Dynamic enzyme-constrained genome scale metabolic network model construction method

Abstract

The invention discloses a dynamic enzyme-constrained genome scale metabolic network model construction method, which comprises the following steps: S1, determining a kinetic model to obtain a reaction flux and a specific numerical value of a metabolite changing along with time; s2, performing upper and lower bound constraint on the same reaction in the ecGEMs according to different reaction fluxes, and solving through FBA to obtain the flux change condition of all the reactions of the organism along with the time change; and S3, verifying the model. The ecGEMs can be dynamic, and the problems that the ecGEMs cannot obtain the metabolite concentration and the intracellular reaction flux changes along with time are solved. And moreover, the accuracy of the ecGEMs model is greatly improved, and the method has important significance on research of systematic biology and synthetic biology.

Description

Technical Field

[0001] The present invention relates to the interdisciplinary field of biology and computational science, and in particular to a method for constructing a dynamic enzyme-constrained genome-scale metabolic network model. Background Art

[0002] Genome-scale metabolic models (GEMs) have been widely studied and used in systems biology and synthetic biology over the past few decades. GEMs link genes encoding enzymes with the reactions catalyzed by these enzymes, and can predict phenotypes from cell genotypes, thereby systematically characterizing various metabolic activities in organisms. GEMs have played an important role in studying biological cell metabolism over the past decade.

[0003] GEMs use Constraint-Based Reconstruction and Analysis Toolbox (COBRA) to simulate the metabolic state of organisms in different environments. Flux balance analysis (FBA) is the most widely used algorithm of COBRA because it can accurately quantify cellular metabolism. FBA simplifies complex metabolic networks into linear programming problems, which significantly reduces the number of parameters and the complexity of the model. This simplification can greatly help analyze cellular metabolism. The mathematical representation of FBA is shown below:

[0004] max c T v

[0005] stSv=0

[0006] lb i ≤v i ≤ub i

[0007] Among them, S∈R m×n is the reaction matrix, m represents the number of metabolites, n represents the number of reactions, v∈R n×1 is the reaction flux, lb i is the lower bound of the reaction of the ith reaction, ub i is the upper bound of the reaction of the ith reaction, c T ∈R 1×n is the target vector.

[0008] FBA characterizes the properties and behaviors of the system by introducing various constraints to achieve an accurate model description without relying heavily on biological information, such as kinetics and thermodynamics. FBA assumes that the system remains steady-state and imposes some constraints on the external fluxes to obtain the objective function value and a set of flux distributions.

[0009] Although GEMs provide a convenient framework for large-scale metabolic studies, they still have some defects. For example, GEMs cannot accurately simulate metabolic changes that occur at higher growth rates, such as overflow metabolism in bacteria and Crabtree effect in Saccharomyces cerevisiae. Enzyme-constrained genome-scale metabolic models (ecGEMs) can solve this problem by integrating enzyme kinetic parameters and enzyme concentrations, and ecGEMs are therefore widely used. ecGEMs are described as follows:

[0010] Sv=0

[0011] lb≤v≤ub

[0012]

[0013] where S∈R m×n is the reaction matrix, m represents the number of metabolites, n represents the number of reactions, v∈R n×1 is the reaction flux, lb is the lower bound of the reaction, ub is the upper bound of the reaction, E i is the enzyme concentration, k cat is the enzyme turnover number, a kinetic parameter of the enzyme.

[0014] However, ecGEMs still have certain limitations. Although ecGEMs can provide the distribution of metabolic fluxes, it cannot obtain information about metabolite concentrations and changes in intracellular reactions over time, which is necessary for studying the dynamic metabolic behavior of organisms. Secondly, ecGEMs make it difficult to quantitatively analyze the interactions of various components in biological cells, which hinders a deeper study of cellular metabolism. In order to address these limitations, kinetic models came into being. Kinetic models use ordinary differential equations (ODEs) to capture the dynamic changes of all components (metabolites, proteins, mRNA, etc.) included in the model. The general expression is as follows:

[0015]

[0016] where S and v represent the stoichiometric matrix and the vector of metabolic reaction flux, respectively. v is determined by the metabolite concentration (x), the kinetic parameters (k), and the enzyme concentration (E). x0 is the initial metabolite concentration, and the kinetic parameters mainly involve the maximum reaction rate constant v max , enzyme catalytic reaction constant k cat , Michaelis constant k m , equilibrium constant k eq etc.

