Optimization method and device for microbial fermentation process

Abstract

The invention relates to the technical field of microbial fermentation, in particular to a method and a device for optimizing a microbial fermentation process. The optimization method of the microbial fermentation process comprises the following steps: constructing a microbial growth kinetic equation and a product generation kinetic equation in the F protein fermentation process before RSV fusion, wherein the product generation kinetic equation is used for representing a function relationship between a product and the maximum specific growth rate of cells; on the basis of platform process fermentation conditions and by changing a single experimental parameter, microorganism growth data and product generation data under different fermentation conditions are obtained; performing data analysis according to the microorganism growth data and the product generation data under different fermentation conditions, the microorganism growth kinetic equation and the product generation kinetic equation to obtain a function relationship between a product and a single experimental parameter; and then predicting the generation trend of the F protein before RSV fusion and optimizing the fermentation conditions. Therefore, on the premise that a large number of experiments are not needed, the microbial fermentation process is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of microbial fermentation, and in particular to a method and device for optimizing a microbial fermentation process. Background Art

[0002] At present, the fermentation of microbial products is increasingly widely used, such as vaccines in medicine. In order to further increase the yield of microbial fermentation products, it is usually necessary to repeatedly optimize the microbial fermentation process.

[0003] However, the inventors found that in traditional processes, optimization experiments are cumbersome, labor-intensive, and it is difficult to find a direct correlation between fermentation conditions and products. Summary of the invention

[0004] To this end, the purpose of the present invention is to provide a method and device for optimizing a microbial fermentation process, thereby establishing a direct connection between fermentation conditions and product generation, and accurately grasping the optimal values ​​of various fermentation parameters.

[0005] According to a first aspect of the present invention, there is provided a method for optimizing a microbial fermentation process, comprising the following steps:

[0006] A microbial growth kinetic equation for the fermentation process of RSV prefusion F protein is constructed, and the microbial growth kinetic equation is used to characterize the functional relationship between microbial growth and the maximum specific growth rate of the cell; a product generation kinetic equation for the fermentation process of RSV prefusion F protein is constructed, and the product generation kinetic equation is used to characterize the functional relationship between the product and the maximum specific growth rate of the cell; based on the platform process fermentation conditions and changing a single experimental parameter, microbial growth data and product generation data under different fermentation conditions are obtained; a single experimental parameter includes but is not limited to any one of temperature, dissolved oxygen, pH, inoculation amount and rotation speed; data analysis is performed based on the microbial growth data and product generation data under different fermentation conditions, the microbial growth kinetic equation and the product generation kinetic equation to obtain the functional relationship between the product and a single experimental parameter; based on the functional relationship between the product and a single experimental parameter, the generation trend of RSV prefusion F protein is predicted and the fermentation conditions are optimized.

[0007] The above aspects and any possible implementations further provide an implementation for constructing a microbial growth kinetic equation during the RSV pre-fusion F protein fermentation process, comprising:

[0008] Construct the microbial growth kinetics equation based on the Logistic equation:

[0009]

[0010] Where X is the density of viable microbial cells; t is the fermentation time; is the maximum specific growth rate of the cells; is the maximum viable cell density of microorganisms; is the initial viable cell density of the microorganism.

[0011] The above aspects and any possible implementations further provide an implementation for constructing a product generation kinetic equation during the RSV pre-fusion F protein fermentation process, comprising:

[0012]

[0013] Wherein, P is the amount of product generated; is the initial amount of the product; α is the synthesis coefficient of the product related to the growth of microorganisms; β is the synthesis coefficient of the product related to the amount of microbial cells.

[0014] In the above aspects and any possible implementation, an implementation is further provided, wherein data analysis is performed based on microbial growth data and product generation data under different fermentation conditions, microbial growth kinetic equations, and product generation kinetic equations to obtain a functional relationship between the product and a single experimental parameter, including:

[0015] The microbial growth kinetic equation was fitted based on the microbial growth data and product generation data under different fermentation conditions to obtain the maximum specific growth rate of the cells under different values ​​of a single experimental parameter; data fitting was performed based on different values ​​of a single experimental parameter and its corresponding maximum specific growth rate of the cells to obtain the function between the maximum specific growth rate of the cells and the single experimental parameter; the function between the maximum specific growth rate of the cells and the single experimental parameter was substituted into the product generation kinetic equation to obtain the functional relationship between the product and the single experimental parameter.

