Culture system, culture method, and culture management apparatus

Abstract

To provide a culture system capable of reducing the time and cost required for culture process development.SOLUTION: The culture system 1 includes a calculation portion 12 that outputs additional experiment information which is information of explanatory variables to be changed in a culture experiment based on values of a plurality of explanatory variables, a plurality of culture chambers 30, and a control portion 13 that controls transfer of cells to be cultured between the plurality of culture chambers 30 based on the additional experiment information output by the calculation portion 12. The plurality of explanatory variables include one or more first explanatory variables whose values are changeable within a predetermined range in each of the plurality of culture chambers 30, and one or more second explanatory variables whose values are different from each other between at least one pair of culture chambers 30 included in the plurality of culture chambers 30, and when the explanatory variables to be changed are the second explanatory variables, the control portion 13 determines whether or not transfer of the cells to be cultured to a second culture chamber 30a different from the first culture chamber 30b is necessary, and controls the transfer of the cells to be cultured.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a culture system, a culture method, and a culture management device. [Background technology]

[0002] Methods for producing useful substances by culturing cells of plants, microorganisms, animals, etc. are used in various industries, including brewing, food, chemicals, and pharmaceuticals. For example, biopharmaceuticals, such as antibody drugs, contain substances produced by animal cells as their main components, and these substances can be obtained by culturing animal cells and isolating and purifying the target substances secreted into the culture medium.

[0003] When developing a production process for a useful substance, first, process conditions such as temperature, pH, and other culture conditions are optimized in a small scale (a few mL to several tens of liters) in laboratory experiments. Then, in developing the production process, the process is scaled up to the desired large scale (hundreds or thousands of liters or more) so as to maintain the production efficiency and product quality of the process optimized in the laboratory experiments. Known methods for scaling up include scaling up based on the law of similarity and scaling up using design space.

[0004] Scaling up using the law of similarity is generally performed based on geometric similarity (all dimensional ratios of the structure and layout of the mixing equipment are similar) and on the criteria of maintaining a constant mixing power requirement per unit liquid volume. Specifically, the vessel is designed so that the mixing power requirement, power consumption per unit liquid volume, mixing impeller rotation speed, mixing impeller diameter, liquid discharge rate, liquid circulation rate within the reactor, mixing impeller tip speed, and Reynolds number remain constant before and after scaling up. However, since it is difficult to scale up in a way that satisfies all of these criteria simultaneously, scaling up is performed by focusing on satisfying the most important mixing criteria (items that affect productivity and quality).

[0005] Here, Figure 14 is an explanatory diagram explaining the concept of scale-up of a fermentor using design space, which is one of the conventional techniques. In scale-up using design space, as shown in the left diagram of Figure 14, the culturable range is evaluated in laboratory experiments, centered around the optimal operating conditions (optimum point) obtained in small-scale laboratory experiments. Then, as shown in the right diagram of Figure 14, when designing a desired large-scale production-scale tank, the tank is designed so that there is a culturable range through fluid analysis. Generally, the culturable range in a large-scale production-scale tank is narrower than the culturable range in a small-scale tank.

[0006] Techniques that can be used to establish the above-mentioned production process are described in, for example, Patent Documents 1 and 2. Patent Document 1 describes a method for optimizing a culture-related process that optimizes a culture-related process related to cell culturing. This method includes an acquisition step, a variable parameter item identification step, an execution procedure generation step, an execution result acquisition step, an evaluation result acquisition step, and a storage step. In the acquisition step, a starting execution procedure that defines the operations performed in the culture-related process is acquired, and serves as a starting point for the search. In the variable parameter item identification step, variable parameter items for which variable parameter values ​​can be set are identified in the starting execution procedure. In the execution procedure generation step, variable parameter values ​​are set for the variable parameter items based on past execution performance results and their evaluation performance results to generate an execution procedure. In the execution result acquisition step, the execution results when an execution entity actually executes the execution procedure in the execution environment are acquired. In the evaluation result acquisition step, the evaluation results for the execution results are acquired by the evaluation result acquisition unit. In the storage step, the execution procedure, variable parameter values, execution results, and evaluation results are recorded in association with each other.