[0017] Since the kinetic model was proposed, how to determine the ODE expression that describes the reaction has become a major problem. One solution is to determine different ODEs for the reaction mechanisms of different reactions based on the reaction principle, and the other is to use the same ODE for all reactions. Either method requires a large amount of experimental data for parameter fitting, which requires a lot of computing resources and time, which is also the difficulty of kinetic modeling. At the same time, the structural and quantitative uncertainties of the kinetic representation increase the difficulty of kinetic modeling. Therefore, kinetic modeling is currently limited to modeling on small metabolic networks that have been well studied, and metabolic dynamics modeling has not been performed at the entire cellular level. In contrast, ecGEMs is modeled and analyzed at the cellular level. Therefore, it is particularly important to integrate kinetic models with ecGEMs to take advantage of each other. Summary of the invention

[0018] The purpose of the present invention is to provide a method for constructing a dynamic enzyme-constrained genome-scale metabolic network model, which uses the time-varying reaction flux obtained from the organism's kinetic model as an input variable, and uses this as a reference to constrain the same reaction in ecGEMs, and performs FBA operations on the constrained ecGEMs, thereby obtaining the distribution of all reaction fluxes in the organism's cells that change with time, so as to solve the problems raised in the above-mentioned background technology.

[0019] To achieve the above-mentioned object of the invention, the present invention provides a method for constructing a dynamic enzyme-constrained genome-scale metabolic network model, comprising the following steps:

[0020] Step S1, determining the kinetic model to obtain the specific values ​​of the reaction flux and the metabolites changing over time;

[0021] Step S2, according to different reaction fluxes, the same reaction in ecGEMs is constrained by upper and lower bounds, and the flux change of all reactions of the organism over time is obtained by FBA solution;

[0022] Step S3: verify the model.

[0023] Furthermore, in step S1, organisms with perfected ecGEMs are collected, including Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, Corynebacterium glutamicum, Yarrowia lipolytica, and Aspergillus niger. The kinetic model involves pathways that have important effects on the metabolism of organisms, including glycolysis pathway, tricarboxylic acid cycle, pentose phosphate pathway, and lipid synthesis.

[0024] Furthermore, in step S1, the input constraints of ecGEMs are obtained by running the organism dynamics model, and the formula is as follows:

[0025] Y i =f i (xi ,t),i=1,2...,m

[0026] where Y i is the reaction flux calculated by the kinetic model of the i-th pathway, and is x i The i-th kinetic model input variable, t is the time variable, and m is the number of kinetic models that have been constructed.

[0027] Furthermore, in step S2, the reaction flux in the kinetic model at different dilution rates is used as a reference value, which is multiplied by a1 (a1∈[0.5,0.9]) as the lower limit of the flux of the same reaction in ecGEMs, and multiplied by a2 (a2∈[1.1,1.5]) as the upper limit of the flux of the same reaction in ecGEMs, and FBA is run to obtain the distribution of all reaction fluxes of Saccharomyces cerevisiae over time.

[0028] Further, the FBA formula is expressed as follows:

[0029] max c T v

[0030] stSv=0

[0031] a1v di ≤v i ≤a2v di

[0032] lb j ≤v j ≤ub j

[0033]

[0034] Among them, S∈R m×n is the reaction matrix, v∈R n×1 is the reaction flux, c T ∈R 1×n is the target vector, v i is the reaction involved in the dynamic model, v di is the reaction flux value obtained by the kinetic model at the current moment, lb j is the lower bound of the j-th reaction, ub j is the upper bound of the jth reaction, v j is the residual reaction flux value, E i is the enzyme concentration, k cat is the enzyme turnover number, a kinetic parameter of the enzyme.

[0035] Furthermore, in step S3, model validation includes three methods: flux variability analysis, model self-validation, and experimental data validation.

[0036] Further, the flux variability analysis method is expressed as follows:

[0037] max / min v i

[0038] stSv=0

[0039] Z0≤γw T

[0040] lb j ≤v j ≤ub j

[0041]

[0042] Where Z0 = w T is the optimal solution of the following formula:

[0043] max c T v

[0044] stSv=0

[0045] lb i ≤v i ≤ub i

[0046] γ is a parameter with a value between 0 and 1, S∈R m×n is the reaction matrix, m represents the number of metabolites, n represents the number of reactions, v∈R n×1 is the reaction flux, lb j is the lower bound of the j-th reaction, ub j is the upper bound of the jth reaction, c T ∈R 1×n is the target vector, v j is the residual reaction flux value, E i is the enzyme concentration, k cat is the enzyme turnover number, a kinetic parameter of the enzyme.