[0016] The above aspects and any possible implementations further provide an implementation method, which predicts the production trend of RSV prefusion F protein and optimizes the fermentation conditions based on the functional relationship between the product and a single experimental parameter, including:

[0017] According to the functional relationship between the product and the single experimental parameter, the maximum predicted value of the product and the optimal single experimental parameter corresponding to the maximum predicted value of the product are obtained; the platform process fermentation conditions are adjusted according to the optimal single experimental parameter.

[0018] The above aspects and any possible implementations further provide an implementation, which predicts the production trend of RSV prefusion F protein and optimizes the fermentation conditions based on the functional relationship between the product and a single experimental parameter, and also includes:

[0019] The actual measurement of the product is carried out under the fermentation conditions including the optimal single experimental parameter to obtain the actual measurement value of the product; the actual measurement value of the product is compared with the maximum predicted value of the product to obtain a comparison result; and the validity of the function between the product and the single experimental parameter is verified according to the comparison result.

[0020] In the above aspects and any possible implementation, an implementation is further provided, which verifies the functional validity between the product and the single experimental parameter according to the comparison result, including:

[0021] If the comparison result is less than the preset threshold, the function between the product and the single experimental parameter is determined to be valid and the optimal single experimental parameter is used as the final optimal single experimental parameter; otherwise, data fitting and analysis are performed again.

[0022] The above aspects and any possible implementations further provide an implementation, which predicts the production trend of RSV prefusion F protein and optimizes the fermentation conditions based on the functional relationship between the product and a single experimental parameter, and also includes:

[0023] The final optimal single experimental parameters determined under different single experimental parameters are integrated to obtain optimized fermentation parameters; microbial fermentation is carried out according to the optimized fermentation parameters.

[0024] As for the above aspects and any possible implementation, there is further provided an implementation, wherein the microorganism is a eukaryotic or prokaryotic cell.

[0025] According to a second aspect of the present invention, there is provided a device for optimizing a microbial fermentation process, comprising:

[0026] The first construction unit is used to construct a microbial growth kinetics equation during the RSV pre-fusion F protein fermentation process, wherein the microbial growth kinetics equation is used to characterize the functional relationship between the growth of the microorganism and the maximum specific growth rate of the cell;

[0027] The second construction unit is used to construct a product generation kinetic equation during the RSV pre-fusion F protein fermentation process, wherein the product generation kinetic equation is used to characterize the functional relationship between the product and the maximum specific growth rate of the cell;

[0028] A data acquisition unit, used to obtain microbial growth data and product generation data under different fermentation conditions based on platform process fermentation conditions and by changing a single experimental parameter; the single experimental parameter is not limited to any one of temperature, dissolved oxygen, pH, inoculation amount and rotation speed;

[0029] A data analysis unit, configured to perform data analysis based on the microbial growth data and product generation data under the different fermentation conditions, the microbial growth kinetic equation and the product generation kinetic equation, to obtain a functional relationship between the product and the single experimental parameter;

[0030] A parameter optimization unit is used to predict the production trend of RSV prefusion F protein and optimize the fermentation conditions according to the functional relationship between the product and the single experimental parameter.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The functional relationship between the product constructed by the present invention and a single experimental parameter can characterize the direct connection between microbial growth and product generation. Therefore, the functional relationship between the product constructed by the present invention and a single experimental parameter can intuitively and practically characterize the fermentation process.

[0033] 2. The microbial fermentation process optimization method provided by the present invention can optimize the process by adjusting parameters, thereby overcoming the shortcomings of traditional process optimization that requires cumbersome experimental design and large workload.

[0034] 3. The optimization method of the microbial fermentation process of the present invention is used to adjust the parameters of the optimization process, which can effectively solve the problems faced by the fermentation process such as unstable product quality and difficult to effectively monitor and control production, and is of great significance to improving fermentation efficiency and raw material utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present invention and do not constitute a limitation of the present invention. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0036] Figure 1 Schematic diagram of cell growth status under platform process conditions in an embodiment of the present invention;

[0037] Figure 2 Schematic diagram of cell sugar consumption and target protein synthesis under platform process conditions in an embodiment of the present invention;

[0038] Figure 3 Schematic diagram of cell growth dynamics under platform process conditions in an embodiment of the present invention;

[0039] Figure 4 Schematic diagram of protein production kinetics under platform process conditions in an embodiment of the present invention;

