Fuzzy intelligent control method and device for pichia pastoris fermentation process

Abstract

The invention discloses a fuzzy intelligent control method and device for a pichia pastoris fermentation process. The fuzzy intelligent control method comprises the following steps: (1) integrating an online sensor in a bioreactor and monitoring state parameters of fermentation liquor in real time; (2) inputting the monitored parameters into a central control system, and processing the parameters through a fuzzy intelligent control algorithm; and (3) according to an output instruction of the fuzzy intelligent control algorithm, cooperatively regulating and controlling fermentation operation parameters in real time. According to the method disclosed by the invention, a fuzzy intelligent control algorithm is developed by monitoring dissolved oxygen, relative concentration of dissolved carbon dioxide, methanol concentration and pH in fermentation liquor in the fermentation process in real time, so that the fermentation process is accurately regulated and controlled by multi-parameter coordination, and the method is applied to process regulation and control of pichia pastoris fermentation expression recombinant protein. The expression quantity and the production efficiency of the recombinant albumin can be remarkably improved, the production period is shortened, and the method is suitable for industrial large-scale production.

Description

Technical Field

[0001] This invention belongs to the field of bioengineering technology and relates to a fuzzy intelligent control method and device for the fermentation process of Pichia pastoris. Background Technology

[0002] Pichia pastoris expression system is a eukaryotic expression system and one of the most successful exogenous protein expression systems to date. Compared with other existing expression systems, Pichia pastoris has significant advantages in the processing of expression products, extracellular secretion, post-translational modification and glycosylation modification, and is now widely used for the expression of exogenous proteins.

[0003] Pichia pastoris fermentation for recombinant protein expression often uses the AOX promoter and methanol as the sole carbon source; this process is aerobic fermentation. Due to the high cell density during fermentation, most fermentation processes currently rely solely on parameters such as pH and dissolved oxygen (DO) for regulation. However, DO concentration is the result of both equipment oxygen supply and metabolic oxygen consumption, rather than a single indicator of microbial metabolic intensity or equipment oxygen supply capacity.

[0004] In the process control of Pichia pastoris fermentation for recombinant protein expression, the methanol flow rate is crucial. Methanol, as the sole carbon source during the induction phase, must meet the metabolic needs of cell growth. A low methanol flow rate results in low levels of cell growth and product synthesis, while an excessively high rate can lead to the synthesis of numerous metabolic byproducts and even cell poisoning. Therefore, both excessive and insufficient methanol can cause abnormal expression of the target product. Furthermore, both cell poisoning and insufficient methanol feeding can cause a rebound in dissolved oxygen (DO) levels during fermentation, which can lead to misjudgments and economic losses in cases of inexperience.

[0005] Therefore, there is an urgent need to provide a method that can reflect the cellular metabolic state in real time and accurately, and based on this, perform multi-parameter collaborative intelligent regulation, in order to break through the yield bottleneck of Pichia pastoris fermentation for recombinant protein production. Summary of the Invention

[0006] To address the shortcomings of existing technologies and practical needs, this invention provides a fuzzy intelligent control method and device for the Pichia pastoris fermentation process. The aim is to provide a multi-scale fermentation regulation method based on online analysis to improve protein yield. An integrated intelligent microbial reactor is used to monitor key parameters in the fermentation broth (carbon dioxide concentration, dissolved oxygen, methanol concentration, and pH) in real time. Through a fuzzy intelligent control algorithm, the Pichia pastoris fermentation process is dynamically optimized, improving the expression level and batch-to-batch stability of recombinant albumin, resulting in more precise fermentation process control and more reliable results.

[0007] To achieve this objective, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a fuzzy intelligent control method for the Pichia pastoris fermentation process, the fuzzy intelligent control method comprising the following steps:

[0009] (1) Integrating online sensors in the bioreactor to monitor the state parameters of the fermentation broth in real time, including dissolved oxygen, relative concentration of dissolved carbon dioxide, methanol concentration and pH;

[0010] (2) Input the parameters monitored in step (1) into the central control system and process them through the fuzzy intelligent control algorithm;

[0011] (3) According to the output instructions of the fuzzy intelligent control algorithm, the fermentation operation parameters are coordinated and controlled in real time. The fermentation operation parameters include the change in methanol flow acceleration rate, the change in stirring speed, the change in aeration rate and the change in pH.