[0047] Furthermore, the model self-validation is based on the actual flux experimental data of the organism, and performs sensitivity analysis on the n reactions common to the kinetic model and the organism's ecGEMs, selecting the m reactions with the largest and smallest sensitivities, a total of 2m reactions, and comparing the remaining n-2m reactions obtained by the dynamic EMP (Saccharomyces cerevisiae glycolysis, Embden-Meyerhof-Parnas pathway) model with the remaining n-2m reactions obtained by running FBA on the 2m reactions fixedly selected from ecGEMs to perform model self-validation.

[0048] Furthermore, the experimental data verification is to compare the reaction flux of the organism's ecGEMs and the reaction flux obtained by integrating the dynamic model of the method with the organism's ecGEMs with the real experimental data of the organism, and obtain the error before and after the combination.

[0049] Compared with the prior art, the present system and method have the following advantages:

[0050] (1) Using flux as the link between dynamic and steady-state models for model integration provides ideas for improving the accuracy of ecGEMs of living organisms;

[0051] (2) The dynamic model is integrated with the biological ecGEMs to solve the problem that steady-state ecGEMs cannot obtain the changes in metabolite concentrations and intracellular reaction fluxes over time. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Flowchart of the method for building a dynamic enzyme-constrained genome-scale metabolic network model.

[0053] Figure 2 A conceptual diagram of the approach to integrating the kinetic model of Saccharomyces cerevisiae with the enzyme-constrained genome-scale metabolic network model.

[0054] Figure 3 The EMP kinetic model includes the main reactions as well as metabolite concentrations and reaction flux change curves.

[0055] Figure 4 This is a comparison chart of the model self-validation error.

[0056] Figure 5 This is a comparison chart of the error between the model and the experimental value before and after integration. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] like Figure 1 Shown is a flow chart of the method of the present invention, and the specific steps are as follows:

[0059] Step S1, determine the kinetic model to obtain the specific values ​​of the reaction flux and the metabolites changing over time.

[0060] First, the kinetic models are collected. The cells involved can be Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, Corynebacterium glutamicum, Yarrowia lipolytica, Aspergillus niger and other organisms that have perfect ecGEMs. The kinetic models can involve pathways that have important effects on the metabolism of organisms, such as glycolysis, tricarboxylic acid cycle, pentose phosphate pathway, and lipid synthesis. Then, according to the corresponding organism ecGEMs, the kinetic models are appropriately modified to make the two models consistent in conditions, and the input constraints of ecGEMs are obtained by running the kinetic models. The mathematical description is:

[0061] Y i =f i (x i ,t),i=1,2...,m

[0062] where Y i is the reaction flux calculated by the kinetic model of the i-th pathway, and is x i The i-th kinetic model input variable, t is the time variable, and m is the number of kinetic models that have been constructed.

[0063] Combination method such as Figure 2 The figure shows the idea of ​​integrating the kinetic model of Saccharomyces cerevisiae with the enzyme-constrained genome-scale metabolic network model.

[0064] Step S2, according to different reaction fluxes, upper and lower bounds are imposed on the same reaction in ecGEMs, and the flux changes of all reactions of the organism over time are obtained by FBA.

[0065] According to the reaction flux in the EMP kinetic model at different dilution rates, it is multiplied by a1 (a1∈[0.5,0.9]) as the lower limit of the flux of the same reaction in ecGEMs, and multiplied by a2 (a2∈[1.1,1.5]) as the upper limit of the flux of the same reaction in ecGEMs. Then run FBA to obtain the distribution of all reaction fluxes of Saccharomyces cerevisiae over time. In this case, FBA becomes:

[0066] max c T v

[0067] stSv=0

[0068] a1v di ≤v i ≤a2v di

[0069] lb j ≤v j ≤ub j

[0070]

[0071] Among them, S∈R m×n is the reaction matrix, v∈R n×1 is the reaction flux, c T ∈R 1×n is the target vector, v i is the reaction involved in the dynamic model, v di is the reaction flux value obtained by the kinetic model at the current moment, lb j is the lower bound of the j-th reaction, ub j is the upper bound of the jth reaction, v j is the residual reaction flux value, E i is the enzyme concentration, k cat is the enzyme turnover number, a kinetic parameter of the enzyme.