[0040] Figure 5Schematic diagram of the relationship between the maximum specific growth rate and temperature under different cell culture conditions in the embodiments of the present invention;

[0041] Figure 6 This is a schematic diagram of the relationship between the first temperature and the expression amount in an embodiment of the present invention;

[0042] Figure 7 Schematic diagram of the relationship between the maximum specific growth rate and dissolved oxygen under different cell culture conditions in the embodiments of the present invention;

[0043] Figure 8 This is a schematic diagram of the relationship between dissolved oxygen and expression level in an embodiment of the present invention;

[0044] Fig. 9 Schematic diagram of the relationship between the maximum specific growth rate and pH under different cell culture conditions in the embodiments of the present invention;

[0045] Fig.10 This is a schematic diagram of the relationship between the second temperature and the expression amount in an embodiment of the present invention;

[0046] Fig.11 Schematic diagram of the relationship between the third temperature and the expression level in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0048] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0049] At present, the fermentation of microbial products is increasingly widely used, such as vaccines in medicine. In order to further increase the yield of microbial fermentation products, it is usually necessary to repeatedly optimize the microbial fermentation process.

[0050] However, the inventors found that in traditional processes, optimization experiments are cumbersome, labor-intensive, and it is difficult to find a direct correlation between fermentation conditions and products.

[0051] To solve the above problems, the present technical solution establishes a direct connection between fermentation conditions and product generation, accurately grasps the optimal value of each fermentation parameter, and thus realizes the optimization of microbial fermentation process. Detailed description is given below.

[0052] The present invention provides a method for optimizing a microbial fermentation process, comprising the following steps:

[0053] Step S101: constructing a microbial growth kinetic equation during the RSV pre-fusion F protein fermentation process, wherein the microbial growth kinetic equation is used to characterize the functional relationship between the growth of the microorganism and the maximum specific growth rate of the cell.

[0054] It should be noted that, in the present invention, the microorganism is a eukaryotic or prokaryotic cell.

[0055] In step S101, a microbial growth kinetic equation during RSV pre-fusion F protein fermentation is constructed, including:

[0056] Construct the microbial growth kinetics equation based on the Logistic equation:

[0057]

[0058] Where X is the density of viable microbial cells; t is the fermentation time; is the maximum specific growth rate of the cells; is the maximum viable cell density of microorganisms; is the initial viable cell density of the microorganism.

[0059] Among them, the parameters are obtained by: using the platform process to ferment and produce RSV pre-fusion F protein, and using a cell counter, a biochemical analyzer, a high performance liquid analyzer, etc. to analyze the cell growth and metabolism during the fermentation process.

[0060] In one embodiment, the fermentation production of RSV pre-fusion F protein includes the following steps: using a 5L cell fermentor to ferment RSV pre-fusion F protein. Culture medium and feed medium: StarCHO, StarCHO Feed, CDFS36; culture conditions: target inoculation amount is 0.5×10^6 cells / ml, temperature is 37.0℃, pH is 7.00, rotation speed is 120rpm, dissolved oxygen is 40%, culture volume is 3L, culture time is 10d; process control: StarCHO Feed feed medium is 4%, 4%, 5%, 4% (a total of 17%) and CDFS36 feed medium is 0.4%, 0.4%, 0.5%, 0.4% (a total of 1.7%) on the 2nd, 4th, 6th, and 8th days of fermentation respectively; sugar concentration is controlled at 2~10g / L, and supplemented to 6g / L if it is lower than 4g / L.

[0061] The RSV pre-fusion F protein was fermented using StarCHO basal medium + StarCHO Feed / CDFS36 supplement medium using a platform process. The fermentation cycle was 8 days. Figure 1 and Figure 2 As shown. During the fermentation process, the live cell density of CHO cells gradually increased, showing an "S"-shaped trend, with the highest live cell density of 17.2*10^6cells / ml. The cell viability was ≥98% throughout the fermentation process, and the cells were in good condition. The glucose concentration gradually decreased while the protein content gradually increased. The protein concentration and cell growth trends were similar, and the protein expression was growth-coupled.

[0062] Cell growth kinetics of RSV prefusion F protein fermentation production under platform process conditions Figure 3 The growth kinetics of RSV prefusion F protein fermentation were analyzed using the Logistic equation:

[0063]

[0064] Integrating the equation yields:

[0065]

[0066] Where: X is the viable cell density of CHO cells (×10^6 cells / ml); t is the fermentation time (d); μm is the maximum specific growth rate of cells (h -1 ); is the maximum viable cell density (×10^6cells / ml); is the initial viable cell density (×10^6 cells / ml).