[0007] Patent Document 2 also describes a cell culture chamber for automating the production of antigen-specific T cells. The chamber has a bottom surface made of a first material to which cells adhere. The chamber has at least one additional surface made of a gas-permeable second material. The chamber has one or more inlet ports. The chamber also has one or more outlet ports. The one or more inlet ports and the one or more outlet ports are arranged to move fluid within the cell culture chamber at least partially along a flow path perpendicular to the bottom surface. Patent Document 2 also describes that the chamber has a surface area comparable to that of conventional well plates, such as 6-well and 24-well plates. Patent Document 2 also describes that the height of the cell culture chamber can be, for example, from 0.5 mm to 100 mm. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Japanese Patent Application Publication No. 2023-35238 [Patent Document 2] Special Publication No. 2019-522980 Summary of the Invention [Problem to be solved by the invention]

[0009] As mentioned above, in process development for production, the culture conditions are first optimized on a small scale. However, there are many items that need to be considered, such as temperature, pH, dissolved oxygen (DO) concentration, and oxygen aeration rate, and optimization requires numerous experiments, which are very costly and time-consuming. When considering the commercialization of useful substances, sufficient optimization may not be achieved, and process development may end with the minimum culture conditions necessary for commercialization.

[0010] Furthermore, when tank design is performed using the scale-up method, numerous small-scale culture experiments are conducted to optimize the process. However, when tank design is performed using the scale-up method, the culture experiment data required for scale-up must be collected again for each scale-up, which tends to lengthen the time required to build a production process from a small scale.

[0011] Furthermore, in small-scale culture experiments, the search range for specific culture condition items (explanatory variables) becomes broad. As a result, it is not possible to build a design space using a single type of culture device; two or more types of culture devices with different configurable ranges are required. For example, to evaluate shear stress due to stirring, multiple culture devices with different impeller shapes and rotation speeds, such as low shear stress range and high shear stress range, are required. In this case, when conducting optimization experiments using machine learning, operations such as replacing the impellers during cultivation are required, making it difficult to optimize culture conditions that require cultivation in a sterile environment.

[0012] The culture-related process optimization method described in Patent Document 1 allows for optimization of culture conditions using machine learning, but does not allow for switching of culture equipment based on explanatory variables. Therefore, this method can be difficult to use when optimizing culture conditions that require culture in a sterile environment. Furthermore, the cell culture chamber described in Patent Document 2 automates the production of antigen-specific T cells and does not take into consideration scaling up from a small scale to a large scale. Furthermore, while the cell culture chamber described in Patent Document 2 is configured to allow for the transfer of culture medium, it is considered difficult to apply machine learning related to scale-up, such as multi-objective optimization. Therefore, even when Patent Documents 1 and 2 are taken into consideration, it cannot be said that conventional techniques have sufficiently reduced the time and costs required for culturing process development.

[0013] The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a culture system, a culture method, and a culture management device that can reduce the time and cost required for developing a culture process. [Means for solving the problem]

[0014] In order to solve the above problems, the present invention provides culture optimization in a culture system that includes two or more culture tanks with different settable ranges for a specific culture condition item (explanatory variable), selects the culture tank that is suitable for the culture conditions proposed by an optimization tool, and has equipment and a control mechanism for transferring a culture solution containing cells to the selected culture tank.The details are as follows.

[0015] The culture system according to the present invention, which has solved the above-mentioned problems, is a culture system for conducting experiments to search for culture conditions in cell culture, and includes a calculation unit that searches for values ​​of a plurality of explanatory variables that will cause a target variable in the culture experiment to satisfy a predetermined condition based on experimental data from the culture experiment, in which a plurality of explanatory variables related to the culture conditions are changed, and outputs additional experimental information, which is information on the explanatory variables to be changed in the culture experiment, based on the searched values ​​of the plurality of explanatory variables; a plurality of culture vessels for the culture experiment; and controls the culture conditions in the plurality of culture vessels based on the additional experimental information output by the calculation unit, and controls the transfer of cells to be cultured between the plurality of culture vessels in accordance with the determination. and a control unit configured to control the transfer of the cells to be cultured from the first culture tank to the second culture tank when the explanatory variable to be changed in the culture experiment performed in a first culture tank of the plurality of culture tanks in the additional experimental information is the second explanatory variable, and when the result indicates that the cells to be cultured need to be transferred, the control unit controls the transfer of the cells to be cultured from the first culture tank to the second culture tank. [Effects of the Invention]

