KR20230014039A - Method and device for controlling protein production process with microbial fermentation - Google Patents

Abstract

The present invention relates to a method and a device for controlling a protein production process using microbial fermentation by using a near-infrared spectrophotometer and chemometrics. More particularly, the present invention relates to a method for simultaneously measuring and monitoring the indexes of various components of a fermentation broth in real time by using a near-infrared spectrophotometer and chemometrics when fermenting proteinase K by using yeast (<i>Pichia pastrois</i>) microorganisms, and automatically controlling a fermentation process in real time by using electrical signals for respective components generated at this time, and a device therefor. The method comprises the steps of: (i) providing a calibration model obtained using two or more near-infrared wavelength regions specific to each of one or more fermentation broth component indexes; and (ii) monitoring changes in each of the fermentation broth component indexes by using a light source having the two or more near-infrared wavelength regions. Therefore, provided is a calibration model capable of monitoring information on changes in the fermentation broth components due to microbial fermentation, in real time.

Description

Method and device for controlling protein production process by microbial fermentation

The present invention relates to a method and apparatus for controlling a protein production process by microbial fermentation using a near infrared spectrophotometer and chemometrics. More specifically, the present invention simultaneously measures and monitors various component indicators in the fermentation broth using a near-infrared spectrometer and chemometrics when Proteinase K is fermented using yeast ( Pichia pastrois ) microorganisms, and each component generated at this time It relates to a method for automatically controlling a fermentation process in real time using an electrical signal of and an apparatus therefor.

The fermentation process is a process of inoculating microorganisms into a medium capable of growing them and producing a desired production material, thereby providing and managing an environment for the microorganisms to grow and efficiently produce the desired production material at the same time through consumption of the medium components, Optimization of physical and chemical processes suitable for the physiology of microorganisms is essential.

Among physical factors related to fermentation process optimization, stirring water, temperature, internal pressure, pH, dissolved oxygen, etc. have already been developed thanks to the development of highly reliable measurement sensors, online real-time analysis has become commonplace, and real-time monitoring and control is possible However, in addition to the above factors commonly applied to the current fermentation process management, changes in the concentration of major media components essential for real-time management or optimization of the fermentation process, growth profile of microorganisms, production trends of target products and by-products, etc. In the case of change, measuring devices (sensors, etc.) or analysis methods that can immediately analyze it have not been developed. For this reason, fermentation process experiments and production site management to date have relied on offline analysis methods or experiences in which samples of fermentation broth are taken and each component is individually analyzed in a separate dedicated analyzer, so optimization of the fermentation process or process management, especially The application of real-time process control is at a very low level compared to chemical processes. Using non-real-time analysis results of biochemical reactions by microorganisms has disadvantages such as low efficiency in fermentation optimization or management, and requiring a lot of time and manpower for analysis.

In addition, in recent years, many types of microorganisms belonging to genetically modified organisms (GMOs) are being applied to research or production sites, and the level of regulation for outflow of genetically modified microorganism strains before extinction to the external environment is becoming stricter, mainly Sampling of the culture medium for analysis of the culture process is recognized as one of the main leakage routes that occur when culturing microorganisms in production or research sites.

On the other hand, the recent development of electronics, information communication, and optical devices and the rapid development of artificial intelligence (AI) chemometrics that can accurately and quickly process spectral analysis spectrum information have made it possible to expand the application of spectroscopic analysis technology. Among them, near-infrared spectroscopy technology can continuously and simultaneously analyze several components in a short time without pretreatment of the sample, compared to the existing wet analysis method that has been analyzed for each component. can do. Therefore, it is possible to significantly reduce analysis time, cost, and labor, as well as to perform rapid quantitative analysis without the use of harmful chemicals and time required for pretreatment. Conventionally, near-infrared spectroscopy technology is mainly used for quality analysis of agricultural products, such as non-destructive total analysis of sugar content such as apples or tangerines (Korean Society of Industrial and Food Engineering, Vol. 11, No. 1 p38-48; Journal of the Korean Chemical Society 2005, Vol.49, No. 6; Korean Society of Machine Tools Vol. 15, No. 5), Process Management of Petrochemical Processes (Korea Society for Near Infrared Spectrometry 2001. June 01, P. 1082~1082; Korea Society for Near Infrared Spectrometry 2002, Nov.01 , p63 ~ 73) have been used.

Korean Patent Publication No. 2015-0077576 Korean Registered Patent No. 1605995

The object of the present invention is to solve all the problems of the prior art described above.

Another object of the present invention is to provide a calibration model capable of real-time monitoring of information on changes in fermentation broth components according to fermentation of microorganisms.

Another object of the present invention is to automatically control a fermentation process in real time in response to real-time information on changes in fermentation broth components according to microbial fermentation.

Another object of the present invention is to be able to control the leakage of microorganisms to the outside of the microbial fermentation system by monitoring the microbial fermentation in situ.

Representative configurations of the present invention for achieving the above object are as follows.

According to one aspect of the present invention, as a method for real-time control of a protein production process by microbial fermentation, (i) providing a calibration model obtained using two or more near-infrared wavelength regions specific to each of one or more fermentation broth component indicators. , and (ii) monitoring changes in each of the fermentation broth component indicators with light sources in the two or more near-infrared wavelength regions.

According to another aspect of the present invention, a fermentation system for producing proteins by microbial fermentation, comprising (i) using two or more near-infrared wavelength regions specific to each of the fermentation broth component indicators to monitor changes in one or more fermentation broth component indicators. Provided is a fermentation system including a control unit for monitoring and controlling a protein production process by microbial fermentation in real time, including a calibration model obtained by the above method.

In addition to this, another method for implementing the present invention, another system, and a computer readable recording medium recording a non-transitory computer program for executing the method are further provided.

