Tool kit for constructing personalized multi-constraint metabolic network model based on modular thinking

Abstract

The invention relates to the technical field of system biology, in particular to a toolkit for constructing a personalized multi-constraint metabolic network model based on modular thinking. The Core module is used for defining and managing a core object and a structure in a toolkit and constructing and expanding a multi-constraint metabolic network model; the Analysis module is used for providing a function tool for analyzing the model; the Ecmpy module is used for acquiring corrected enzyme kinetic parameters and automatically constructing an enzyme constraint model; the lo module is used for reading, writing and processing multi-constraint metabolic network model data; the Plotting module is used for visualizing multi-constraint metabolic network model data and an analysis result; and the Tmodel module is used for automatically acquiring thermodynamic parameters and automatically constructing a thermodynamic constraint model. According to the toolkit for constructing the personalized multi-constraint metabolic network model based on modular thinking, through modular design and automatic parameter acquisition and model construction functions, the demand of manual intervention is reduced, the flexibility and expansibility of modeling are improved, and the modeling speed and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of systems biology, and in particular to a toolkit for constructing a personalized multi-constraint metabolic network model based on modular thinking. Background Art

[0002] A toolkit for building personalized multi-constraint metabolic network models based on modular thinking. It aims to modularly build personalized multi-constraint metabolic network models. It does not rely on the model structure defined in COBRApy and Cobra Tool box, but adopts a new multi-constraint model building system, allowing users to freely build any model according to their needs.

[0003] In the traditional construction of multi-constraint metabolic network models, not only does it require placing mathematical expressions into template model functions, but it also requires complex definitions and conversions, which can lead to complex operations, time-consuming and error-prone problems. Therefore, a toolkit based on modular thinking to build personalized multi-constraint metabolic network models is designed. Summary of the invention

[0004] The purpose of the present invention is to provide a toolkit for constructing personalized multi-constraint metabolic network models based on modular thinking, so as to solve the problem proposed in the above background technology that constructing multi-constraint metabolic network models not only requires placing mathematical expressions into template model functions, but also requires complex definitions and conversions, which will lead to complex operations, long time consumption and prone to errors.

[0005] To achieve the above object, the present invention aims to provide a toolkit for building a personalized multi-constraint metabolic network model based on modular thinking, including: a Core module, the Core module is used to define and manage core objects and structures in the toolkit, and build an extended multi-constraint metabolic network model;

[0006] Analysis module, which provides a functional tool for analyzing a multi-constraint metabolic network model;

[0007] Ecmpy module, which is used to obtain and correct enzyme kinetic parameters and automatically construct enzyme constraint models;

[0008] Io module, the lo module is used to read, write and process multi-constraint metabolic network model data;

[0009] A Plotting module, which is used to visualize multi-constraint metabolic network model data, pathway calculation results, and model analysis results;

[0010] Tcmodel module, the TcmodelI module is used to automatically obtain thermodynamic parameters and automatically build a thermodynamic constraint model.

[0011] The Core module includes Model objects, Gene objects, Reaction objects, Metabolite objects, Environment objects and multi-constraint model template construction units;

[0012] The Model object is used to define the structure and operation of the entire metabolic network model, manage all reactions, metabolites and genes contained in the model, and contains the variable information required for multi-constraint model analysis and its acquisition and modification functions;

[0013] The Gene object adds uniprot, a key enzyme constraint model construction variable, to define the relevant properties and functions of the gene and manage gene information;

[0014] The Reaction object adds the variable information required for constructing the kcat, MW and g0 multi-constraint model to define the relevant properties and functions of the reaction and manage the information related to the reaction;

[0015] Add metabolite concentration variable information to the Metabolite object to define the properties and functions of the metabolites and manage the metabolite information;

[0016] The Environment object is used to define environmental parameters, including pH, pMg, ionic strength, and temperature.

[0017] pH: pH value of the environment.

[0018] pMg: magnesium ion concentration in the environment.