[0016] According to the present invention, it is possible to provide a culture system, a culture method, and a culture management device that can reduce the time and cost required for developing a culture process. Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is an explanatory diagram illustrating an overview of a culture system 1 according to the present embodiment. [Figure 2] FIG. 1 is a flow diagram illustrating an optimization process using machine learning. [Figure 3] FIG. 1 is a schematic diagram illustrating an example of the configuration of a general culture device 300. [Figure 4] FIG. 10 is an explanatory diagram showing how to deal with a case where the settable range of the explanatory variable is narrow. [Figure 5] FIG. 2 is a configuration diagram illustrating an example of the configuration of a plurality of culture vessels 30 used in the culture system 1. [Figure 6] FIG. 2 is an explanatory diagram showing the state before cells to be cultured are transferred from a first culture tank 30a to a second culture tank 30b. [Figure 7] FIG. 1 is an explanatory diagram showing a state in which cells to be cultured are being transferred from a first culture tank 30a to a second culture tank 30b. [Figure 8] FIG. 10 is an explanatory diagram showing the state after cells to be cultured have been transferred from a first culture tank 30a to a second culture tank 30b. [Figure 9A] This is a graph of a function used in a test calculation in multi-objective optimization, and is a three-dimensional representation of the graph. [Figure 9B] 9B is a graph of a function used in a test calculation in multi-objective optimization, which is a two-dimensional representation of the graph in FIG. 9A. [Figure 9C] This is a graph of a function used in a test calculation in multi-objective optimization, and is a three-dimensional representation of the graph. [Figure 9D] 9C is a graph of a function used in a test calculation in multi-objective optimization, which is a two-dimensional representation of the graph in FIG. 9C. [Figure 10] 1 is a graph showing an example of a Pareto front for a response variable M (e.g., productivity) and a response variable N (e.g., quality). [Figure 11] FIG. 1 is a flow chart illustrating the contents of a culture method according to one embodiment of the present invention. [Figure 12] FIG. 2 is an explanatory diagram illustrating an overview of a culture management device 120. [Figure 13] 1 is a graph showing the relationship between the number of culture days (days) and the antibody concentration under conditions before optimization (conditions for the first cycle) and conditions after optimization (culture conditions for the 20th cycle). [Figure 14] FIG. 1 is an explanatory diagram illustrating the concept of scale-up of a fermentor using design space, which is one of the prior art techniques. DETAILED DESCRIPTION OF THE INVENTION

[0018] In order to achieve the above-mentioned objects, the present invention has the following configuration. Note that the objects, features, advantages, and ideas of the present invention will be apparent to those skilled in the art from the description in this specification, and those skilled in the art will be able to easily reproduce the present invention from the description in this specification. The specific embodiments of the invention described below show preferred embodiments of the present invention and are shown for illustrative or explanatory purposes, and are not intended to limit the present invention thereto. It will be apparent to those skilled in the art that various changes and modifications can be made based on the description in this specification within the spirit and scope of the present invention disclosed in this specification.

[0019] An embodiment of the present invention will be described below with reference to the drawings. However, the embodiment described below is an example for explaining the present invention, and appropriate omissions and simplifications have been made for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. The position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc., in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings.

[0020] When there are multiple components with the same or similar functions, they may be described using the same reference numeral with different subscripts. Also, when there is no need to distinguish between these multiple components, the subscripts may be omitted. Components with the same or similar functions may be described using the same reference numeral, and duplicate descriptions may be omitted.

[0021] In the embodiments, processing performed by executing a program may be described. Here, a computer executes the program using a processor (e.g., a CPU or a GPU) and performs processing defined by the program using storage resources (e.g., memory) and interface (I / F) devices (e.g., communication ports). Therefore, the entity performing the processing by executing the program may be the processor. Similarly, the entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The entity performing the processing by executing the program may be any computing unit, and may include a dedicated circuit that performs specific processing. Here, the dedicated circuit may be, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a CPLD (Complex Programmable Logic Device).

[0022] A program may be installed on a computer from a program source. The program source may be, for example, a program distribution server or a computer-readable storage medium. When the program source is a program distribution server, the program distribution server may include a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers. In addition, in an embodiment, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0023] <Culture system> A culture system according to one embodiment of the present invention relates to a data collection method and a small culture device for data collection that are used to establish a manufacturing process for mass-culturing cells for the production of useful substances, for example. First, an overview of one embodiment of the culture system 1 according to the present invention will be described with reference to FIG. 1. FIG. 1 is an explanatory diagram illustrating an overview of the culture system 1 according to this embodiment. The culture system 1 shown in FIG. 1 performs experiments to explore culture conditions for cell culture.

[0024] As shown in FIG. 1, the culture system 1 includes a calculation unit 12, a plurality of culture vessels 30, and a control unit 13. The calculation unit 12 and the control unit 13 are components of the computer 10. The calculation unit 12 and the control unit 13 are configured, for example, by a central processing unit (CPU 11) that executes a program that controls the operation of each configuration, function, and processing unit.