The method for real-time control of the protein production process by microbial fermentation according to the present invention monitors changes in the main component indicators of the fermentation process in real time and uses the electrical signals of each component generated at this time to automatically control the fermentation process in real time. It has the effect of maximizing accurate process management and productivity. In addition, since the method of the present invention can simultaneously analyze various components in real time without sampling during fermentation culture, when genetically modified microorganisms are used in the fermentation process, the problem of leakage of such microorganisms to the outside can be fundamentally solved.

1 is a schematic diagram showing a near-infrared spectrometer and a fermentation process.
Figure 2 shows the raw spectrum (Wavenumber: cm-1) of the Proteinase K fermentation broth.
Figure 3 shows the selected region (Calibration, Wavenumber: cm-1) of the Proteinase K (protein) concentration spectrum of the Proteinase K fermentation broth.
Figure 4 shows the proteinase K concentration measurement spectrum (1st derivative) of the Proteinase K fermentation broth.
5 shows a calibration equation model of Proteinase K concentration in Proteinase K fermentation broth.
6 shows a validation calibration model of Proteinase K concentration in Proteinase K fermentation broth.
7 shows the selected region (Calibration Wavenumber: cm-1) of the concentration spectrum of glycerol, which is the carbon source of the Proteinase K fermentation broth.
Figure 8 shows the spectrum (first differential) of measuring the concentration of glycerol, which is a carbon source, in the Proteinase K fermentation broth.
9 shows a calibration calibration model of the concentration of glycerol, which is a carbon source of Proteinase K fermentation broth.
10 shows a validation calibration model of the concentration of glycerol, which is the carbon source of the Proteinase K fermentation broth.
11 shows the selected region (Calibration, Wavenumber: cm-1) of the concentration spectrum of methanol, which is the carbon source of the Proteinase K fermentation broth.
12 shows the spectrum (first differential) of measuring the concentration of methanol, which is a carbon source, in the Proteinase K fermentation broth.
13 shows a calibration equation model for the concentration of methanol, which is the carbon source of Proteinase K fermentation broth.
14 shows a validation calibration model of the concentration of methanol, which is a carbon source of Proteinase K fermentation broth.
15 shows the selected region (Calibration, Wavenumber: cm-1) of the optical density spectrum representing the cell growth of the Proteinase K fermentation broth.
Figure 16 shows the absorbance measurement spectrum (first differential) showing the cell growth of the Proteinase K fermentation broth.
17 shows a calibration equation model of absorbance representing cell growth of Proteinase K fermentation broth.
18 shows a validation calibration model of absorbance representing cell growth of Proteinase K fermented broth.
Figure 19 connects a near-infrared spectrometer to the fermentor to monitor Proteinase K concentration, carbon source glycerol and methanol concentration, and absorbance (OD) trend indicating the growth of bacteria in an online state with a calibration model, and to manage the carbon source concentration. It shows the result of automatically adding and controlling phosphorus glycerol and methanol in real time.

Hereinafter, the present invention will be described in more detail.

According to one aspect of the present invention, as a method for real-time control of a protein production process by microbial fermentation, (i) providing a calibration model obtained using two or more near-infrared wavelength regions specific to each of one or more fermentation broth component indicators. , and (ii) monitoring changes in each of the fermentation broth component indicators with light sources in the two or more near-infrared wavelength regions.

As a result of intensive efforts to develop a method for monitoring and/or controlling in real time one or more fermentation broth components such as microorganisms, proteins, media components, etc. present in the fermentation broth during microbial fermentation for protein production, the present inventors have found that each fermentation broth component The present invention was completed by identifying two or more near-infrared wavelength regions that can be specifically detected/calibrated, and constructing a calibration model capable of quantifying the electrical signals of each component generated using the two or more near-infrared wavelength regions.

The method may further include controlling the fermentation process in real time in response to the electrical signal of each component indicator generated through the monitoring.

In one embodiment, the monitoring may be performed through a near-infrared spectrometer attached to the fermentation tube in the form of a probe.

In one embodiment, the monitoring can be performed in situ without sampling of the fermentation broth.

The near-infrared spectrometer is attached to the fermentation tube in the form of a probe, so that changes in the protein fermentation broth component index can be monitored without sampling. For example, when genetically modified microbial strains that are undesirable to leak into the external environment are used, containment process management (Contained On-tank or in-line (process control), non-sampling fermentation process analytical control can be used.

The microorganism may include any microorganism as long as it can produce protein from a carbon source through fermentation. For example, the microorganisms may be microorganisms belonging to genetically modified organisms (GMOs) including living modified organisms (LMOs). Specifically, the microorganism may be bacteria or yeast. More specifically, the microorganism may be Pichia pastoris .

The protein may include any protein produced by a microorganism or engineered to be produced by recombinant technology. In one embodiment, the protein may be Proteinase K.

Glycerol, methanol, or a combination thereof may be used as a carbon source for microbial fermentation for the production of the protein.

The microbial fermentation may be batch, continuous or fed-batch culture. Preferably, the microbial fermentation may be continuous or fed-batch culture.

In the microbial fermentation, the fermentation broth component that needs to be monitored may be a microorganism used in the microbial fermentation, a carbon source, a nitrogen source, other organic components, inorganic components, and proteins to be produced. The fermentation broth component indicator may be any indicator capable of knowing the concentration change of the fermentation broth component in the fermentation broth. For example, it may be the concentration of each component present in the fermentation broth. In the case of microorganisms, it may be absorbance indicating the growth rate or concentration of the microorganisms.

In one embodiment, the microbial fermentation may be performed by continuous or fed-batch culture of yeast for Proteinase K production. Glycerol and methanol may be used as the carbon source for the microbial fermentation.