[0019] ionic_strength: ionic strength of the environment;

[0020] temperature: ambient temperature;

[0021] get_pH():get pH value;

[0022] set_pH(): sets the pH value;

[0023] As a further improvement of the technical solution, the multi-constraint model template construction unit is used to define and manage multi-constraint metabolic model templates, and use the basic model skeleton under the Pyomo framework to construct metabolic network models, enzyme constraint models, thermodynamic constraint models, and enzyme thermal constraint models;

[0024] As described above, the multi-constraint model template constructs and manages a multi-constraint metabolic network model, as follows:

[0025] S1.1. Create a Pyomo ConcreteModel object and define metabolite concentration variables, reaction flux variables, thermodynamic driving force variables, and binary variables.

[0026] S1.2. Add and manage metabolites, and set the upper and lower limits of metabolite concentrations through the function set_metabolite_constraints;

[0027] S1.3. Add and manage reactions: Set the thermodynamic driving force constraints of the reaction through the function set_reaction_thermodynamic_constraints;

[0028] S1.4. Set and manage constraints, including stoichiometric constraints, reaction flux upper and lower limits, medium substrate concentration constraints, enzyme total amount constraints, thermodynamic constraints, and integer problem constraints;

[0029] S1.5. Define a solve_model function to solve the model and return the result.

[0030] As a further improvement of the technical solution, the specific method for setting the upper and lower limits of metabolite concentration in S1.2 is:

[0031] ln(C i，min )≤x i ≤ln(C i，max );

[0032] Among them, C i，min is the minimum concentration of metabolite i; C i，max is the maximum concentration of metabolite i;

[0033] As described above, the specific method for setting the thermodynamic driving force constraint of the reaction in S1.3 is:

[0034]

[0035] Among them, Df i is the thermodynamic driving force of reaction i; is the Gibbs free energy change of reaction i under standard conditions; R is the gas constant; T is the absolute temperature; is the transpose of the stoichiometric coefficient vector of reaction i; x i is a vector of logarithmic values ​​of the metabolite concentrations of reaction i.

[0036] As a further improvement of the technical solution, the specific method for setting the stoichiometric constraints in S1.4 is:

[0037]

[0038] The specific method for setting the upper and lower limits of the reaction flux is:

[0039] lb≤v j ≤ub;

[0040] The specific method for setting the culture medium substrate concentration constraint is:

[0041] v substrate ≤v medium ;

[0042] The specific method to set the enzyme total amount constraint is:

[0043]

[0044] The specific method for setting thermodynamic constraints is:

[0045] Df i +(1-z i )·K≥Df min ;

[0046] The specific method for setting integer problem constraints is:

[0047] v i ≤z i *v ub ;

[0048] Among them, v i is the flux of reaction i; z i is a binary variable of response i; v ub is the upper limit of the flux of reaction i.

[0049] As a further improvement of the present technical solution, the function tools in the Analysis module analyze the constructed multi-constraint metabolic network model, and the analysis includes flux balance analysis, metabolic flux analysis, sensitivity analysis, steady-state analysis and flux change analysis.

[0050] As a further improvement of the technical solution, the Ecmpy module includes a parameter acquisition unit, a parameter correction unit and a model automation construction unit;

[0051] The parameter acquisition unit acquires enzyme kinetic parameters through API call and preset data source, and integrates them into the multi-constraint metabolic network model;

[0052] Wherein, the parameter correction unit corrects and adjusts the enzyme kinetic parameters from the parameter acquisition unit;

[0053] Among them, the model automation construction unit integrates the enzyme kinetic parameters into the constraints of the model based on the acquired and corrected enzyme kinetic parameters, defines the total amount constraints of the enzyme, and automatically constructs the enzyme constraint metabolic network model.

[0054] As a further improvement of the present technical solution, the lo module includes a file reading and writing unit, a parameter saving unit and a JSON file processing unit;

[0055] Wherein, the file reading and writing unit is used to save and read the data and parameters of the multi-constraint metabolic network model in JSON format through file I / O operations;

[0056] Wherein, the parameter storage unit stores the multi-constraint metabolic network model parameters in JSON format;

[0057] The JSON file processing unit is used to process the reading and writing operations of the JSON file.

[0058] As a further improvement of the technical solution, the Plotting module includes a model data display unit, a pathway calculation display unit and a model analysis display unit;

[0059] Wherein, the model data display unit displays the parameters of the multi-constraint metabolic network model, including metabolite concentrations and reaction fluxes, and generates graphics for parameter display using a visualization library;

[0060] Wherein, the pathway calculation display unit generates a metabolic pathway diagram using a graphics library to visually display the metabolic pathway calculation results;

[0061] Wherein, the model analysis results are visualized, including flux balance analysis, metabolic flux analysis, sensitivity analysis, steady-state analysis and flux change analysis results.