[0012] (4) Through the closed-loop control of steps (1) to (3), the dissolved oxygen in the methanol induction stage of the fermentation process is maintained at 20%-40%, the relative concentration of dissolved carbon dioxide is less than 50%, and the residual concentration of methanol is less than 1g / L.

[0013] This invention integrates carbon dioxide and methanol sensors into traditional pH and DO sensors, enabling real-time monitoring of pH, dissolved oxygen (DO), relative dissolved carbon dioxide concentration (DCO2), and methanol concentration during fermentation. This provides a more comprehensive and intuitive reflection of cellular metabolic status. Simultaneously, a fuzzy intelligent control algorithm has been developed, changing the traditional single-variable correlation control mode and achieving multi-parameter fuzzy collaborative regulation. This maintains the cellular metabolic process in a more suitable environment, thereby effectively improving the yield and productivity of the target product.

[0014] The multi-parameter synergistic regulation strategy developed in this invention has important guiding significance for the global optimization of the fermentation process, including seed formation, process identification, process optimization, and pilot-scale amplification. It can not only effectively improve the metabolic synthesis efficiency of the target product, but also help accelerate the iterative upgrading of biomanufacturing equipment and accelerate the commercialization of synthetic biology products.

[0015] Preferably, the processing via fuzzy intelligent control algorithm in step (2) includes:

[0016] (1) Fuzzification of input variables: The triangular membership function is used to convert the precise input values ​​into the membership degrees of the fuzzy set;

[0017] (2) Fuzzy reasoning: Based on the preset fuzzy rule base, reason about the input fuzzy quantity to obtain the output fuzzy set;

[0018] (3) Defuzzification of output variables: The centroid method is used to convert the fuzzy set of output variables into precise control variables.

[0019] Preferably, the inputs of the fuzzy intelligent control algorithm are dissolved oxygen, relative concentration of dissolved carbon dioxide, methanol concentration, and pH; the outputs are the change in methanol flow acceleration rate, the change in stirring speed, the change in aeration rate, and the change in pH.

[0020] Preferably, the calculation formulas of the fuzzy intelligent control algorithm are shown in equations (1) and (2):

[0021] Equation (1);

[0022] Equation (2);

[0023] Where μ is the membership degree; l is the left endpoint value of the triangle membership function parameter; m is the vertex value of the triangle membership function parameter; r is the right endpoint value of the triangle membership function parameter; and zj is the sampling point on the output universe of discourse. It is the total membership degree synthesized after reasoning with all relevant rules; N is the total number of sampling points; Ycrisp is the clear output value.

[0024] Preferably, the methanol flow acceleration rate in step (3) is controlled within the range of 2-10 mL / L / h, for example, 2 mL / L / h, 5 mL / L / h or 10 mL / L / h.

[0025] Preferably, the stirring speed is adjustable in the range of 100-800 rpm, for example, 100 rpm, 500 rpm or 800 rpm.

[0026] Preferably, the ventilation rate is controlled within a range of 0.5-2.0 VVM, such as 0.5 VVM, 1 VVM, or 2.0 VVM.

[0027] Preferably, the pH range is 5-6.

[0028] Preferably, the fermentation process in step (4) includes: a glycerol batch culture stage, a glycerol fed batch culture stage, and a methanol induction stage.

[0029] Preferably, step (4) of maintaining dissolved oxygen at 20%-40% during the methanol-induced fermentation phase includes:

[0030] (a) When dissolved oxygen > 40% and the relative concentration of dissolved carbon dioxide < 50%, increase the methanol flow acceleration rate;

[0031] (b) When dissolved oxygen is <20% and the relative concentration of dissolved carbon dioxide is <50%, increase the stirring speed;

[0032] (c) When dissolved oxygen <20% and the relative concentration of dissolved carbon dioxide >50%, increase the ventilation rate.

[0033] In practical applications, situations where dissolved oxygen > 40% and the relative concentration of dissolved carbon dioxide > 50% are generally not encountered, so the operation under these conditions is negligible.