At this time, the fermentation broth component index may be one or more selected from the group consisting of Proteinase K concentration, glycerol concentration, methanol concentration, and microbial growth (absorbance).

The near-infrared wavelength used herein refers to a wavelength in the near infrared (NIR) region of 800 to 2500 nm (12,000 to 4,000 cm-1) existing between visible and mid-infrared rays.

In the above method, providing or monitoring a calibration model may be performed using a near-infrared spectrometer.

In order to provide or monitor the calibration model, two or more near-infrared wavelength regions may be used in combination for one fermentation broth component index. Specifically, it may be desirable to use two near-infrared wavelength regions in combination to provide a calibration model or monitor one fermentation broth component indicator.

In one embodiment, the two or more wavelength regions include a wavelength region of 9403.5 to 7498.1 cm -1 and a wavelength region of 6101.9 to 5446.2 cm -1 in the case of the Proteinase K concentration; In the case of the glycerol concentration, it includes a wavelength range of 8886.7 to 7745.0 cm -1 and a wavelength range of 5870.4 to 5420.0 cm -1 ; In the case of the methanol concentration, it includes a wavelength range of 8457.7 to 7498.1 cm -1 and a wavelength range of 6101.9 to 5446.2 cm -1 ; The microbial growth rate may include a wavelength range of 8535.3 to 7503.4 cm -1 and a wavelength range of 5797.2 to 5439.5 cm -1 .

The calibration model used in the method is: (a) collecting n samples of the fermentation broth at regular time intervals, (b) calibration of m samples among the n samples collected using a near-infrared spectrometer (Calibration) ) measuring the raw spectrum, (c) performing a physicochemical standard analysis on the m samples from which the raw spectrum was measured, (d) statistically preprocessing the raw spectrum measured in step (b) Step, (e) selecting two or more near-infrared wavelength regions specific to each of the one or more fermentation broth component indicators to be quantified among the spectra pretreated in step (d), and calibrating from the spectra of the selected regions through chemometrics constructing a calibration model, and (f) applying the results obtained by applying the n-m samples not used in the raw spectrum measurement to the calibration calibration model developed in step (e) to the physicochemical It can be obtained by a method comprising the steps of calibration and optimization by comparison with standard analytical results.

Wherein n and m are natural numbers, n is a natural number greater than m. Preferably, n and m may be a natural number of 50 or more, 100 or more, 150 or more, 200 or more, 250 or more, 300 or more, 350 or more, 400 or more, 450 or more, or 500 or more, but are not limited thereto. It can be a larger natural number to increase the accuracy of the calibration model.

In addition, the difference between n and m may be 10 or more, 50 or more, 100 or more, 150 or more, 200 or more, 250 or more, or 300 or more, but is not limited thereto.

The n and m are appropriately large numbers to derive a calibration model for statistical processing, and can be easily selected and used by a person skilled in the art.

As used herein, "chemometrics" is a general term for mathematical statistical algorithms that enable qualitative and quantitative analysis by deriving desired information from various analytical chemical data such as spectra and chromatograms. In chemometrics, multivariate analysis can be used mainly. Multivariate analysis is a statistical technique that simultaneously analyzes multiple dependent variables for multiple independent variables in contrast to univariate analysis. Statistical analysis of chemometrics includes Multiple Linear Regression (MLR), Principle Component Regression (PCL), Partial Least Squares (PLS), Modified Partial Least Squares, MPLS) or the like may be used, and in particular, Partial Least Squares (PLS) may be used.

In this specification, the term "partial least squares (PLS)" may be used interchangeably with the term "partial least square regression (PLSR)".

Through the calibration model, it is possible to monitor in real time the change in each of the fermentation broth component indicators that change every moment during the microbial fermentation process, and the fermentation process can be automatically controlled in real time.

According to another aspect of the present invention, a fermentation system for producing proteins by microbial fermentation, comprising (i) using two or more near-infrared wavelength regions specific to each of the fermentation broth component indicators to monitor changes in one or more fermentation broth component indicators. Provided is a fermentation system including a control unit for monitoring and controlling a protein production process by microbial fermentation in real time, including a calibration model obtained by the above method.

In one embodiment, the fermentation system may further include (ii) a fermentation unit for microbial fermentation and (iii) a near-infrared spectroscopy unit.

In one embodiment, the control unit may automatically control the fermentation process in real time in response to electrical signals of each component indicator generated by monitoring changes in the one or more fermentation broth component indicators.

In one embodiment, the fermentation unit may include any means or devices known in the art for microbial fermentation, such as a fermentation tank, stirrer, culture medium supply pipe, gas pipe, etc. required for microbial fermentation, but is not limited thereto.

In one embodiment, the near-infrared spectroscopy unit may be mounted in the form of a probe in the fermentation unit. The near-infrared spectrometer is attached to the fermentation tube in the form of a probe, so that changes in the protein fermentation broth component index can be monitored in real time without sampling. For example, when genetically modified microbial strains that are undesirable to leak into the external environment are used, containment process management (Contained An on-tank or in-line non-sampling fermentation process analysis management system, which is a process control, may be used.

In one embodiment, the fermentation system may be hermetic. For example, the fermentation system can check the fermentation state of the protein production process by microbial fermentation through an on-tank measurement facility.

The microorganism may include any microorganism as long as it can produce protein from a carbon source through fermentation. For example, the microorganisms may be microorganisms belonging to genetically modified organisms (GMOs) including living modified organisms (LMOs). Specifically, the microorganism may be bacteria or yeast. More specifically, the microorganism may be Pichia pastoris .

In one embodiment, the fermentation broth component index may be one or more selected from the group consisting of Proteinase K concentration, glycerol concentration, methanol concentration, and microbial growth (absorbance). For example, the one or more fermentation broth component indicators may include a concentration of glycerol, which is a carbon source for microbial growth.