[0062] As a further improvement of the technical solution, the Tcmode 1 module includes an automatic parameter acquisition unit and an automatic model construction unit;

[0063] Wherein, the automatic parameter acquisition unit automatically acquires the thermodynamic parameters of the reactions and metabolites involved in the model by calling the database API and using a preset thermodynamic data file;

[0064] Wherein, the automatic model building unit automatically builds a metabolic network model with thermodynamic constraints based on the acquired thermodynamic parameters.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] 1. This toolkit is based on modular thinking to build personalized multi-constrained metabolic network models. Through modular design and implementation, the model building process can be significantly simplified, the modeling efficiency can be improved, and the error rate can be reduced;

[0067] 2. This toolkit is based on modular thinking to build personalized multi-constraint metabolic network models. Through automated parameter acquisition and model building functions, biological data can be quickly integrated and analyzed to achieve accurate metabolic network simulation and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a flowchart of the overall process of the present invention.

[0069] The meaning of each number in the figure is:

[0070] 1. Core module; 2. Analysis module; 3. Ecmpy module; 4. lo module; 5. Plotting module; 6. Tcmodel module. DETAILED DESCRIPTION

[0071] 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.

[0072] Example

[0073] See also Figure 1 As shown, a toolkit for building a personalized multi-constraint metabolic network model based on modular thinking is provided, including a Core module 1, wherein the Core module 1 is used to define and manage core objects and structures in the toolkit to build an extended multi-constraint metabolic network model;

[0074] The Core module (1) includes a Model object, a Gene object, a Reaction object, a Metabolite object, an Environment object and a multi-constraint model template construction unit;

[0075] The Model object is used to define the structure and operation of the entire metabolic network model, manage all reactions, metabolites and genes contained in the model, and contains the variable information required for multi-constraint model analysis and its acquisition and modification functions;

[0076] Among them, the variables and functions involved in the Model object are as follows:

[0077] medium: substrate concentration of the culture medium;

[0078] K_value: constant value in thermodynamic constraints;

[0079] E_total: enzyme total amount constraint;

[0080] get_medium(): Get medium information;

[0081] get_obj_name(): Get the target function name;

[0082] The Gene object adds uniprot, a key enzyme constraint model construction variable, to define the relevant properties and functions of the gene and manage gene information;

[0083] Among them, the variables and functions involved in the Gene object are as follows:

[0084] uniprot: a key variable in the construction of enzyme constraint models;

[0085] get_uniprot(): Get uniprot information;

[0086] set_uniprot(): set uniprot information;

[0087] The Reaction object adds the variable information required for constructing the kcat, MW and g0 multi-constraint model to define the relevant properties and functions of the reaction and manage the information related to the reaction;

[0088] Among them, the variables and functions involved in the Gene object are as follows:

[0089] kcat: catalytic constant of the enzyme;

[0090] MW: molecular weight of enzyme;

[0091] g0: standard free energy change;

[0092] get_kcat(): Get the kcat value;

[0093] set_kcat(): sets the kcat value;

[0094] Add metabolite concentration variable information to the Metabolite object to define the properties and functions of the metabolites and manage the metabolite information;

[0095] Among them, the variables and functions involved in the Metabolite object are as follows:

[0096] concentration_lb: lower limit of metabolite concentration;

[0097] concentration_ub: upper limit of metabolite concentration;

[0098] get_concentration_lb(): Get the lower limit of concentration;

[0099] set_concentration_lb(): Set the lower limit of concentration;

[0100] get_concentration_ub(): Get the upper limit of concentration;

[0101] set_concentration_ub(): Set the upper limit of concentration;

[0102] The Environment object is used to define environmental parameters, including pH, pMg, ionic strength, and temperature.

[0103] Among them, the variables and functions involved in the Environment object are as follows:

[0104] pH: pH value of the environment.

[0105] pMg: magnesium ion concentration in the environment.