[0067] The fitting results are: , R^2=0.99568

[0068] This kinetic equation can characterize the cell growth state during protein expression. According to this kinetic equation, the maximum viable cell density can reach 17.61×10^6 cells / ml, and the maximum specific growth rate is about 1.17 h -1 .

[0069] Step S102: constructing a product generation kinetic equation during the RSV pre-fusion F protein fermentation process, wherein the product generation kinetic equation is used to characterize the functional relationship between the product and the maximum specific growth rate of the cell.

[0070] In step S102, a product generation kinetic equation during the RSV pre-fusion F protein fermentation process is constructed, including:

[0071]

[0072] Wherein, P is the amount of product generated; is the initial amount of the product; α is the synthesis coefficient of the product related to the growth of microorganisms; β is the synthesis coefficient of the product related to the amount of microbial cells.

[0073] Figure 4 The schematic diagram of protein production kinetics under platform process conditions in the embodiment of the present invention is shown in Figure 1. The Luedeking-Piret equation was used to analyze the protein production kinetics.

[0074]

[0075] Integrating the equation yields:

[0076] .

[0077] P=0+0.102×(17.584 / [1+80.194×e -1.063t ]-0.217)-0.006×16.542×ln[1-0.012×(1-e 1.063t )]; R^2=0.99996.

[0078] P 0 +d1•(a / (1+b•exp(-k•t))-a / (b+1))+d2• ((a / k) •ln((b+exp(k•t)) / (b+1)))

[0079] Where: P is the amount of product generated (g / L); is the starting amount of the product (g / L).

[0080] In this equation =0; α is the synthesis coefficient of the product and the growth of microorganisms; in this equation, α=0.102; β is the synthesis coefficient of the product and the amount of microorganisms. In this equation, β=-0.003.

[0081] Step S103: Based on the platform process fermentation conditions and changing a single experimental parameter, the microbial growth data and product generation data under different fermentation conditions are obtained; the single experimental parameter includes but is not limited to any one of temperature, dissolved oxygen, pH, inoculation amount and rotation speed.

[0082] Step S104: performing data analysis based on the microbial growth data and product generation data under different fermentation conditions, the microbial growth kinetic equation and the product generation kinetic equation to obtain a functional relationship between the product and a single experimental parameter.

[0083] In step S104, data analysis is performed based on the microbial growth data and product generation data under different fermentation conditions, the microbial growth kinetic equation and the product generation kinetic equation to obtain a functional relationship between the product and a single experimental parameter, including:

[0084] Step S141: fitting the microbial growth kinetics equation according to the microbial growth data and product generation data under different fermentation conditions to obtain the maximum specific growth rate of the cells under different values ​​of a single experimental parameter.

[0085] Step S142: performing data fitting based on different values ​​of a single experimental parameter and its corresponding maximum specific growth rate of the cell, and obtaining a function between the maximum specific growth rate of the cell and the single experimental parameter.

[0086] Step S143: Substitute the function between the maximum specific growth rate of the cells and the single experimental parameter into the product generation kinetic equation to obtain the functional relationship between the product and the single experimental parameter.

[0087] In some embodiments, taking temperature as an example, data analysis is performed based on microbial growth data and product generation data under different fermentation conditions, microbial growth kinetic equations and product generation kinetic equations to obtain a functional relationship between product and temperature, including the following steps: Figure 5 As shown, the relationship between the maximum specific growth rate and temperature under different cell culture conditions was established: the temperature and maximum specific growth rate data were collected, as shown in Table 1.

[0088] Table 1 Temperature and maximum specific growth rate data Culture temperature (℃) <![CDATA[Maximum specific growth rate (h -1 ).]]> 33 0.832 35 1.054 37 1.125 39 0.923 The quadratic equation was used to establish the specific maximum growth rate (y = ) and temperature (x=T): = -0.0284 × T 2 +2.0587×T – 36.206; R² = 0.9991.

[0089] Will Substitution , and let t = 8 (cell culture 8 days) to obtain the equation:

[0090] P=0+0.102×(17.584 / [1+80.194×e-(-0.0284×T^2+2.0587×T-36.206) ×8]-0.217)-0.006×16.542×ln[1-0.012×(1-e(-0.0284×T^2+2.0587×T-36.206) ×8)].