[0034] Secondly, the present invention provides an integrated bioreactor device, which includes: an online monitoring unit, a signal processing unit, a central control unit, and a regulation unit;

[0035] The online monitoring unit includes a dissolved oxygen sensor, a carbon dioxide sensor, and a methanol sensor that can be sterilized in situ;

[0036] The signal processing unit is configured to perform the following tasks: acquiring and converting signals from the online monitoring unit;

[0037] The central control unit is used to perform actions including: receiving processed signals and outputting control commands through a fuzzy intelligent control algorithm;

[0038] The regulating unit is used to perform actions including: receiving regulation commands and operating the fermentation equipment, and adjusting the stirring speed, aeration rate, tank pressure and methanol flow acceleration rate.

[0039] Preferably, the inputs of the fuzzy intelligent control algorithm are dissolved oxygen, relative concentration of dissolved carbon dioxide, methanol concentration, and pH; the outputs are the change in methanol flow acceleration rate, the change in stirring speed, the change in aeration rate, and the change in pH.

[0040] Preferably, the calculation formulas of the fuzzy intelligent control algorithm are shown in equations (1) and (2):

[0041] Equation (1);

[0042] Equation (2);

[0043] Where μ is the membership degree; l is the left endpoint value of the triangle membership function parameter; m is the vertex value of the triangle membership function parameter; r is the right endpoint value of the triangle membership function parameter; z j These are sampling points on the output universe of discourse; It is the total membership degree synthesized after reasoning with all relevant rules; N is the total number of sampling points; Y crisp It is a clear output value.

[0044] For example, to set the pH to be adjustable within the range of 5-6, three fuzzy sets need to be defined: pH_L (low) [5.0, 5.0, 5.4], pH_OK (suitable) [5.2, 5.5, 5.8], and pH_H (high) [5.6, 6.0, 6.0]. For pH_L [5.0, 5.0, 5.4]: the left endpoint and the vertex are both 5.0, and the right endpoint is 5.4; for pH_OK (suitable) [5.2, 5.5, 5.8]: the left endpoint is 5.2, the vertex is 5.5, and the right endpoint is 5.8; for pH_H (high) [5.6, 6.0, 6.0]: the left endpoint is 5.6, the vertex is 6.0, and the right endpoint is 6.0; satisfying the condition that the left endpoint ≤ the vertex ≤ the right endpoint.

[0045] Thirdly, the present invention provides the application of the fuzzy intelligent control method described in the first aspect or the integrated bioreactor device described in the second aspect in the production of recombinant albumin by Pichia pastoris fermentation.

[0046] Preferably, the Pichia pastoris includes GS115 recombinant Pichia pastoris.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] (1) Precision and reliability: The relative concentration of dissolved carbon dioxide and methanol concentration directly reflect the intensity of cell metabolism, overcoming the limitations of a single DO parameter, making the regulation more precise and avoiding misjudgment;

[0049] (2) High efficiency and high yield: Through multi-parameter collaborative fuzzy control, the cells are always in the optimal metabolic environment, and the expression level of recombinant albumin is increased from 4 g / L in the comparative example to more than 22 g / L, an increase of more than 450%;

[0050] (3) Shortened cycle: The fermentation induction cycle was shortened from 114 hours to 92 hours, which improved production efficiency;

[0051] (4) Stability and universality: Intelligent algorithms reduce the dependence on human experience and improve batch-to-batch stability. This system and strategy have good prospects for industrial scale-up applications. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of a sensor integration system in a bioreactor;

[0053] Figure 2 This is an SDS-PAGE electrophoresis image. Detailed Implementation

[0054] To further illustrate the technical means and effects of this invention, the following description, in conjunction with embodiments and accompanying drawings, provides a further explanation of the invention. It is understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it.

[0055] Where specific techniques or conditions are not specified in the examples, they shall be performed in accordance with the techniques or conditions described in the literature in this field, or in accordance with the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased through legitimate channels.

[0056] The bacterial strain used in the following embodiments of the present invention is: GS115 recombinant Pichia pastoris.

[0057] The solid culture medium in the following embodiments of the present invention consists of 20 g / L tryptone, 10 g / L yeast extract, 20 g / L glucose, and 18 g agar powder, sterilized at 121°C for 20 min, with glucose sterilized separately.

[0058] The seed culture medium in the following embodiments of the present invention consists of 20 g / L tryptone, 10 g / L yeast extract, and 20 g / L glucose, sterilized at 121°C for 20 min, with glucose sterilized separately.