[0106] ionic_strength: ionic strength of the environment;

[0107] temperature: ambient temperature;

[0108] get_pH():get pH value;

[0109] set_pH(): sets the pH value;

[0110] The multi-constraint model template construction unit is used to define and manage multi-constraint metabolic model templates, and use the basic model skeleton under the Pyomo framework to construct metabolic network models, enzyme constraint models, thermodynamic constraint models, and enzyme thermal constraint models;

[0111] As described above, the multi-constraint model template constructs and manages a multi-constraint metabolic network model, as follows:

[0112] S1.1. Create a Pyomo ConcreteModel object and define metabolite concentration variables, reaction flux variables, thermodynamic driving force variables, and binary variables.

[0113] importpyomo.environaspyo

[0114] frompyomo.environimport*

[0115] Concretemodel=ConcreteModel()

[0116] Concretemodel.metabolite=pyo.Var(metabolite_list, within=Reals)

[0117] Concretemodel.Df=pyo.Var(reaction_list, within=Reals)

[0118] Concretemodel.B=pyo.Var()

[0119] Concretemode1.reaction=pyo.Var(reaction_list,within=NonNegativeReals)

[0120] Concretemodel.z=pyo.Var(reaction_list, within=pyo.Binary);

[0121] S1.2. Add and manage metabolites, and set the upper and lower limits of metabolite concentrations through the function set_metabolite_constraints;

[0122] defset_metabolite_constraints(model, metabolites_lnC):

[0123] defset_metabolite(m, i):

[0124] return pyo.inequality(metabolites_lnC.loc[i, ′lnClb′], m.metabolite[i], metabolites_lnC.loc[i, ′lnCub′])

[0125] model.set_metabolite=pyo.Constraint(metabolite_list, rule=set_metabolite);

[0126] S1.3. Add and manage reactions: Set the thermodynamic driving force constraints of the reaction through the function set_reaction_thermodynamic_constraints;

[0127] defset_reaction_thermodynamic_constraints(model, reaction_g0, coef_matrix):

[0128] defset_Df(m,j):

[0129] returnm.Df[j]===-reaction_g0.loc[j,'g0']-2.579*sum(coef_matrix[i,j]*m.metabolite[i]for i in metabolite_list if(i,j)incoef_matrix.keys())

[0130] model.set_Df=pyo.Constraint(reaction_list, rule=set_Df)

[0131] S1.4. Set and manage constraints, including stoichiometric constraints, reaction flux upper and lower limits, medium substrate concentration constraints, enzyme total amount constraints, thermodynamic constraints, and integer problem constraints;

[0132] Stoichiometry constraints:

[0133] defset_stoichiometric_constraints(model, coef_matrix):

[0134] defset_stoi_matrix(m,i):

[0135] returnsum(coef_matrix[i,j]*m.reaction[j]for j in reaction_list if(i,j)in coef_matrix.keys())===0

[0136] model.set_stoi_matrix=pyo.Constraint(metabolite_list, rule=set_stoi_matrix);

[0137] Reaction flux upper and lower limits:

[0138] def set_reaction_bound_constraints(model, lb_list, ub_list):

[0139] def set_bound(m, j):

[0140] return pyo.inequality(lb_list[j], m.reaction[j], ub_list[j])

[0141] model.set_bound = pyo.Constraint(reaction_list, rule=set_bound);

[0142] Medium substrate concentration constraint:

[0143] def set_substrate_constraints(model, substrate):

[0144] def set_substrate_ini(m):

[0145] for substrate_name, substrate_value in

[0146] substrate.items():

[0147] return m.reaction[substrate_name] <= substrate_value

[0148] model.set_substrate_ini = pyo.Constraint(rule=set_substrate_ini);

[0149] Total enzyme amount constraint:

[0150] def set_enzyme_constraints(model, reaction_kcat_MW, E_total):

[0151] def set_enzyme_constraint(m):

[0152] return sum(m.reaction[j] / (reaction_kcat_MW.loc[j, 'kcat_MW']) for j in reaction_kcat_MW.index) <= E_total

[0153] mode1.set_enzyme_constraint=pyo.Constraint(rule=set_enzyme_constraint);

[0154] Thermodynamic constraints:

[0155] defset_thermodynamic_constraints(model, K_value, B_value):

[0156] defset_thermodynamics(m,j):

[0157] return(m.Df[j]+(1-mz[j])*K_value)>=B_value

[0158] model.set_thermodynamics=pyo.Constraint(reaction_list, rule=set_thermodynamics);

[0159] Integer problem constraints:

[0160] defset_integer_constraints(model, ub_list):

[0161] defset_integer(m, j):

[0162] returnm.reaction[j]<=mz[j]*ub_list[j]

[0163] model.set_integer=pyo.Constraint(reaction_list, rule=set_integer);

[0164] S1.5. Define a solve_model function to solve the model and return the result.