[0025] That is, the culture system 1 includes a computer 10, as shown in Fig. 1. The computer 10 includes a CPU 11, a RAM 14, a ROM 15, a HDD 16, a communication I / F 17, an input / output I / F 18, and a media I / F 19. The communication I / F 17 is connected to a communication circuit 20. The input / output I / F 18 is connected to an input / output device 21. The media I / F 19 reads and writes data from a recording medium 22. The ROM 15 stores application programs executed by the CPU 11, various data, etc. The HDD 16 stores experimental data, various data, control programs, etc. as appropriate. The CPU 11 executes application programs loaded into the RAM 14 to realize various functions. The culture system 1 may be configured using one computer 10, or may be configured using multiple computers 10 connected via a communication circuit 20.

[0026] In this embodiment, the calculation unit 12 searches for values ​​of multiple explanatory variables that will cause the objective variable in the culture experiment to satisfy a predetermined condition, based on experimental data from a culture experiment conducted by changing multiple explanatory variables related to the culture conditions. The calculation unit 12 also outputs additional experimental information, which is information on explanatory variables to be changed in the culture experiment, based on the searched values ​​of the multiple explanatory variables. The search for values ​​of the multiple explanatory variables can be performed using machine learning, specifically Bayesian optimization or multi-objective optimization. The search for values ​​of multiple explanatory variables and machine learning will be described later. The objective variable and explanatory variables will also be described later.

[0027] Furthermore, multiple culture tanks 30 are used in the culture experiment described above. The multiple culture tanks 30 include, for example, multiple first culture tanks 30a and multiple second culture tanks 30b. The second culture tanks 30b may be culture tanks separate from the first culture tank 30a, and the number of second culture tanks 30b is not particularly limited. For example, while FIG. 1 illustrates a state in which one second culture tank 30b is connected to one first culture tank 30a, two or more second culture tanks 30b may be connected. In this case, the first culture tank 30a and two or more second culture tanks 30b may be connected in series, such as first culture tank 30a-second culture tank 30b-second culture tank 30b, or may be connected to the first culture tank 30a in the order of second culture tank 30b-first culture tank 30a-second culture tank 30b. The plurality of culture vessels 30 can all be small-scale with a volume of several mL to several tens of liters.

[0028] The control unit 13 controls the culture conditions in the plurality of culture tanks 30 based on the additional experimental information output by the calculation unit 12, and also controls the transfer of cells to be cultured between the plurality of culture tanks 30 based on the judgment described below.

[0029] Here, the multiple explanatory variables include one or more first explanatory variables whose values ​​can be changed within a predetermined range in each of the multiple culture tanks 30. That is, the first explanatory variables are variables whose values ​​can be changed within the same predetermined numerical range (for example, the same predetermined numerical range of 1 to 100) in all of the multiple culture tanks 30. The multiple explanatory variables include one or more second explanatory variables whose possible values ​​differ between at least one set of culture tanks 30 included in the multiple culture tanks 30 (specifically, a set of a first culture tank 30a and a second culture tank 30b (including the case where there are multiple second culture tanks 30b)). That is, the second explanatory variables can take different values ​​between a set of culture tanks 30, for example, one (first culture tank 30a) has a value in the range of 1 to 40, the other (second culture tank 30b) has a value in the range of 35 to 75, and if there is another one (second culture tank 30b), the other (second culture tank 30b) has a value in the range of 70 to 100.

[0030] Then, when the explanatory variable to be changed in the culture experiment to be performed in the first culture tank 30a among the plurality of culture tanks 30 in the additional experiment information is the second explanatory variable, the control unit 13 determines whether or not it is necessary to transfer the cells to be cultured to a second culture tank 30b, which is different from the first culture tank 30a among the plurality of culture tanks 30. When the control unit 13 determines that the transfer is necessary, it controls the transfer of the cells to be cultured from the first culture tank 30a to the second culture tank 30b. Note that the case where the control unit 13 determines that the transfer is necessary is, for example, when the conditions (numerical range) of the second culture tank 30b do not fall within the conditions (numerical range) of the first culture tank 30a, or when control becomes difficult.