In one embodiment, the two or more wavelength regions include a wavelength region of 9403.5 to 7498.1 cm -1 and a wavelength region of 6101.9 to 5446.2 cm -1 in the case of the Proteinase K concentration; In the case of the glycerol concentration, it includes a wavelength range of 8886.7 to 7745.0 cm -1 and a wavelength range of 5870.4 to 5420.0 cm -1 ; In the case of the methanol concentration, it includes a wavelength range of 8457.7 to 7498.1 cm -1 and a wavelength range of 6101.9 to 5446.2 cm -1 ; The microbial growth rate may include a wavelength range of 8535.3 to 7503.4 cm -1 and a wavelength range of 5797.2 to 5439.5 cm -1 .

In one embodiment, the near-infrared spectroscopy unit includes a wavelength region of 9403.5 to 7498.1 cm -1 and 6101.9 to 5446.2 cm -1 in the case of the Proteinase K concentration during the real-time monitoring; In the case of the glycerol concentration, it includes a wavelength range of 8886.7 to 7745.0 cm -1 and a wavelength range of 5870.4 to 5420.0 cm -1 ; In the case of the methanol concentration, it includes a wavelength range of 8457.7 to 7498.1 cm -1 and a wavelength range of 6101.9 to 5446.2 cm -1 ; In the case of the microbial growth rate, it may be set to emit a light source in a wavelength range including a wavelength range of 8535.3 to 7503.4 cm -1 and a wavelength range of 5797.2 to 5439.5 cm -1 .

Referring to FIG. 1, the control unit is connected to the fermentation unit by wire or wireless communication and receives an electrical signal of a fermentation broth component indicator in the fermentation unit obtained through a light source in the wavelength range emitted from the near-infrared spectroscopy unit to be established. It is possible to monitor the fermentation broth component index converted by the calibrated calibration model, and to control the carbon source supply in real time in response thereto.

For terms related to the fermentation system, microbial fermentation, produced proteins, fermentation broth component indicators, calibration models, etc., reference may be made to a description of a method for real-time control of a protein production process by microbial fermentation.

Implementations according to the present invention described above may be implemented in the form of program instructions that can be executed through various computer components and recorded in a non-temporary computer readable recording medium. The non-transitory computer readable recording medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the non-transitory computer readable recording medium may be specially designed and configured for the present invention or may be known and usable to those skilled in the art of computer software. Examples of non-transitory computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROM and DVD, and magneto-optical media such as floptical disks ( magneto-optical media), and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter or the like as well as machine language codes such as those produced by a compiler. The hardware device may be configured to act as one or more software modules to perform processing according to the present invention and vice versa.

Hereinafter, the present invention will be described in more detail by the following examples. However, the following examples are only for exemplifying the present invention, and the scope of the present invention is not limited only to these.

Now, the process for developing the calibration model of the main management factors applied to the present invention will be described in detail by taking Proteinase K fermentation as an example.

(P1) Acquisition of near-infrared absorption spectrum related to proteinase K concentration in proteinase K fermentation broth, carbon source glycerol and methanol concentration, and cell growth (absorbance)

After taking 300 samples of the fermentation broth in the continuous Proteinase K fermentation process at regular intervals (every 2 hours), 200 of them were sampled at 12,000 cm-1 to 4,000 cm-1 using a near-infrared spectrometer. The raw spectrum was obtained by scanning in a constant wavelength unit of 2 nm in the full-wave field region of . At this time, the passing distance of the light source was 2 mm, the measurement mode was a transmission reflection mode, and the FT-NIR device used a matrix-F FT-NIR (FT-NIR Matrix-F Bruker Optics: Germany). Using the same 200 samples, physicochemical standard analysis method was used to determine the concentration of glycerol as a carbon source by high performance liquid chromatography (HPLC), the concentration of methanol as another carbon source by gas chromatography (GC), and the concentration of Proteinase K (Protein) was measured at 600 nm using a spectrophotometer to measure cell viability using the Bradford Protein Assay (Kit) method, and was used as a standard sample analysis value for developing a calibration model. Samples of the fermentation broth can be used by sequentially collecting the fermentation broth in a fed batch every 2 hours, and 300 samples were taken to widen the calibration equation and develop a significant calibration model. 24 to 30 samples were taken per fermentation cycle, and 200 samples from each fermentation broth were used for raw spectrum measurement for the development of a calibration model, and the remaining 100 samples were used for the validation model. ) was used as a sample for assay. 300 samples were selected as evenly distributed samples throughout the fermentation process.

(P2) Physicochemical standard analysis of samples

About 25 batches (total of 200 Dog) samples were taken and physicochemical standard analysis was performed. 30 ml of the sample was taken, the FT-NIR spectrum of 10 ml was immediately measured, and the remaining samples were stored in a refrigerator. After dissolving the refrigerated samples at room temperature, each was analyzed using an existing standard analyzer. That is, the Bradford Protein Assay (Kit) method for proteinase K concentration, the high-performance liquid chromatography (LC) method for the concentration of glycerol, the carbon source, the gas chromatography (GC) method for the methanol concentration, and the measurement of cell growth using a spectrometer It was measured at 600 nm and used as a standard analysis value for developing the FT_NIR calibration model.

(P3) Preprocessing of raw spectra

In order to obtain an accurate quantitative calibration model from the raw spectrum of the fermentation broth obtained in (P1) above, it is necessary to remove the spectral variables according to the change in instrument conditions and sample and to remove the noise included in the measured spectrum. For spectral correction and preprocessing, BRUKER's OPUS Quantitative Method (Germany) program was used. Such preprocessing is a necessary process to secure a high-quality spectrum required for the development of a robust, highly accurate and reproducible calibration model by removing noise from the raw spectrum and matching the base line.