[0165] defsolve_model(model, solver='glpk'):

[0166] solver=pyo.SolverFactory(solver)

[0167] results = solver.solve(model)

[0168] return results;

[0169] As described above, the specific method for setting the upper and lower limits of metabolite concentration in S1.2 is:

[0170] Ln(C i，min )≤x i ≤ln(C i，max );

[0171] Among them, C i，min is the minimum concentration of metabolite i; C i，max is the maximum concentration of metabolite i;

[0172] As described above, the specific method for setting the thermodynamic driving force constraint of the reaction in S1.3 is:

[0173]

[0174] Where Dfi is the thermodynamic driving force of reaction i; is the Gibbs free energy change of reaction i under standard conditions; R is the gas constant; T is the absolute temperature; is the transpose of the stoichiometric coefficient vector of reaction i; x i is a vector of logarithmic values ​​of the metabolite concentrations of reaction i.

[0175] The specific method for setting the stoichiometric constraints in S1.4 is:

[0176]

[0177] The specific method for setting the upper and lower limits of the reaction flux is:

[0178] lb≤v j ≤ub;

[0179] The specific method for setting the culture medium substrate concentration constraint is:

[0180] v substrate ≤v medium ;

[0181] The specific method to set the enzyme total amount constraint is:

[0182]

[0183] The specific method for setting thermodynamic constraints is:

[0184] Df i +(1-z i )·K≥Df min ;

[0185] The specific method for setting integer problem constraints is:

[0186] v i ≤z i *v ub ;

[0187] Among them, v i is the flux of reaction i; z i is a binary variable of response i; v ub is the upper limit of the flux of reaction i.

[0188] Analysis module 2, wherein the Analysis module 2 provides a functional tool for analyzing a multi-constraint metabolic network model;

[0189] The function tools in the Analysis module 2 analyze the constructed multi-constraint metabolic network model, and the analysis includes flux balance analysis, metabolic flux analysis, sensitivity analysis, steady-state analysis and flux change analysis.

[0190] Ecmpy module 3, the Ecmpy module 3 is used to obtain and correct enzyme kinetic parameters and automatically construct an enzyme constraint model;

[0191] The Ecmpy module 3 includes a parameter acquisition unit, a parameter correction unit and a model automation construction unit;

[0192] The parameter acquisition unit obtains enzyme kinetic parameters through API calls and preset data sources, and integrates them into the multi-constraint metabolic network model; the sample code is as follows:

[0193]

[0194] The parameter correction unit corrects and adjusts the enzyme kinetic parameters from the parameter acquisition unit; the sample code is as follows:

[0195]

[0196] The model automatic construction unit integrates the enzyme kinetic parameters into the model's constraint conditions based on the obtained and corrected enzyme kinetic parameters, defines the total amount constraint of the enzyme, and automatically constructs the enzyme constraint metabolic network model. The sample code is as follows:

[0197]

[0198]

[0199] Io module 4, the lo module 4 is used to read, write and process multi-constraint metabolic network model data;

[0200] The lo module 4 includes a file reading and writing unit, a parameter saving unit and a JSON file processing unit;

[0201] The file reading and writing unit is used to save and read the data and parameters of the multi-constraint metabolic network model in JSON format through file I / O operations; the sample code is as follows:

[0202]

[0203] The parameter storage unit stores the multi-constraint metabolic network model parameters in JSON format, including metabolites, reactions and other related data. The sample code is as follows:

[0204]

[0205] The JSON file processing unit is used to process the reading and writing operations of the JSON file.