[0072] Step S3, model verification.

[0073] Model validation is mainly carried out in three ways:

[0074] Method 1, Flux variability analysis (FVA).

[0075] Flux variability analysis is often used to determine the robustness of metabolic models under various simulation conditions. FVA is mathematically expressed as:

[0076] max / min v i

[0077] stSv=0

[0078] Z0≤γw T

[0079] lb j ≤v j ≤ub j

[0080]

[0081] Where Z0 = w T is the optimal solution of the following formula:

[0082] max c T v

[0083] stSv=0

[0084] lb i ≤v i ≤ub i

[0085] γ is a parameter with a value between 0 and 1, S∈Rm×n is the reaction matrix, m represents the number of metabolites, n represents the number of reactions, v∈R n×1 is the reaction flux, lb j is the lower bound of the j-th reaction, ub j is the upper bound of the jth reaction, c T ∈R 1×n is the target vector, v j is the residual reaction flux value, E i is the enzyme concentration, k cat is the enzyme turnover number, a kinetic parameter of the enzyme. If the FVA analysis shows that the variability of the model is reduced after binding, it means that the model performance is improved.

[0086] Method 2: Model self-verification.

[0087] Using the actual flux experimental data of the organism as a reference, sensitivity analysis was performed on the n reactions common to the kinetic model and the organism's ecGEMs, and the m reactions with the highest and lowest sensitivity, a total of 2m reactions, were selected. The remaining n-2m reactions obtained by the dynamic EMP model were compared with the remaining n-2m reactions obtained by running FBA with the 2m reactions fixedly selected by ecGEMs to perform self-verification of the model. The errors before and after the combination can be obtained, thus indicating whether the combination is reasonable.

[0088] Method 3, experimental data verification.

[0089] The reaction flux of the organism ecGEMs and the reaction flux obtained by integrating the dynamic model of the present invention with the organism ecGEMs are compared with the real experimental data of the organism to obtain the errors before and after the combination, thereby obtaining whether the integration method proposed by the present invention is conducive to improving the accuracy of the organism ecGEMs.

[0090] In order to clearly describe the integration idea, the kinetic model of the Saccharomyces cerevisiae glycolysis (Embden-Meyerhof-Parnas pathway, EMP) pathway was integrated with Saccharomyces cerevisiae ecGEMs to describe the specific implementation method. The integration method of other pathway kinetic models with ecGEMs was the same.

[0091] The kinetic model adopts the well-studied Saccharomyces cerevisiae EMP kinetic model. In order to combine it with ecGEMs in the future, we modified the original EMP kinetic model. Since the combination requires kinetic models at different dilution rates, and the kinetic parameters involved in the kinetic models at different dilution rates are also variable, the kinetic parameters at different dilution rates are calculated by piecewise cubic polynomial interpolation to obtain 8 different dilution rates (0.027h -1 , 0.051h -1, 0.118h -1 , 0.154h -1 , 0.207h -1 , 0.249h -1 , 0.316h -1 , 0.355h -1 ) under the EMP kinetic model. The EMP kinetic model includes 29 reactions (vGLT, vGLK, vPG I, vPFK, vALD, vTPI, vGAPDH, vPGK, vPGM, vENO, vPYK, vPDC, vADH, vG3PDH, vHOR 2, vVacPi, vADK1, vmitoNADH, vETOHt, vGLYCt, vATPase, vmito, vsinkG6P, vsinkF6P, vsinkGAP, vsinkP3G, vsinkPEP, vsinkPYR, vsinkACE), 21 metabolites (G6P, GLC i, NAD, NADH, ATP, P2G, P3G, PEP, PYR, GLYCERAL3P, ADP, AMP, DHAP, GLYC3P, GL YCEROL, ETOH, G1P, TRE, GLYCec, BPG, PHOS), 45 kinetic parameters (G6P, GLCi, NAD, N ADH, ATP, P2G, P3G, PEP, PYR, GLYCERAL3P, ADP, AMP, DHAP, GLYC3P, GLYCEROL, E TOH, G1P, TRE, GLYCec, p_GLT_VmGLT, p.HXK_ExprsCor, p.PGI_ExprsCor, p.PFK_Exp rsCor, p.FBA_ExprsCor, p.TPI_ExprsCor, p.GAPDH_ExprsCor, p.PGK_ExprsCor, p.PG M_ExprsCor, p.ENO_ExprsCor, p.PYK_ExprsCor, p.PDC_ExprsCor, p.ADH_ExprsCor, f_GlCo, p.poly_sinkACE, p.poly_sinkF6P, p.poly_sin kG6P, p.poly_sinkGAP, p.poly_sinkP3G, p.poly_sinkPEP, p.poly_sinkPYR, p_mitoNADHVmax, RQ, p.gam_ngam, adp_atp_ratio, p_T6P_ss).