[0059] The fermentation medium in the following examples of the present invention is: H3PO4 10.7 mL, CaSO4 0.93 g / L, K2SO4 18.2 g / L, MgSO4·7H2O 10.9 g / L, KOH 4.13 g / L, glycerol 40 g / L, sterilized at 121℃ for 20 min, and glucose sterilized separately.

[0060] In the following embodiments of the present invention, the cell concentration was determined by the wet weight method: 8 mL of fermentation broth was centrifuged at 10000 r / min for 5 min, and the cell weight per liter of fermentation broth was calculated.

[0061] Example 1

[0062] This embodiment provides an integrated bioreactor device.

[0063] (1) The integrated bioreactor device mainly consists of the following parts:

[0064] Online monitoring unit: includes pH electrode, DO electrode, carbon dioxide (CO2) sensor and methanol sensor. All sensors are sterilizable in situ.

[0065] Signal processing unit: responsible for acquiring the output signals of each sensor in real time and converting them into digital signals.

[0066] Central control unit: It analyzes various parameters and outputs control commands through fuzzy intelligent control algorithms.

[0067] The control unit includes valves, frequency converters, air flow meters, pressure regulating valves, and feed pumps. It receives control commands and operates the fermentation equipment to adjust the stirring speed, aeration rate, tank pressure, and methanol flow acceleration rate.

[0068] The inputs to the fuzzy intelligent control algorithm are the relative concentrations of dissolved oxygen, dissolved carbon dioxide, methanol concentration, and pH; the outputs are the changes in methanol flow acceleration, stirring speed, aeration rate, and pH.

[0069] The calculation formulas for the fuzzy intelligent control algorithm are shown in equations (1) and (2):

[0070] Equation (1);

[0071] Equation (2);

[0072] Where μ is the membership degree; l is the left endpoint value of the triangle membership function parameter; m is the vertex value of the triangle membership function parameter; r is the right endpoint value of the triangle membership function parameter; z j These are sampling points on the output universe of discourse; It is the total membership degree synthesized after reasoning with all relevant rules; N is the total number of sampling points; Y crisp It is a clear output value.

[0073] (2) Bioreactor Integration and Working Principle

[0074] Various online sensors (DO, pH, CO2, methanol) are installed in the fermenter via standard interfaces, ensuring full contact between the probe's sensitive element and the fermentation broth. The signal acquisition system continuously collects data from each sensor, and the central control unit receives the data and performs real-time calculations and decisions based on a preset multi-scale control strategy.

[0075] Example 2

[0076] This embodiment uses Pichia pastoris fermentation tanks for fermentation.

[0077] (1) Sensor integration in bioreactor: By monitoring the concentration of various parameters in the fermentation broth in real time, the data is fed back to the central control unit (fuzzy intelligent control algorithm) of the device described in Example 1. The central control unit issues instructions to regulate the fermentation process in real time at multiple scales.

[0078] (2) Activation of microbial strains

[0079] Take the bacterial culture from the glycerol tube stored at -80℃, activate the bacterial culture using the three-zone streak method, and place it in a 30℃ constant temperature incubator for inverted incubation for about 48 hours.

[0080] (3) Seed culture

[0081] Take the above-mentioned activated fresh plate, pick a single colony under aseptic conditions, inoculate it into a 250 mL Erlenmeyer flask, and culture at 30℃ and 200 rpm for 24 h with shaking to obtain the primary seed.

[0082] At an inoculum rate of 5-10%, primary seeds were transferred to 300 mL / 1000 mL YPD medium and cultured at 30℃ and 200 rpm for 5 h with shaking to obtain secondary seeds.

[0083] (4) Fermentation culture

[0084] Glycerol batch culture stage for cell amplification: Secondary seed culture was inoculated into the fermenter at a rate of 5%. Initial culture parameters were set as follows: stirring speed 150 rpm, culture temperature 30℃, pH 5.0. As the cells grew, the dissolved oxygen (DO) value gradually decreased. By synergistically controlling the stirring speed, aeration rate, and tank pressure using multiple parameters, the DO was maintained at ≥30%. Simultaneously, dissolved oxygen, DCO2 concentration, methanol concentration, and pH in the fermentation broth were monitored.