[0206] Plotting module 5, the Plotting module 5 is used to visualize multi-constraint metabolic network model data, pathway calculation results and model analysis results;

[0207] The Plotting module 5 includes a model data display unit, a pathway calculation display unit and a model analysis display unit;

[0208] The model data display unit displays the parameters of the multi-constraint metabolic network model, including metabolite concentrations and reaction fluxes, and uses a visualization library to generate graphics for parameter display; the sample code is as follows:

[0209]

[0210]

[0211] The pathway calculation display unit generates a metabolic pathway diagram using a graphics library to visualize the metabolic pathway calculation results; the sample code is as follows:

[0212]

[0213] The visualization of the model analysis results includes flux balance analysis, metabolic flux analysis, sensitivity analysis, steady-state analysis and flux change analysis results. The sample code is as follows:

[0214]

[0215] Tcmodel module 6, the Tcmodel module 6 is used to automatically obtain thermodynamic parameters and automatically build a thermodynamic constraint model;

[0216] The Tcmodel module 6 includes an automatic parameter acquisition unit and an automatic model construction unit;

[0217] The automatic parameter acquisition unit automatically acquires the thermodynamic parameters of the reactions and metabolites involved in the model by calling the database API and using a preset thermodynamic data file; the sample code is as follows:

[0218]

[0219] returnparameters, wherein the automatic model building unit automatically builds a metabolic network model with thermodynamic constraints based on the obtained thermodynamic parameters. The sample code is as follows:

[0220]

[0221] return mode l.

[0222] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A toolkit for building personalized multi-constraint metabolic network models based on modular thinking, featuring: include: A Core module (1), wherein the Core module (1) is used to define and manage core objects and structures in the toolkit and to construct an extended multi-constraint metabolic network model; Analysis module (2), the Analysis module (2) provides a functional tool for analyzing a multi-constraint metabolic network model; An Ecmpy module (3), wherein the Ecmpy module (3) is used to obtain and correct enzyme kinetic parameters and automatically construct an enzyme constraint model; lo module (4), the lo module (4) is used to read, write and process multi-constraint metabolic network model data; A plotting module (5), wherein the plotting module (5) is used to visualize multi-constraint metabolic network model data, pathway calculation results, and model analysis results; A Tcmodel module (6), wherein the Tcmodel module (6) is used to automatically obtain thermodynamic parameters and automatically construct a thermodynamic constraint model.

2. The toolkit for constructing a personalized multi-constraint metabolic network model based on modular thinking according to claim 1, characterized in that: The Core module (1) includes a Model object, a Gene object, a Reaction object, a Metabolite object, an Environment object and a multi-constraint model template construction unit; The Model object is used to define the structure and operation of the entire metabolic network model, manage all reactions, metabolites and genes contained in the model, and contains the variable information required for multi-constraint model analysis and its acquisition and modification functions; The Gene object adds uniprot, a key enzyme constraint model construction variable, to define the relevant properties and functions of the gene and manage gene information; The Reaction object adds the variable information required for constructing the kcat, MW and g0 multi-constraint model to define the relevant properties and functions of the reaction and manage the information related to the reaction; Add metabolite concentration variable information to the Metabolite object to define the properties and functions of the metabolites and manage the metabolite information; The Environment object is used to define environmental parameters, including pH, pMg, ionic strength, and temperature.

3. The toolkit for constructing a personalized multi-constraint metabolic network model based on modular thinking according to claim 2, characterized in that: The multi-constraint model template construction unit is used to define and manage multi-constraint metabolic model templates, and use the basic model skeleton under the Pyomo framework to construct metabolic network models, enzyme constraint models, thermodynamic constraint models, and enzyme thermal constraint models; As described above, the multi-constraint model template constructs and manages a multi-constraint metabolic network model, as follows: S1.

1. Create a Pyomo ConcreteModel object and define metabolite concentration variables, reaction flux variables, thermodynamic driving force variables, and binary variables. S1.

2. Add and manage metabolites, and set the upper and lower limits of metabolite concentrations through the function set_metabolite_constraints; S1.

3. Add and manage reactions: Set the thermodynamic driving force constraints of the reaction through the function set_reaction_thermodynamic_constraints; S1.

4. Set and manage constraints, including stoichiometric constraints, reaction flux upper and lower limits, culture medium substrate concentration constraints, enzyme total amount constraints, thermodynamic constraints, and integer problem constraints.