[0092] For the convenience of description, only the 0.027h dilution rate among the eight different dilution rates is used below. -1The kinetic model under the dilution rate was integrated with e cGEMs, and the integration method of other dilution rates was the same. The 45 kinetic parameters in the model were interpolated by piecewise cubic polynomials to obtain a 0.027h -1The kinetic model under the dilution rate, the 45 kinetic parameters obtained are: G6P = 1.09684093517648, GLCi = 0.118979261665238, NAD = 1.57950584410507, NADH = 0.0104941558949249, ATP = 2.79653743424957, P2G = 0.286343064496289, P3G = 2.38938534937561, PEP=1.15570412745331, PYR=0.266866352602992, GLYCERAL3P=0.00063497 9086903727, ADP=0.766679639293019, AMP=0.218513060483924, DHAP=0.0838634387000135, GL YC3P=0.0402357801663814, GLYCEROL=0.122214112919759, ETOH=0, G1P=0.1, TRE=125.347006856529, GLYCe c=0.100005858162466, p_GLT_VmGLT=1.72719667166439, p.HXK_ExprsCor=1.31243129845865, p.PGI_Exprs Cor=1.00172412081006,p.PFK_ExprsCor=1.00290408474836,p.FBA_ExprsCor=1.11908785294106,p.TPI_E xprsCor=1.21746764132189,p.GAPDH_ExprsCor=1.4727106219162,p.PGK_ExprsCor=1.2970865370221,p.PG M_ExprsCor=1.31190013040651, p.ENO_ExprsCor=1.58021536154459, p.PYK_ExprsCor= 1.15718799856897, p.PDC_ExprsCor=1.34472986275393, p.ADH_ExprsCor=1.284773775 24626, f_GlCo=0.132214112919759, p.poly_sinkACE=-0.00937597409185371, p.poly_s inkF6P=0.00608215002899966, p.poly_sinkG6P=-0.0222991353718, p.poly_sinkGAP=0.00545677667953178, p.poly_sinkP3G=-0.0041405749, p.poly_sinkPEP=-0.0025123373, p.poly_sinkPYR=-0.0484338781019264, p_mitoNADHVmax=0.141225290814443, RQ=1.0467349537037, p.gam_ngam=0.322231922115345, adp_atp_ratio=0.274153183112584, p_T6P_ss=0.153874697609578. According to the calculation, 0.027h. -1 The dynamic EMP model under dilution rate can obtain the curve of metabolites and reaction fluxes changing with time, such as Figure 3 As shown in the figure, the EMP kinetic model includes the main reactions and metabolite concentrations, and the reaction flux change curve. The flux at each moment can be used as the input of the ecGEMs model at this moment to obtain the distribution of all reaction fluxes at this moment.

[0093] The reaction fluxes calculated by the ecGEMs model and the reaction fluxes obtained by the integrated method proposed in the present invention were compared with the experimental values. It can be found that only one of the seven common reactions in the self-verification has a large error, and the errors of the other reactions are very small, which also proves the rationality of the combined method. The specific errors are as follows: Figure 4 As shown in the figure, it is a comparison chart of the model self-verification error. At the same time, the error of the direct calculation using the ecGEMs model is 58.12%, and the error of the integrated method proposed in the present invention is 35.82%, which is a relative improvement of 38.26%. Figure 5 As shown in Figure 3, the error comparison between the model and the experimental value before and after integration is shown.

[0094] In summary, the method of the present invention can make ecGEMs dynamic, solving the problem that ecGEMs cannot obtain metabolite concentrations and intracellular reaction fluxes changing over time. It also greatly improves the accuracy of the ecGEMs model, which is of great significance for the study of systems biology and synthetic biology.