[0091] like Figure 6 As shown in the figure, according to the equation, when the culture temperature T=36.5℃, the protein expression level is the highest at 1.381g / L. When the culture temperature is controlled at 36.5℃, the final expression level is 1.379g / L, which is not significantly different from the predicted value.

[0092] Step S105: Based on the functional relationship between the product and the single experimental parameter, the production trend of RSV prefusion F protein is predicted and the fermentation conditions are optimized.

[0093] In step S105, based on the functional relationship between the product and the single experimental parameter, the generation trend of RSV prefusion F protein is predicted and the fermentation conditions are optimized, including:

[0094] According to the functional relationship between the product and the single experimental parameter, the maximum predicted value of the product and the optimal single experimental parameter corresponding to the maximum predicted value of the product are obtained; the platform process fermentation conditions are adjusted according to the optimal single experimental parameter.

[0095] In some embodiments, the dissolved oxygen concentration and Ph can be adjusted according to the optimal temperature value, including the following steps: Figure 7 As shown, the relationship between the maximum specific growth rate and dissolved oxygen under different cell culture conditions was established: the dissolved oxygen and maximum specific growth rate data were collected, as shown in Table 2.

[0096] Table 2 Dissolved oxygen and maximum specific growth rate data Dissolved oxygen control (%) <![CDATA[Maximum specific growth rate (h -1 ).]]> 20% 1.0768 40% 1.1576 60% 1.1392 70% 1.0896 The quadratic equation was used to establish the specific maximum growth rate (y = ) and dissolved oxygen (x=O): = -1.2706 × O 2 +1.1705×O+0.8933; R² = 0.9996.

[0097] Will Substitution , and let t = 8 (cell culture 8 days) to obtain the equation: P=0+0.102×(17.584 / [1+80.194×e-(-1.2706×O^2+1.1705×O+0.8933) ×8]-0.217)-0.006×16.542×ln[1-0.012×(1-e(-1.2706×O^2+1.1705×O+0.8933) ×8)].

[0098] like Figure 8 As shown in the figure, according to the equation, when the dissolved oxygen concentration O=45%, the protein expression level is the highest at 1.477 g / L. When the dissolved oxygen concentration is controlled to 45%, the final expression level is 1.472 g / L, which is not significantly different from the predicted value.

[0099] In some embodiments, Fig. 9 As shown, the relationship between the maximum specific growth rate and pH under different cell culture conditions was established: the pH and maximum specific growth rate data were collected, as shown in Table 3.

[0100] Table 3 pH and maximum specific growth rate data.

[0101] pH <![CDATA[Maximum specific growth rate (h -1 ).]]> 6.25 1.1934 6.75 1.3113 7.25 1.3086 7.50 1.2681 7.75 1.1889 The quadratic equation was used to establish the specific maximum growth rate (y = ) and dissolved oxygen (x=p): =-0.2383×p 2 +3.3347×p-10.338; R² = 0.9991.

[0102] Will Substitution , and let t = 8 (cell culture 8 days) to obtain the equation: P=0+0.102×(17.584 / [1+80.194×e-(-0.2383×p^2+3.3347×p-10.338) ×8]-0.217)-0.006×16.542×ln[1-0.012×(1-e(-0.2383×p^2+3.3347×p-10.338) ×8)]; like Fig.10 As shown in the figure, according to the equation, when the dissolved oxygen concentration pH = 7.00, the protein expression level is the highest at 1.597 g / L. When the cell culture pH is controlled at 7.00, the final expression level is 1.595 g / L, which is not significantly different from the predicted value.

[0103] In some embodiments, a kinetic function of product formation versus temperature is constructed and the maximum product formation is found.

[0104] Will = -0.0106t 2 + 0.7706t - 12.536 (R² = 0.9957) to be entered into the product formation kinetic equation.

[0105] like Fig.11 As shown, according to the equation, when the temperature t=36.4, the expression level can reach a maximum of 1.605g / L.

[0106] In step S105, according to the functional relationship between the product and the single experimental parameter, the generation trend of RSV prefusion F protein is predicted and the fermentation conditions are optimized, which also includes:

[0107] Step S151: Perform actual measurement of the product under fermentation conditions including the optimal single experimental parameters to obtain an actual measurement value of the product.

[0108] Step S152: Compare the actual measured value of the product with the maximum predicted value of the product to obtain a comparison result.