4. The toolkit for constructing a personalized multi-constraint metabolic network model based on modular thinking according to claim 3, characterized in that: As described above, the specific method for setting the upper and lower limits of metabolite concentration in S1.2 is: ln(C i，min )≤x i ≤ln(C i，max ); Among them, C i，min is the minimum concentration of metabolite i; C i,max is the maximum concentration of metabolite i; As described above, the specific method for setting the thermodynamic driving force constraint of the reaction in S1.3 is: Among them, Df i is the thermodynamic driving force of reaction i; is the Gibbs free energy change of reaction i under standard conditions; R is the gas constant; T is the absolute temperature; is the transpose of the stoichiometric coefficient vector of reaction i; x i is a vector of logarithmic values ​​of the metabolite concentrations of reaction i.

5. The toolkit for constructing a personalized multi-constraint metabolic network model based on modular thinking according to claim 3, characterized in that: The specific method for setting the stoichiometric constraints in S1.4 is: The specific method for setting the upper and lower limits of the reaction flux is: lb≤v j ≤ub; The specific method for setting the culture medium substrate concentration constraint is: in substrate ≤in medium ; The specific method to set the enzyme total amount constraint is: The specific method for setting thermodynamic constraints is: Df i +(1-z i )·K≥Df min ; The specific method for setting integer problem constraints is: v i ≤z i *v ub ; Among them, v i is the flux of reaction i; z i is a binary variable of response i; v ub is the upper limit of the flux of reaction i.

6. The toolkit for constructing a personalized multi-constraint metabolic network model based on modular thinking according to claim 1, characterized in that: The function tools in the Analysis module (2) analyze the constructed multi-constraint metabolic network model, and the analysis includes flux balance analysis, metabolic flux analysis, sensitivity analysis, steady-state analysis and flux change analysis.

7. The toolkit for constructing a personalized multi-constraint metabolic network model based on modular thinking according to claim 1, characterized in that: The Ecmpy module (3) comprises a parameter acquisition unit, a parameter correction unit and a model automatic construction unit; The parameter acquisition unit acquires enzyme kinetic parameters through API call and preset data source, and integrates them into the multi-constraint metabolic network model; Wherein, the parameter correction unit corrects and adjusts the enzyme kinetic parameters from the parameter acquisition unit; Among them, the model automation construction unit integrates the enzyme kinetic parameters into the constraints of the model based on the acquired and corrected enzyme kinetic parameters, defines the total amount constraints of the enzyme, and automatically constructs the enzyme constraint metabolic network model.

8. The toolkit for constructing a personalized multi-constraint metabolic network model based on modular thinking according to claim 1, characterized in that: The lo module (4) includes a file reading and writing unit, a parameter storage unit and a JSON file processing unit; Wherein, the file reading and writing unit is used to save and read the data and parameters of the multi-constraint metabolic network model in JSON format through file I / O operations; Wherein, the parameter storage unit stores the multi-constraint metabolic network model parameters in JSON format; The JSON file processing unit is used to process the reading and writing operations of the JSON file.

9. The toolkit for constructing a personalized multi-constraint metabolic network model based on modular thinking according to claim 1, characterized in that: The Plotting module (5) comprises a model data display unit, a pathway calculation display unit and a model analysis display unit; Among them, the model data display unit displays the parameters of the multi-constraint metabolic network model, including metabolite concentrations and reaction fluxes, and uses the visualization library to generate graphics for parameter display; Among them, the pathway calculation display unit uses the graphics library to generate a metabolic pathway diagram and visualize the metabolic pathway calculation results; Among them, the model analysis results are visualized, including flux balance analysis, metabolic flux analysis, sensitivity analysis, steady-state analysis and flux change analysis results.

10. The toolkit for constructing a personalized multi-constraint metabolic network model based on modular thinking according to claim 1, characterized in that: The Tcmodel module (6) includes an automatic parameter acquisition unit and an automatic model construction unit; Wherein, the automatic parameter acquisition unit automatically acquires the thermodynamic parameters of the reactions and metabolites involved in the model by calling the database API and using a preset thermodynamic data file; Wherein, the automatic model building unit automatically builds a metabolic network model with thermodynamic constraints based on the acquired thermodynamic parameters.

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