[0085] When DO suddenly rises to nearly 100%, it indicates that the glycerol in the culture medium has been completely consumed, and the culture should begin in the glycerol-feeding batch stage (when the cell concentration reaches 90-150 g / L).

[0086] Glycerol fed batch culture stage for cell amplification: 50% glycerol was added to the fermenter, and the stirring speed, aeration rate and tank pressure were controlled by fuzzy intelligent control to maintain DO at 20%-40%. When the cell wet weight reached 180-220 g / L, the DO value was observed to rise back to 100%. The "glycerol starvation" state was maintained for another 0.5 h, and then the methanol-induced expression stage was started, which lasted for 4 h.

[0087] Methanol-induced expression stage: During the "starvation" period, methanol was used as the carbon source, with DO controlled at 20%-40%, temperature at 28℃, and pH at 5.5. Simultaneously, dissolved oxygen, DCO2 concentration, methanol concentration, and pH in the fermentation broth were monitored. Using the intelligent algorithm of the central control unit of the device described in Example 1 of this invention, the stirring speed, aeration rate, tank pressure, and methanol flow acceleration were precisely controlled to maintain the residual methanol content at 0.3-0.5 g / L, keeping the DO value at 20%-40% and DCO2 at 30%-40%. Samples were taken at regular intervals to analyze the wet weight of yeast cells and protein concentration in the fermentation broth. In actual operation, the parameter settings for each item in the device described in Example 1 are a dynamic set with feedback and feedforward functions. It not only includes target set values, such as DO 20-40%, but also a series of control strategies, linkage logic, and adaptive rules, enabling the system to intelligently adjust various parameters in complex biological processes, ensuring the entire process proceeds stably and efficiently. This fuzzy intelligent control algorithm dynamically fine-tunes and optimizes within the core target range based on real-time metabolic status. For example, in order to achieve more precise metabolic control, the algorithm will automatically narrow the regulation range of DO to a narrower range of 25%-35%.

[0088] Example 3

[0089] This embodiment uses Pichia pastoris fermentation tanks for fermentation.

[0090] (1) Sensor integration in bioreactor: By monitoring the concentration of various parameters in the fermentation broth in real time, the data is fed back to the central control unit (fuzzy intelligent control algorithm) of the device described in Example 1. The central control unit issues instructions to regulate the fermentation process in real time at multiple scales.

[0091] (2) Activation of microbial strains

[0092] Take the bacterial culture from the glycerol tube stored at -80℃, activate the bacterial culture using the three-zone streak method, and place it in a 30℃ constant temperature incubator for inverted incubation for about 48 hours.

[0093] (3) Seed culture

[0094] Take the above-mentioned activated fresh plate, pick a single colony under aseptic conditions, inoculate it into a 250 mL Erlenmeyer flask, and culture at 30℃ and 200 rpm for 24 h with shaking to obtain the primary seed.

[0095] At an inoculum rate of 5-10%, primary seeds were transferred to 300 mL / 1000 mL YPD medium and cultured at 30℃ and 200 rpm for 5 h with shaking to obtain secondary seeds.

[0096] (4) Fermentation culture

[0097] Glycerol batch culture stage for cell amplification: Secondary seed culture was inoculated into the fermenter at a rate of 5%. Initial culture parameters were set as follows: stirring speed 150 rpm, culture temperature 30℃, pH 5.0. As the cells grew, the dissolved oxygen (DO) value gradually decreased. By synergistically controlling the stirring speed, aeration rate, and tank pressure using multiple parameters, the DO was maintained at ≥30%. Simultaneously, dissolved oxygen, DCO2, and pH in the fermentation broth were monitored.

[0098] When DO suddenly rises to nearly 100%, it indicates that the glycerol in the culture medium has been completely consumed, and the culture should begin in the glycerol-feeding batch stage (when the cell concentration reaches 90-150 g / L).

[0099] Glycerol fed batch culture stage for cell amplification: 50% glycerol was added to the fermenter, and the stirring speed, aeration rate, and tank pressure were controlled by fuzzy intelligent control to maintain DO at 20%-40%. When the cell wet weight reached 180-220 g / L, the DO value was observed to rise back to 100%. The "glycerol starvation" state was maintained for another 0.5 h, and then the methanol-induced expression stage was started, which lasted for 4 h.