In the present invention, the first derivative of the first derivative, second derivative, Norris-Williams derivatives, and Savitzky-Golay polynomial derivatives, which are generally applied to the transmission and reflection spectrum, is applied. The raw spectrum was mathematically preprocessed (Math treatment) to remove noise and match the baseline to enhance the utilization of useful information in the spectrum, and a more accurate and reproducible preprocessed spectrum for the development of a calibration model was selected and used.

(P4) Development of a calibration calibration model through preprocessed spectral region selection and regression analysis

In the case of the fermentation process liquid used in the present invention, since it contains many impurities and foreign substances, a specific wavelength region in which the spectrum of the component to be quantified is unique is selected, and a calibration calibration model is developed from the spectrum of the selected region through chemometrics. do. At this time, the applicable statistical analysis methods of chemometrics are Multiple Linear Regression (MLR), Principle Component Regression (PCL), Partial Least Squares (PLS), and Transformed Partial Least Squares method. There are methods such as (Modified Partial Least Squares, MPLS), and the present invention developed a calibration model using the partial least squares method (PLS) among them. Selection of the optimal calibration calibration model was selected using statistics such as standard error of calibration (SEC) and correlation coefficient (Coefficient of determination in calibration, R2).

(P5) Validation and optimization of the selected calibration calibration model

In order to verify the applicability of the selected calibration calibration model to the new sample to be analyzed, the results obtained by applying the spectrum of 100 new calibration samples that were not used as analysis samples to the calibration calibration model were analyzed by physicochemical standard analysis. It was tested by comparison. The applicability test of the calibration calibration model is performed using statistics such as SEP (Standard error of prediction), R2 (Coefficient of determination in prediction), Bias (Average difference between reference and NIR values) and SD (Standard deviation). Accuracy and reproducibility were cross-checked by applying unknown samples (100 samples) to the calibration model. When the final validation model optimized as described above is used, the proteinase K concentration of the fermentation broth to be analyzed in the present invention, the carbon source glycerol and methanol concentration, and the cell growth rate absorbance, etc. are simultaneously measured in real time It can be measured accurately and reproducibly, and the measurement time is completed within a few seconds, which is incomparably faster than conventional physicochemical analysis methods. After scanning the radio field 500 times within 10 seconds, it was confirmed that the concentration of various substances can be monitored in real time by obtaining an average and measuring the spectrum with one time.

(P6) Control management of time fermentation process through process automation

In addition, if you set the concentrations of glycerol and methanol in the medium, which are supplied carbon sources to improve productivity, by connecting the unique electrical signals of each component concentration generated during real-time simultaneous multi-material analysis of the fermentation process by FT-NIR to an automated system. It is possible to automatically manage the supply of key raw materials for productivity improvement in the culture medium, thereby creating an environment in which microorganisms can maximize the production of target products. Therefore, it was confirmed that this method is an innovative method capable of real-time automatic control of fermentation, which was impossible until now, as it can be used for productivity improvement as well as real-time control management of the fermentation process.

Example 1. Development of a method for measuring Proteinase K concentration in Proteinase K fermentation broth using microorganisms

Example 1.1. Installation of FT-NIR and sample collection

In order to measure and monitor the fermentation status of the fermentation tube online in real time, convert each signal generated at this time to electricity, and use it to control the fermentation process. A proteinase K fermentation process monitoring and control system was constructed so that the NIR measurement signal could be used for fermentation process control (see FIG. 1).

In order to perform physicochemical standard analysis while measuring the raw spectrum of FT-NIR of the continuously proceeding fermentation process, about 30 ml of samples were taken at intervals of 1 to 2 hours, and a portion (10 ml) of this sample was It was used for star spectrum measurement, and the rest was immediately refrigerated and used as a sample for physicochemical standard analysis. Samples were taken during the fermentation culture period of 10 to 15 times, and the number of samples was 300, and more samples were collected and used by repeating fermentation experiments if necessary.

Example 1.2. Measurement of raw spectra and analysis of physicochemical standards of samples

Example 1.2.1. Spectral measurement of a sample

About 25 to 30 samples were taken during one run of the fermentation tube (the entire process of 130-hour Fed Batch Culture by inoculating the strain into the fermentation medium), and some of the samples (10 ml) were subjected to FT-NIR. In the entire wavelength range from 12,000 cm-1 to 4,000 cm-1, the beam passing distance is 2 mm and the wavelength interval is 2 nm. The measurement mode of the spectrum was set to transmission reflection mode, and the raw spectrum was measured and collected (FIG. 2). At this time, the NIR device used Matrix-F FT-NIR (FT-NIR Matrix-F Bruker Optics; Germany). In addition, in all the spectra shown in the figure, the x-axis represents the wave number (cm-1), and the y-axis represents the absorbance unit.

Example 1.2.2. Physicochemical Standard Analysis of Proteinase K Concentration

After dissolving the samples stored in the refrigerator at room temperature, each concentration was analyzed using the Bardford Protein Assay Kit that analyzes the proteinase K concentration.

Example 1.3. Selection of proteinase K spectral region and development of calibration model

The raw spectrum of Proteinase K concentration measured in Example 1.2 and the physicochemical standard analysis result of the proteinase K concentration of the sample were processed by the methods mentioned in (P2), (P3) and (P4) to derive a calibration calibration model. . More specifically, first, the raw spectra of 300 samples are pre-processed with first derivative to obtain a stable spectrum (see FIG. 3), and a spectral region specific to Proteinase K, that is, two wavelength regions as shown in FIG. was selected to promote the development of a calibration calibration model. The chemometrics method used to develop the calibration calibration model was PLS, and the optimal calibration calibration model was selected using statistical values such as predicted standard deviation (SEC) and correlation coefficient (R2). As shown in FIG. 5, in the calibration calibration model curve developed in this way, the Y axis is the FT-NIR analysis value of the proteinase K concentration in the fermentation broth, and the X axis is the standard analysis value of the proteinase K concentration obtained by analysis with the Bardford Protein Assay Kit. It is a very high level It can be seen that there is a correlation between

Example 1.4. Test of the developed Proteinase K calibration calibration model

In order to test whether the calibration calibration model developed using the spectra of 200 samples is applicable to the new samples that were not used in the development of the calibration calibration model, the spectra of 100 new samples prepared and the physicochemical standard analysis method Proteinase K concentration was substituted and compared, and the results are shown in FIG. 6 (X axis and Y axis are as described with respect to FIG. 5) and Table 1.