[0095] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing a dynamic enzyme-constrained genome-scale metabolic network model, characterized in that: The following steps are involved: Step S1, determining the kinetic model to obtain the specific values ​​of the reaction flux and the metabolites changing over time; Step S2, according to different reaction fluxes, the same reaction in ecGEMs is constrained by upper and lower bounds, and the flux change of all reactions of the organism over time is obtained by FBA solution; Step S3: verify the model.

2. A method for constructing a dynamic enzyme-constrained genome-scale metabolic network model according to claim 1, characterized in that: In step S1, organisms with perfect ecGEMs are collected, including Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, Corynebacterium glutamicum, Yarrowia lipolytica, and Aspergillus niger. The kinetic model involves pathways that have important effects on the metabolism of organisms, including glycolysis, tricarboxylic acid cycle, pentose phosphate pathway, and lipid synthesis.

3. The method for constructing a dynamic enzyme-constrained genome-scale metabolic network model according to claim 1, characterized in that: In step S1, the input constraints of ecGEMs are obtained by running the biological dynamics model, and the formula is as follows: Y i =f i (x i ,t),i=1,2...,m where Y i is the reaction flux calculated by the kinetic model of the i-th pathway, and is x i The i-th kinetic model input variable, t is the time variable, and m is the number of kinetic models that have been constructed.

4. A method for constructing a dynamic enzyme-constrained genome-scale metabolic network model according to claim 3, characterized in that: In step S2, the reaction flux in the kinetic model at different dilution rates is taken as a reference value, which is multiplied by a1 (a1∈[0.5,0.9]) as the flux lower limit of the same reaction in ecGEMs, and multiplied by a2 (a2∈[1.1,1.5]) was used as the flux upper limit of the same reaction in ecGEMs, and FBA was run to obtain the distribution of all reaction fluxes of Saccharomyces cerevisiae over time.

5. A method for constructing a dynamic enzyme-constrained genome-scale metabolic network model according to claim 4, characterized in that: The FBA formula is expressed as follows: max c T v stSv=0 a1v di ≤v i ≤a2v di lb j ≤v j ≤ub j Among them, S∈R m×n is the reaction matrix, v∈R n×1 is the reaction flux, c T ∈R 1×n is the target vector, v i is the reaction involved in the dynamic model, v di is the reaction flux value obtained by the kinetic model at the current moment, lb j is the lower bound of the j-th reaction, ub j is the upper bound of the jth reaction, v j is the residual reaction flux value, E i is the enzyme concentration, k cat is the enzyme turnover number, a kinetic parameter of the enzyme.

6. A method for constructing a dynamic enzyme-constrained genome-scale metabolic network model according to claim 5, characterized in that: In step S3, model validation includes three methods: flux variability analysis, model self-validation, and experimental data validation.

7. A method for constructing a dynamic enzyme-constrained genome-scale metabolic network model according to claim 6, characterized in that: The flux variability analysis method is expressed as follows: max / min v i stSv=0 Z0≤γw T lb j ≤v j ≤ub j Where Z0 = w T is the optimal solution of the following formula: max c T v stSv=0 lb i ≤v i ≤ub i γ is a parameter with a value between 0 and 1, S∈R m×n is the reaction matrix, m represents the number of metabolites, n represents the number of reactions, v∈R n×1 is the reaction flux, lb i is the lower bound of the j-th reaction, ub i is the upper bound of the jth reaction, c T ∈R 1×n is the target vector, v j is the residual reaction flux value, E i is the enzyme concentration, k cat is the enzyme turnover number, a kinetic parameter of the enzyme.

8. The method for constructing a dynamic enzyme-constrained genome-scale metabolic network model according to claim 6, characterized in that: The model self-validation is based on the actual flux experimental data of the organism, and performs sensitivity analysis on the n reactions common to the kinetic model and the organism's ecGEMs, selecting the m reactions with the largest and smallest sensitivities, a total of 2m reactions, and comparing the remaining n-2m reactions obtained by the dynamic EMP model with the 2m reactions fixedly selected from ecGEMs to run FBA to obtain the remaining n-2m reactions for comparison to perform model self-validation.

9. The method for constructing a dynamic enzyme-constrained genome-scale metabolic network model according to claim 6, characterized in that: The experimental data verification is to compare the reaction flux of the organism's ecGEMs and the reaction flux obtained by integrating the dynamic model of the method with the organism's ecGEMs with the real experimental data of the organism, and obtain the error before and after the combination.

Images