[0100] Methanol-induced expression stage: During the "starvation" period, methanol was used as the carbon source, with DO controlled at 20%-30%, temperature at 28℃, and pH at 5.5. Dissolved oxygen, DCO2, and pH in the fermentation broth were monitored. Using the intelligent algorithm of the central control unit of the device described in Example 1 of this invention, the stirring speed, aeration rate, and methanol flow acceleration were precisely controlled to maintain DO at 20%-40% and DCO2 at 30%-40%. Samples were taken at regular intervals to analyze the wet weight of yeast cells and protein concentration in the fermentation broth.

[0101] Example 4

[0102] This embodiment uses Pichia pastoris fermentation tanks for fermentation.

[0103] (1) Sensor integration in bioreactor: By monitoring the concentration of various parameters in the fermentation broth in real time, the data is fed back to the central control unit (fuzzy intelligent control algorithm) of the device described in Example 1. The central control unit issues instructions to regulate the fermentation process in real time at multiple scales.

[0104] (2) Activation of microbial strains

[0105] Take the bacterial culture from the glycerol tube stored at -80℃, activate the bacterial culture using the three-zone streak method, and place it in a 30℃ constant temperature incubator for inverted incubation for about 48 hours.

[0106] (3) Seed culture

[0107] Take the above-mentioned activated fresh plate, pick a single colony under aseptic conditions, inoculate it into a 250 mL Erlenmeyer flask, and culture at 30℃ and 200 rpm for 24 h with shaking to obtain the primary seed.

[0108] At an inoculum rate of 5-10%, primary seeds were transferred to 300 mL / 1000 mL YPD medium and cultured at 30℃ and 200 rpm for 5 h with shaking to obtain secondary seeds.

[0109] (4) Fermentation culture

[0110] Glycerol batch culture stage for cell amplification: Secondary seed culture was inoculated into the fermenter at a rate of 5%. Initial culture parameters were set as follows: stirring speed 150 rpm, culture temperature 30℃, pH 5.0. As the cells grew, the dissolved oxygen (DO) value gradually decreased. By synergistically controlling the stirring speed, aeration rate, and tank pressure using multiple parameters, the DO was maintained at ≥30%. Simultaneously, dissolved oxygen, methanol concentration, and pH in the fermentation broth were monitored.

[0111] When DO suddenly rises to nearly 100%, it indicates that the glycerol in the culture medium has been completely consumed, and the culture should begin in the glycerol-feeding batch stage (when the cell concentration reaches 90-150 g / L).

[0112] Glycerol fed batch culture stage for cell amplification: 50% glycerol was added to the fermenter, and the stirring speed, aeration rate, and tank pressure were controlled by fuzzy intelligent control to maintain DO at 20%-40%. When the cell wet weight reached 180-220 g / L, the DO value was observed to rise back to 100%. The "glycerol starvation" state was maintained for another 0.5 h, and then the methanol-induced expression stage was started, which lasted for 4 h.

[0113] Methanol-induced expression stage: During the "starvation" period, methanol was used as the carbon source, with DO controlled at 20%-30%, temperature at 28℃, and pH at 5.5. Dissolved oxygen, methanol concentration, and pH in the fermentation broth were monitored. Using the intelligent algorithm of the central control unit of the device described in Example 1 of this invention, the stirring speed, aeration rate, and methanol flow acceleration were precisely controlled to maintain the DO value at 20%-40% and the residual methanol content at 0.3-0.5 g / L. Samples were taken at regular intervals to analyze the wet weight of yeast cells and protein concentration in the fermentation broth.

[0114] Comparative Example 1

[0115] The only difference from Example 2 is the specific fermentation culture, which is as follows:

[0116] Glycerol batch culture stage for cell amplification: Secondary seed culture was inoculated into the fermenter at a rate of 5%. Initial culture parameters were set as follows: stirring speed 150 rpm, culture temperature 30℃, pH 5.0. As the cells grew, the dissolved oxygen (DO) value gradually decreased. The stirring speed, aeration rate, and tank pressure were adjusted to control DO ≥ 30%, and the dissolved oxygen and pH in the fermentation broth were monitored.

[0117] When DO suddenly rises to nearly 100%, it indicates that the glycerol in the culture medium has been completely consumed, and the culture should begin in the glycerol-feeding batch stage (when the cell concentration reaches 90-150 g / L).