[0031] Here, a method for searching for suitable values ​​of a plurality of explanatory variables in the culture system 1 having the above configuration, that is, an optimization process using machine learning, will be described. Figure 2 is a flow diagram explaining the optimization process using machine learning. As shown in Figure 2, the optimization process using machine learning consists of [1] experimental condition setting, [2] culture experiment, [3] analysis, [4] input to the calculation unit (including recording), [5] calculation to propose the next experimental condition, [6] presentation of the next experimental condition, and [7] termination condition judgment. The processes are carried out in order from [1], and if the termination condition is not met in [7], the cycle returns to [1] and repeats. Each component is explained below.

[0032] [1] Setting experimental conditions In the optimization of culture conditions according to this embodiment, the optimal conditions are reached by repeating the cycles of [1] to [7] above. In the first cycle of experiments, the experimental conditions can be set arbitrarily. The experimental conditions may be standard experimental conditions used in literature, or may be one of the culture conditions used in the past. Furthermore, if results under multiple culture conditions are already available, this data can be input into "(4) Input to the calculation unit (including recording)" (described below) and used as learning data for the calculation in (5). Then, depending on the learning results, the culture conditions can be input in "(6) Presentation of the next experimental conditions." The culture experiment results data used for learning in this case can be a group of data obtained in previous developments, or the results of individual culture experiments for groups of experimental conditions with evenly spaced experimental conditions within a predetermined search range can be obtained and used as learning data. The latter learning data is desirable because it enables efficient search for the optimal point in "(5) Calculation for proposing the next experimental conditions" and reduces the number of cycles.

[0033] [2] Culture experiment In culture experiments, it is necessary to have a mechanism that allows the culture conditions (explanatory variables) to be optimized to be set arbitrarily and that can analyze the target variables. Specific examples of explanatory variables and response variables are given below using the scale-up of the culture tank 30 as an example, but are not limited to the items described here.

[0034] 1.Selection of dependent and explanatory variables The objective variable is the productivity or quality of the substance to be produced. Examples of the objective variable include, but are not limited to, the concentration of the product, the total yield of the product, and the purity of the product. The objective variable does not change before and after scaling up. Explanatory variables are divided into variables that do not change before and after scale-up (the first explanatory variables described above) and variables that can be set and controlled before scale-up but are not set or controlled after scale-up (the second explanatory variables described above). The first explanatory variable is a parameter that is set and controlled after scale-up, such as temperature, pH, and DO concentration. Examples of the second explanatory variable include shear stress, bubble diameter, Kolmogorov scale, and nutrient concentration distribution. At least one of these can be selected as the second explanatory variable. By setting these as the second explanatory variables, it is possible to appropriately search and optimize a wide range of the set but uncontrolled variables after scale-up.

[0035] FIG. 3 is a schematic diagram illustrating an example of the configuration of a typical culture apparatus 300. This culture apparatus 300 is designed for perfusion culture. The culture apparatus 300 is composed of a culture vessel 301, a mechanism 302 for controlling the addition of medium components via feed, a mechanism for removing the cell-containing culture medium via bleed (not shown), a cell separation mechanism 303 for recovering target substances while leaving the cells in the culture vessel 301, and a mechanism (not shown) for controlling the temperature, pH, dissolved oxygen, and mixability within the culture vessel 301. In perfusion culture, appropriate control of feed, cell separation, and bleed allows not only the temperature, pH, dissolved oxygen, and mixability but also nutrient concentrations, waste product concentrations, and cell density to be maintained at steady states. Perfusion culture is advantageous because it provides higher quality data than conventional batch and fed-batch cultures. In batch culture and fed-batch culture, fluctuations in cell density occur due to cell consumption of nutrients and accumulation of waste products, as well as cell growth. However, if these fluctuations are within acceptable limits, culture equipment for batch culture and fed-batch culture can also be used.

[0036] Among the explanatory variables in the general culture apparatus 300 (perfusion culture, batch culture, fed-batch culture), temperature and pH can be set and controlled over a relatively wide range. However, when the explanatory variables are shear stress or aeration bubble diameter, the range of values ​​that can be set is limited by the shape of the agitator blades and the pore size of the sparger used for aeration. Therefore, for example, when shear stress is used as an explanatory variable, pitched paddles are used in low shear stress regions, flat paddles in medium shear stress regions, and disc turbine blades in high shear stress regions, and the blades must be replaced depending on the culture conditions. Regarding aeration, spargers with different pore sizes must also be replaced. However, the culture medium during cultivation must be protected from external contamination, making it a closed system. Therefore, replacing the agitator blades or sparger during cultivation is not appropriate.