As can be seen from FIG. 6 and Table 1, it was confirmed that there was no statistically significant difference in the R2 value, the RMSEE value, and the Bias value. In other words, the analysis results measured by FT-NIR and the standard physicochemical analysis results were very similar, so the development of the validation calibration model was successfully achieved, and it was confirmed that the proteinase K concentration in the fermentation broth was measured accurately and reproducibly. Therefore, the development of a real-time continuous monitoring method of Proteinase K concentration in fermentation broth by FT-NIR, which can replace the existing offline analysis method (Bardford Protein Assay Kit method), was completed.

^R2 RMSEE RPD Bias Offset Slop Corr. Coeff Calibration 99.26 14.2 11.9 - 1.655 0.993 0.9965 Validation 98.62 17.2 9.61 -0.149 1.990 0.992 0.9946

Example 2. Development of a method for measuring glycerol (carbon source) concentration in Proteinase K fermentation broth using microorganisms

Example 2.1. Installation of FT-NIR and sample collection

It was carried out in the same manner as in Example 1.1. At this time, the sample used is the same sample as in Example 1.

Example 2.2. Measurement of spectra and physicochemical analysis of pretreatment and glycerol concentrations

It was carried out in the same manner as in Example 1, and the raw spectrum for analyzing the concentration of glycerol was also used as the same raw spectrum as the raw spectrum for analyzing the concentration of Proteinase K. The same spectrum as the spectrum for measuring the proteinase K concentration was used for preprocessing of the original spectrum by first derivative. This is the greatest advantage and characteristic of FT-NIR analysis, which can simultaneously analyze various components in a single spectrum analysis.

Meanwhile, in the present invention, glycerol is a representative sugar component of Proteinase K fermentation broth, and as a result of preliminary analysis using HPLC, glycerol was defined as the main sugar component. The physicochemical standard analysis of the glycerol concentration was performed using an HPLC analyzer (LC_40A. SHIMADZU. JAPAN).

Example 2.3. Selection of glycerol spectral region and development of calibration model

The region of the spectrum for measuring the glycerol concentration was selected as two wavelength regions as shown in FIG. A calibration calibration model was derived by processing in the methods mentioned in (P2), (P3) and (P4) (see FIG. 8).

The chemometrics method used to develop the calibration calibration model was PLS, and the optimal calibration calibration model was selected using statistical values such as predicted standard deviation (SEC) and correlation coefficient (R2). As shown in FIG. 9, in the calibration calibration model curve developed in this way, the Y axis is the FT-NIR analysis value of the glycerol concentration in the fermentation broth, and the X axis is the actual physical and chemical analysis value, that is, the standard analysis value of the glycerol concentration obtained by HPLC analysis. It can be seen that there is a correlation.

Example 2.4. Test of the developed glycerol calibration calibration model

The calibration calibration equation model developed using the spectra of 200 samples (Fig. 9) substitutes the spectra of 100 new samples prepared for reference and the glycerol concentration by the physicochemical standard analysis method to test whether it is applicable to the new samples in the image , Comparison test was performed, and the results are shown in FIG. 10 (X-axis and Y-axis are as described for FIG. 9) and Table 2.

As can be seen from FIG. 10 and Table 2, it was confirmed that there was no statistically significant difference in the R2 value, the RMSEE value, and the Bias value. In other words, the analysis results measured by FT-NIR and the standard physicochemical analysis results were very similar, so the development of the validation calibration model was successfully performed, and it was confirmed that the glycerol concentration in the fermentation broth was accurately and reproducibly measured. there was. In other words, since the physicochemical standard analysis value and the analysis value by FT_NIR showed a very high significant correlation, it was confirmed that the change over time in the glycerol concentration in the Proteinase K fermentation broth could be monitored in real time by FT-NIR. Therefore, as sugar monitoring and control, one of the biggest problems in real-time monitoring and control of changes over time in fermentation phenomena, has been solved, real-time monitoring of Proteinase K fermentation and the development of an automatic control process have become possible.

^R2 RMSEE RPD Bias Offset Slop Corr. Coeff Calibration 99.83 0.734 24.4 - 0.022 0.998 0.9992 Validation 99.73 0.889 19.2 -0.00494 0.039 0.997 0.9986

Example 3. Development of a method for measuring methanol (carbon source) concentration in Proteinase K fermentation broth using microorganisms

Example 3.1. Installation of FT-NIR and sample collection

It was carried out in the same manner as in Example 1.1. At this time, the sample used is the same sample as in Example 1.

Example 3.2. Measurement of spectrum and physicochemical analysis of pretreatment and methanol concentration

It was carried out in the same manner as in Example 1, and the same raw spectrum as the raw spectrum for analyzing the proteinase K concentration was used for the analysis of methanol concentration. The same spectrum as the spectrum for measuring the proteinase K concentration was used for preprocessing of the original spectrum by first derivative.

The physicochemical standard analysis of methanol was analyzed using gas chromatography (GC-2010 Pluse. SHIMADZU. JAPAN).