[0118] Glycerol fed batch culture stage for cell amplification: 50% glycerol was added to the fermenter, and the stirring speed, aeration rate, and tank pressure were adjusted to maintain DO at 20%-40%. When the cell wet weight reached 180-220 g / L, the DO value was observed to rise back to 100%. The "glycerol starvation" state was maintained for another 0.5 h, and then the methanol-induced expression stage was started, which lasted 96-120 h.

[0119] Methanol-induced expression phase: During the "starvation" period, methanol was used as the carbon source, the temperature was controlled at 28℃, pH 5.5, and the stirring and aeration rates were kept constant. Only dissolved oxygen in the fermentation broth was monitored, and the DO and methanol flow rate were correlated to maintain the DO value between 20% and 40%. Samples were taken at regular intervals to analyze the wet weight of yeast cells and protein concentration in the fermentation broth.

[0120] Test case

[0121] After fermentation, the expression level of recombinant albumin, the fermentation induction period, and the degradation rate of recombinant albumin were measured.

[0122] Detection method: The expression level of recombinant albumin was detected by SDS-PAGE. The results of SDS-PAGE detection of the recombinant albumin obtained in Example 2 are as follows: Figure 2 As shown, the concentration of recombinant albumin in the fermentation broth was estimated by diluting it at different ratios and comparing it with the standard protein BSA.

[0123] SDS-PAGE was used to quickly and intuitively detect the degradation rate of recombinant albumin: During the fermentation process, samples were taken every 1 hour, centrifuged to remove the cells, and the supernatant was collected. The supernatant was then mixed with the loading buffer and subjected to SDS-PAGE.

[0124] In addition, the concentration of recombinant albumin in the supernatant was detected by Coomassie brilliant blue staining: the supernatant was taken, mixed with Coomassie brilliant blue, and allowed to stand for 5 min for staining. The OD value was detected at 595 nm using a spectrophotometer. Finally, the concentration of recombinant albumin was calculated by substituting it into the standard curve of standard protein BSA.

[0125] The results of recombinant albumin expression level, fermentation induction period and recombinant albumin degradation rate are shown in Table 1.

[0126] Table 1

[0127]

[0128] As shown in Table 1, the method of this invention, by real-time monitoring of dissolved oxygen, carbon dioxide concentration, methanol concentration and pH in the fermentation broth during the fermentation process, and developing a fuzzy intelligent control algorithm, achieves precise multi-parameter coordinated regulation of the fermentation process. The recombinant albumin expression level is as high as 22 g / L, which is 450% higher than that of the control group. The fermentation induction period is significantly shortened to 92 h, and the degradation rate of the target protein is also significantly reduced, thus significantly improving the yield of the target protein.

[0129] In summary, this invention achieves multi-scale precise control of the Pichia pastoris fermentation process through sensor integration and intelligent control algorithms in a bioreactor, significantly improving the expression level and process stability of recombinant albumin, and has good prospects for industrial application.

[0130] The applicant declares that the detailed method of the present invention is illustrated by the above embodiments, but the present invention is not limited to the above detailed method, that is, it does not mean that the present invention must rely on the above detailed method to be implemented. Those skilled in the art should understand that any improvements to the present invention, equivalent substitutions of the raw materials of the product of the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A fuzzy intelligent control method for Pichia pastoris fermentation process, characterized in that, The fuzzy intelligent control method includes the following steps: (1) Integrating online sensors in the bioreactor to monitor the state parameters of the fermentation broth in real time, including dissolved oxygen, relative concentration of dissolved carbon dioxide, methanol concentration and pH; (2) Input the parameters monitored in step (1) into the central control system and process them through the fuzzy intelligent control algorithm; (3) According to the output instructions of the fuzzy intelligent control algorithm, the fermentation operation parameters are coordinated and controlled in real time. The fermentation operation parameters include the change in methanol flow acceleration rate, the change in stirring speed, the change in aeration rate and the change in pH. (4) Through the closed-loop control of steps (1) to (3), the dissolved oxygen in the methanol induction stage of the fermentation process is maintained at 20%-40%, the relative concentration of dissolved carbon dioxide is less than 50%, and the residual concentration of methanol is less than 1g / L.