[0109] Step S153: Verify the functional validity between the product and the single experimental parameter based on the comparison result.

[0110] In step S153, the functional validity between the product and the single experimental parameter is verified according to the comparison result, including:

[0111] If the comparison result is less than the preset threshold, the function between the product and the single experimental parameter is determined to be valid and the optimal single experimental parameter is used as the final optimal single experimental parameter; otherwise, data fitting and analysis are performed again.

[0112] In step S105, according to the functional relationship between the product and the single experimental parameter, the generation trend of RSV prefusion F protein is predicted and the fermentation conditions are optimized, which also includes:

[0113] The final optimal single experimental parameters determined under different single experimental parameters are integrated to obtain optimized fermentation parameters; microbial fermentation is carried out according to the optimized fermentation parameters.

[0114] It should be noted that the detection method involved in the embodiments of the present invention is as follows:

[0115] Detection of live cell density and cell viability: Take about 1 ml of cell culture medium and mix it evenly with 0.4% trypan blue solution in a ratio of 1:1, and then detect it on a cell counter.

[0116] Glucose concentration detection: Take about 1 ml of cell culture medium, centrifuge at 4000g for 5 minutes, and take the supernatant for detection on a biochemical analyzer.

[0117] RSV pre-fusion F protein detection: Sample treatment: Take the supernatant and dilute it 2 times with ultrapure water, mix well and filter it with a 0.22μm filter membrane; Mobile phase A (0.1% trifluoroacetic acid water): Measure 1000ml ultrapure water, add 1ml trifluoroacetic acid, mix well and then ultrasonically degas; Mobile phase B (0.1% trifluoroacetic acid acetonitrile): Measure 1000ml acetonitrile, add 1ml trifluoroacetic acid, mix well and then ultrasonically degas. Chromatographic column: BioResolve RP mAb 4.6×100mm2.7μm; Chromatographic conditions: Mobile phase A: mobile phase B = 1:1, flow rate of 0.5ml / min, column temperature of 55℃, wavelength of 215nm, sample plate temperature of 5℃, injection volume of 50μl; Result calculation: Calculate the sample protein concentration by peak area according to the external standard method.

[0118] The present application also provides an optimization device for a microbial fermentation process, comprising:

[0119] The first construction unit is used to construct a microbial growth kinetics equation during the RSV pre-fusion F protein fermentation process, wherein the microbial growth kinetics equation is used to characterize the functional relationship between the growth of the microorganism and the maximum specific growth rate of the cell;

[0120] The second construction unit is used to construct a product generation kinetic equation during the RSV pre-fusion F protein fermentation process, wherein the product generation kinetic equation is used to characterize the functional relationship between the product and the maximum specific growth rate of the cell;

[0121] A data acquisition unit, used to obtain microbial growth data and product generation data under different fermentation conditions based on platform process fermentation conditions and by changing a single experimental parameter; the single experimental parameter is not limited to any one of temperature, dissolved oxygen, pH, inoculation amount and rotation speed;

[0122] A data analysis unit, configured to perform data analysis based on the microbial growth data and product generation data under the different fermentation conditions, the microbial growth kinetic equation and the product generation kinetic equation, to obtain a functional relationship between the product and the single experimental parameter;

[0123] A parameter optimization unit is used to predict the production trend of RSV prefusion F protein and optimize the fermentation conditions according to the functional relationship between the product and the single experimental parameter.

[0124] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0126] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0127] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing a microbial fermentation process, characterized in that: The steps include: Constructing a microbial growth kinetic equation during RSV prefusion F protein fermentation, wherein the microbial growth kinetic equation is used to characterize the functional relationship between microbial growth and the maximum specific growth rate of the cell; Constructing a product generation kinetic equation during the RSV prefusion F protein fermentation process, wherein the product generation kinetic equation is used to characterize the functional relationship between the product and the maximum specific growth rate of the cell; Based on the platform process fermentation conditions and changing a single experimental parameter, obtaining microbial growth data and product generation data under different fermentation conditions; the single experimental parameter is not limited to any one of temperature, dissolved oxygen, pH, inoculation amount and rotation speed; Performing data analysis based on the microbial growth data and product generation data under the different fermentation conditions, the microbial growth kinetic equation and the product generation kinetic equation to obtain a functional relationship between the product and the single experimental parameter; Based on the functional relationship between the product and the single experimental parameter, the production trend of RSV prefusion F protein is predicted and the fermentation conditions are optimized.