Example 3.3. Selection of methanol spectral region and development of calibration model

The region of the spectrum for measuring the methanol concentration was selected as two wavelength regions as shown in FIG. A calibration calibration model was derived by processing in the methods mentioned in (P2), (P3) and (P4) above (see FIG. 12).

The chemometrics method used to develop the calibration calibration model was PLS, and the optimal calibration calibration model was selected using statistical values such as predicted standard deviation (SEC) and correlation coefficient (R2). As shown in FIG. 13, the calibration calibration model curve thus developed has a very high degree of correlation with the X axis being the standard analysis value of the methanol concentration obtained by GC analysis and the Y axis being the FT-NIR analysis value of the methanol concentration in the fermentation broth. can

Example 3.4. Verification of the developed methanol calibration calibration model

In order to test whether the calibration calibration model (FIG. 13) developed using the spectra of 200 samples is applicable to unknown new samples, the spectra of 100 new samples and the methanol concentration by physicochemical standard analysis method Substitution and comparison tests were performed, and the results are shown in FIG. 14 (X-axis and Y-axis are as described for FIG. 13) and Table 3.

As can be seen from FIG. 14 and Table 3, it was confirmed that there was no statistically significant difference in R2 value, RMSEE value, and the like. That is, the analysis results measured by FT-NIR and the results of standard physicochemical analysis were very similar, so the development of the validation calibration model was successfully achieved, and it was confirmed that the methanol concentration in the fermentation broth was accurately and reproducibly measured. could In other words, since the physicochemical standard analysis value and the analysis value by FT-NIR show a very high significant correlation, it is possible to accurately and reproducibly monitor the change in methanol concentration in Proteinase K fermentation broth in real time with FT-NIR. Confirmed. Therefore, real-time measurement of methanol concentration, which has been one of the biggest barriers to real-time monitoring and control of fermentation over time, has become possible, enabling online real-time monitoring of the Proteinase K fermentation process and development of an automatic control process.

^R2 RMSEE RPD Bias Offset Slop Corr. Coeff Calibration 99.37 0.063 12.6 - 0.008 0.994 0.9968 Validation 98.31 0.0984 7.69 -0.0006 0.018 0.987 0.9915

Example 4. Development of a method for measuring absorbance (OD) of Proteinase K fermentation broth using microorganisms

Example 4.1. Installation of FT-NIR and sample collection

It was carried out in the same manner as in Example 1.1. At this time, the sample used is the same sample as in Example 1.

Example 4.2. Spectral measurement and pretreatment and physicochemical analysis of absorbance

It was performed in the same manner as in Example 1, and the raw spectrum for absorbance analysis was also used as the same raw spectrum as the raw spectrum for Proteinase K concentration analysis. The same spectrum as the spectrum for measuring the proteinase K concentration was used for preprocessing of the original spectrum by first derivative.

The physicochemical standard analysis of absorbance was performed at 600 nm using a spectrophotometer (LIBAR S80W BIOCHROM USA), and the obtained value was used as a standard analysis value.

Example 4.3. Selection of absorbance spectral region and development of calibration model

As shown in FIG. 15, the spectrum region for measuring absorbance was selected as two wavelength regions, and the raw absorbance spectrum measured in Example 4.1 and Example 4.2 and the physicochemical standard analysis result of sample absorbance were presented as above (P2 ), (P3) and (P4), a calibration calibration model was derived (see FIG. 16).

The chemometrics method used to develop the calibration calibration model was PLS, and the optimal calibration calibration model was selected using statistical values such as predicted standard deviation (SEC) and correlation coefficient (R2). As shown in FIG. 17, in the calibration calibration model curve developed in this way, the Y-axis is the FT-NIR analysis value of the absorbance of the fermentation broth, and the X-axis is the actual physical and chemical analysis value, that is, the standard analysis value obtained by the spectrometer at 600 nm. Representing cell growth It can be seen that the absorbance measurements also have a very high degree of correlation.

Example 4.4. Verification of the developed absorbance calibration calibration model

In order to test whether the calibration calibration model (FIG. 17) developed using the spectra of 200 samples is applicable to unknown new samples, the spectra of 100 new samples prepared for reference and the absorbance by the standard physicochemical analysis method are substituted. , Comparison test was performed, and the results are shown in FIG. 18 (X-axis and Y-axis are as described for FIG. 17) and Table 4.

As can be seen from FIG. 18 and Table 4, it was confirmed that there was no statistically significant difference in the R2 value, the RMSEE value, and the Bias value. That is, the absorbance (OD) analysis value measured by FT-NIR and the result of the standard physicochemical analysis were very similar, so the development of a validation calibration model was successfully achieved. It was confirmed that cell viability could be accurately and reproducibly measured. Therefore, real-time measurement of cell growth, one of the biggest problems in real-time monitoring and control of changes over time in fermentation phenomena, became possible, enabling online real-time monitoring of the Proteinase K fermentation process and development of an automatic control process.

^R2 RMSEE RPD Bias Offset Slop Corr. Coeff Calibration 99.26 14.2 11.9 - 1.655 0.993 0.9965 Validation 98.62 17.2 9.61 -0.149 1.990 0.992 0.9946

Example 5. Monitoring and control results of Proteinase K fermentation process using FT-NIR

A calibration equation that can measure the main fermentation factors developed through Examples 1 to 4, that is, Proteinase K (protein) concentration, carbon source glycerol and methanol concentration, and absorbance (OD _{600 nm} ) representing cell growth. The FT-NIR system equipped with the model was applied to the actual fermentation process to measure and monitor multiple fermentation components simultaneously and continuously, and to test whether the Proteinase K fermentation process could be controlled in real time using the electrical signals generated at this time. The results are shown in Figure 19.