2. The fuzzy intelligent control method according to claim 1, characterized in that, The processing via fuzzy intelligent control algorithm described in step (2) includes: (1) Fuzzification of input variables: The triangular membership function is used to convert the precise input values ​​into the membership degrees of the fuzzy set; (2) Fuzzy reasoning: Based on the preset fuzzy rule base, reason about the input fuzzy quantity to obtain the output fuzzy set; (3) Defuzzification of output variables: The centroid method is used to convert the fuzzy set of output variables into precise control variables.

3. The fuzzy intelligent control method according to claim 1 or 2, characterized in that, The inputs to the fuzzy intelligent control algorithm are the relative concentrations of dissolved oxygen, dissolved carbon dioxide, methanol concentration, and pH; the outputs are the changes in methanol flow rate, stirring speed, aeration rate, and pH.

4. The fuzzy intelligent control method according to any one of claims 1-3, characterized in that, The calculation formulas of the fuzzy intelligent control algorithm are shown in equations (1) and (2): Equation (1); Equation (2); Where μ is the membership degree; l is the left endpoint value of the triangle membership function parameter; m is the vertex value of the triangle membership function parameter; r is the right endpoint value of the triangle membership function parameter; z j These are sampling points on the output universe of discourse; It is the total membership degree synthesized after reasoning with all relevant rules; N is the total number of sampling points; Y crisp It is a clear output value.

5. The fuzzy intelligent control method according to any one of claims 1-4, characterized in that, The methanol flow acceleration rate in step (3) is controlled within a range of 2-10 mL / L / h; Preferably, the stirring speed is adjustable within the range of 100-800 rpm; Preferably, the ventilation rate is adjustable within the range of 0.5-2.0 VVM; Preferably, the pH range is 5-6.

6. The fuzzy intelligent control method according to any one of claims 1-5, characterized in that, The fermentation process described in step (4) includes: a glycerol batch culture stage, a glycerol fed batch culture stage, and a methanol induction stage.

7. The fuzzy intelligent control method according to any one of claims 1-6, characterized in that, Step (4) involves maintaining dissolved oxygen at 20%-40% during the methanol-induced fermentation phase, which includes: (a) When dissolved oxygen > 40% and the relative concentration of dissolved carbon dioxide < 50%, increase the methanol flow acceleration rate; (b) When dissolved oxygen is <20% and the relative concentration of dissolved carbon dioxide is <50%, increase the stirring speed; (c) When dissolved oxygen <20% and the relative concentration of dissolved carbon dioxide >50%, increase the ventilation rate.

8. An integrated bioreactor device, characterized in that, The integrated bioreactor device includes: an online monitoring unit, a signal processing unit, a central control unit, and a regulation unit; The online monitoring unit includes a dissolved oxygen sensor, a carbon dioxide sensor, and a methanol sensor that can be sterilized in situ; The signal processing unit is configured to perform the following tasks: acquiring and converting signals from the online monitoring unit; The central control unit is used to perform actions including: receiving processed signals and outputting control commands through a fuzzy intelligent control algorithm; The regulating unit is used to perform actions including: receiving regulation commands and operating the fermentation equipment to regulate the stirring speed, aeration rate, tank pressure and methanol flow acceleration rate; Preferably, the inputs of the fuzzy intelligent control algorithm are dissolved oxygen, carbon dioxide concentration, methanol concentration, and pH; the outputs are the change in methanol flow acceleration rate, the change in stirring speed, the change in aeration rate, and the change in pH. Preferably, the calculation formulas of the fuzzy intelligent control algorithm are shown in equations (1) and (2): Equation (1); Equation (2); Where μ is the membership degree; l is the left endpoint value of the triangle membership function parameter; m is the vertex value of the triangle membership function parameter; r is the right endpoint value of the triangle membership function parameter; z j These are sampling points on the output universe of discourse; It is the total membership degree synthesized after reasoning with all relevant rules; N is the total number of sampling points; Y crisp It is a clear output value.

9. The application of the fuzzy intelligent control method according to any one of claims 1-7 or the bioreactor integrated device according to claim 8 in the production of recombinant albumin by Pichia pastoris fermentation.

10. The application according to claim 9, characterized in that, The Pichia pastoris includes GS115 recombinant Pichia pastoris.

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