2. The method according to claim 1, characterized in that The method for constructing a microbial growth kinetics equation during the RSV pre-fusion F protein fermentation process includes: Construct the microbial growth kinetics equation based on the Logistic equation: ; Wherein, X is the density of viable microorganism cells; t is the fermentation time; is the maximum specific growth rate of cells; is the maximum viable cell density of microorganisms; is the initial viable cell density of the microorganism.

3. The method according to claim 2, characterized in that The method of constructing a product generation kinetic equation during the RSV pre-fusion F protein fermentation process comprises: ; Wherein, P is the amount of product generated; is the initial amount of the product; α is the synthesis coefficient of the product related to the growth of microorganisms; β is the synthesis coefficient of the product related to the amount of microbial cells.

4. The method according to claim 3, characterized in that The data analysis is performed based on the microbial growth data and product generation data under the different fermentation conditions, the microbial growth kinetic equation and the product generation kinetic equation to obtain the functional relationship between the product and the single experimental parameter, including: According to the microbial growth data and product generation data under the different fermentation conditions, the microbial growth kinetic equation is fitted to obtain the maximum specific growth rate of the cell under different values ​​of the single experimental parameter; Performing data fitting based on different values ​​of the single experimental parameter and the corresponding maximum specific growth rate of the cell to obtain a function between the maximum specific growth rate of the cell and the single experimental parameter; Substituting the function between the maximum specific growth rate of the cells and the single experimental parameter into the product formation kinetic equation, the functional relationship between the product and the single experimental parameter is obtained.

5. The method according to claim 4, characterized in that The method predicts the generation trend of RSV prefusion F protein and optimizes the fermentation conditions according to the functional relationship between the product and the single experimental parameter, comprising: The step of obtaining the maximum predicted value of the product and the optimal single experimental parameter corresponding to the maximum predicted value of the product according to the functional relationship between the product and the single experimental parameter; The platform process fermentation conditions are adjusted according to the optimal single experiment parameters.

6. The method according to claim 5, characterized in that The method of predicting the generation trend of RSV prefusion F protein and optimizing the fermentation conditions according to the functional relationship between the product and the single experimental parameter also includes: Perform actual measurement of the product under fermentation conditions including the optimal single experimental parameters to obtain an actual measurement value of the product; Comparing the actual measured value of the product with the maximum predicted value of the product to obtain a comparison result; The functional validity between the product and the single experimental parameter is verified based on the comparison result.

7. The method according to claim 6, characterized in that The verifying the functional validity between the product and the single experimental parameter according to the comparison result comprises: If the comparison result is less than a preset threshold, the function between the product and the single experimental parameter is determined to be valid and the optimal single experimental parameter is used as the final optimal single experimental parameter; otherwise, data fitting and analysis are performed again.

8. The method according to claim 7, characterized in that The method of predicting the generation trend of RSV prefusion F protein and optimizing the fermentation conditions according to the functional relationship between the product and the single experimental parameter also includes: The final optimal single experimental parameters determined under different single experimental parameters are integrated to obtain the optimized fermentation parameters; The microbial fermentation is carried out according to the optimized fermentation parameters.

9. The method according to claim 5, characterized in that The microorganism is a eukaryotic or prokaryotic cell.

10. An optimization device for a microbial fermentation process, characterized in that: include: The first construction unit is used to construct a microbial growth kinetics equation during the RSV pre-fusion F protein fermentation process, wherein the microbial growth kinetics equation is used to characterize the functional relationship between the growth of the microorganism and the maximum specific growth rate of the cell; The second construction unit is used to construct a product generation kinetic equation during the RSV pre-fusion F protein fermentation process, wherein the product generation kinetic equation is used to characterize the functional relationship between the product and the maximum specific growth rate of the cell; A data acquisition unit, used to obtain microbial growth data and product generation data under different fermentation conditions based on platform process fermentation conditions and by changing a single experimental parameter; the single experimental parameter is not limited to any one of temperature, dissolved oxygen, pH, inoculation amount and rotation speed; A data analysis unit, configured to perform data analysis based on the microbial growth data and product generation data under the different fermentation conditions, the microbial growth kinetic equation and the product generation kinetic equation, to obtain a functional relationship between the product and the single experimental parameter; A parameter optimization unit is used to predict the production trend of RSV prefusion F protein and optimize the fermentation conditions according to the functional relationship between the product and the single experimental parameter.

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