Specifically, the spectrum measured in real time through the probe installed in the fermentation tube is calculated through a calibration model, and the proteinase K concentration in the Proteinase K fermentation broth by FT-NIR, the concentration of glycerol and methanol as carbon sources, and the degree of cell growth Absorbance (OD _600nm ) is not only analyzed and monitored accurately and reproducibly in real time, but also by converting the signal generated during monitoring into an electrical signal and linking it with PLC (Programmable Logic Controller), an existing fermentation process control program, the fermentation process can be monitored in real time. could be controlled automatically.

In addition, as shown in FIG. 19, after taking samples off-line, the physicochemical standard analysis results and the measured values of FT-NIR, that is, the concentration of Proteinase K as the target product, the concentration of glycerol and methanol as carbon sources, and the absorbance as cell growth ( OD _{600 nm} ) all showed very good analysis values with little difference from the standard analysis value, confirming that real-time monitoring of the Proteinase K fermentation process by FT-NIR and real-time automatic control of the fermentation process using the signal proceeded very well. .

Claims (21)

As a method for real-time control of a protein production process by microbial fermentation,
(i) providing a calibration model obtained using two or more near-infrared wavelength regions specific to each of the one or more fermentation broth component indicators, and
(ii) monitoring changes in each of the fermentation broth component indicators with light sources in the two or more near-infrared wavelength regions
Way. According to claim 1,
The method further comprises the step of controlling the fermentation process in real time in response to the electrical signal of each component indicator generated through the monitoring
Way. According to claim 1,
The monitoring is performed through a near-infrared spectrometer attached to the fermentation tube in the form of a probe
Way. According to claim 1,
The monitoring can be performed without sampling of the fermentation broth
Way. According to claim 1,
The protein is Proteinase K
Way. According to claim 1,
The microorganism is a yeast
Way. According to claim 1,
The microorganism is Pichia pastoris ( Pichia pastoris )
Way. According to claim 1,
The fermentation broth component index is at least one selected from the group consisting of Proteinase K concentration, glycerol concentration, methanol concentration, and microbial growth (absorbance)
Way. According to claim 1,
The two or more wavelength regions include a wavelength region of 9403.5 to 7498.1 cm -1 and a wavelength region of 6101.9 to 5446.2 cm -1 in the case of the Proteinase K concentration; In the case of the glycerol concentration, it includes a wavelength range of 8886.7 to 7745.0 cm -1 and a wavelength range of 5870.4 to 5420.0 cm -1 ; In the case of the methanol concentration, it includes a wavelength range of 8457.7 to 7498.1 cm -1 and a wavelength range of 6101.9 to 5446.2 cm -1 ; In the case of the microbial growth rate, including a wavelength region of 8535.3 to 7503.4 cm -1 and a wavelength region of 5797.2 to 5439.5 cm -1
Way. According to claim 1,
The calibration model is
(a) collecting n samples of the fermentation broth at regular time intervals;
(b) measuring raw spectra for calibration using a near-infrared spectrometer for m samples out of n samples collected;
(c) performing standard physicochemical analysis on the m samples from which the raw spectra were measured;
(d) statistically preprocessing the raw spectrum measured in step (b);
(e) among the spectra preprocessed in step (d), select two or more near-infrared wavelength regions specific to each of the one or more fermentation broth component indicators to be quantified, and calibrate calibration models from the spectra of the selected regions through chemometrics Building a step, and
(f) the result obtained by applying the nm sample not used for the raw spectrum measurement to the calibration calibration model developed in step (e) was compared with the physicochemical standard analysis result obtained in step (c) and calibrated; Obtained by a method comprising the step of optimizing,
wherein n and m are natural numbers greater than or equal to 50, and n is a natural number greater than m
Way. A fermentation system for producing proteins by microbial fermentation,
(i) Control that includes a calibration model obtained by using two or more near-infrared wavelength regions specific to each of the fermentation broth component indicators to monitor changes in one or more fermentation broth component indicators and monitors and controls the protein production process by microbial fermentation in real time including wealth
fermentation system. According to claim 11,
(ii) a fermentation section for microbial fermentation and
(iii) further comprising a near-infrared spectroscopy unit
fermentation system. According to claim 12,
The near-infrared spectroscopy unit is mounted in the form of a probe in the fermentation unit
fermentation system. According to claim 11,
The control unit automatically controls the fermentation process in real time in response to electrical signals of each component indicator generated by simultaneously monitoring changes in the one or more fermentation broth component indicators in real time.
fermentation system. According to claim 11,
The fermentation broth component index is at least one selected from the group consisting of Proteinase K concentration, glycerol concentration, methanol concentration, and microbial growth (absorbance)
fermentation system. According to claim 11,
The microorganism is a living modified organisms (LMO) strain.
fermentation system. According to claim 11,
The microorganism is the yeast Pichia pastoris ( Pichia pastoris )
fermentation system. According to claim 12,
The fermentation system is closed
fermentation system. According to claim 12,
The fermentation system can check the fermentation status of the protein production process by microbial fermentation through an on-tank measurement facility
fermentation system. According to claim 11,
The one or more fermentation broth component indicators include glycerol concentration, which is a carbon source for microbial growth
fermentation system. According to claim 15,
The two or more wavelength regions include a wavelength region of 9403.5 to 7498.1 cm -1 and a wavelength region of 6101.9 to 5446.2 cm -1 in the case of the Proteinase K concentration; In the case of the glycerol concentration, it includes a wavelength range of 8886.7 to 7745.0 cm -1 and a wavelength range of 5870.4 to 5420.0 cm -1 ; In the case of the methanol concentration, it includes a wavelength range of 8457.7 to 7498.1 cm -1 and a wavelength range of 6101.9 to 5446.2 cm -1 ; In the case of the microbial growth rate, including a wavelength region of 8535.3 to 7503.4 cm -1 and a wavelength region of 5797.2 to 5439.5 cm -1
fermentation system.

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