characterized in that The step S5 includes using formula 9 to formula 13 to gradually introduce heterologous reactions from Nmin+1 to Nmax-1, and display the optimization process and obtain multiple optimization paths; wherein formula 9 is maxv product ; Formula 10 is S ij ·v j =0; Formula 11 is Formula 12 is 0.1v product_max <v product <v product_max ; Formula 13 is In the above formula, formula 9 represents maximizing the flux of the target product; formula 12 represents that the flux of the target product must not be less than the maximum flux V of the product pathway. product_max 10% and must not exceed the maximum flux V of the product pathway product_max ; Formula 13 indicates that heterologous reactions are introduced gradually, and the number of heterologous reactions ranges from the number of heterologous reactions Nmin+1 to the number of heterologous reactions Nmax+1.

7. A system for the quantitative heterologous pathway design method based on the BiGG database according to any one of claims 1 to 6, characterized in that: It includes model building module, error elimination module and pathway generation module; The model building module is used to obtain a composite model from the BiGG database to construct a high-quality cross-species metabolic network model CSMN; The error elimination module is used to identify and correct errors in the model; The pathway generation module is used to gradually introduce heterologous reactions through the CSMN model to obtain multiple optimized pathways.

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