[0037] In view of this, the culture system 1 according to this embodiment uses multiple culture tanks 30 with different settable ranges for a specific explanatory variable (see FIGS. 1 and 4). FIG. 4 is an explanatory diagram showing how to handle a case where the settable range of the explanatory variable is narrow. As shown in FIG. 4, the culture system 1 has culture tanks 30 corresponding to conditions A (a≦x≦b), B (c≦x≦d), and C (e≦x≦f) when controlling the second explanatory variable, for example, and switches between culture tanks 30 appropriate for the second explanatory variable indicated by x. Among the aforementioned a to f, the magnitude relationships of a to e as numerical values ​​are a≦c≦b and c≦e≦d. In this example, the numerical ranges of conditions A to C are set to overlap. While FIG. 4 illustrates the second explanatory variable as an example, the first explanatory variable can also be handled in a similar manner.

[0038] In the present embodiment, for example, as conditions A to C described above, a culture vessel 30 equipped with at least one of pitched paddles, flat paddles, and disk turbine blades as stirring blades for controlling shear stress may be prepared, and the control unit 13 may determine which culture vessel 30 is to be used. In this way, the shear stress, which is a variable that is set but not controlled after scale-up, can be appropriately explored and optimized within a wide range. Similarly, the bubble diameter can be appropriately explored and optimized within a wide range by preparing multiple culture vessels 30 equipped with spargers with different pore sizes.

[0039] For example, when a value of an explanatory variable suitable for the second culture tank 30b, which is different from the first culture tank 30a used in the N-1th cycle, is presented in the Nth cycle (for example, when an instruction to change from condition A to condition B is given), the culture system 1 transfers the culture medium containing cells from the first culture tank 30a to the second culture tank 30b. In this embodiment, as shown in FIG. 4, when a wide range of optimization conditions is searched for for a specific explanatory variable, multiple culture tanks 30 with different settable ranges are provided, and when "(6) Presentation of next experimental conditions" is performed, it is determined which culture tank 30 should be used for the specific explanatory variable. The explanatory variable of the characteristic is not limited to one, and the same applies when there are multiple explanatory variables.

[0040] 5 is a configuration diagram illustrating an example of the configuration of the plurality of culture vessels 30 used in the culture system 1. As described above, the plurality of culture vessels 30 have different settable regions for a specific explanatory variable. As shown in Fig. 5, each of the plurality of culture tanks 30 preferably includes a pipe 31a for supplying gas to the first culture tank 30a and a gas supply valve 31 provided thereon. Each of the plurality of culture tanks 30 preferably includes a pipe 32c for connecting the first culture tank 30a and the second culture tank 30b and transfer valves 32a and 32b provided thereon. Each of the plurality of culture tanks 30 preferably includes a pipe 33a for discharging gas from the second culture tank 30b and a gas discharge valve 33 provided thereon. The gas supplied via the gas supply valve 31 is preferably sterilized using an air filter or the like. When multiple culture tanks 30 are configured in this manner, when transferring cells to be cultured from the first culture tank 30a to the second culture tank 30b, the control unit 13 first opens the gas supply valve 31 to start supplying gas to the first culture tank 30a. Thereafter, the control unit 13 opens the transfer valves 32a and 32b and the gas exhaust valve 33 to transfer the cells to be cultured. In this way, the culture medium CM containing cells can be suitably and quickly transferred from the first culture tank 30a to the second culture tank 30b while maintaining a sterile state by the gas pressure. The plurality of culture vessels 30 is not limited to the embodiment shown in FIG. 5, as long as an appropriate culture vessel 30 can be selected each time "(6) Presentation of next experimental conditions" is performed.

[0041] As shown in FIG. 5, the plurality of culture tanks 30 preferably include a feed mechanism 34, a cell separation mechanism 35, a bleeding mechanism 36, and control mechanisms (not shown) for temperature, pH, and dissolved oxygen. The feed mechanism 34 adds medium components by feeding. The cell separation mechanism 35 recovers the target substance while leaving the cells in the culture tank 30. The bleeding mechanism 36 removes the culture solution CM containing the cells by bleeding. The temperature, pH, and dissolved oxygen control mechanisms control the temperature, pH, and dissolved oxygen. These mechanisms are equipped with valves in the connecting pipes to control the flow rate of gas, culture solution CM, etc. as needed. The plurality of culture tanks 30 have these configurations, so that culture can be carried out suitably in any of the culture tanks 30.

[0042] An example of a method and apparatus for transferring cells to be cultured from a first culture tank 30a to a second culture tank 30b is shown in Figures 6 to 8. Figure 6 is an explanatory diagram showing the state before the cells to be cultured are transferred from the first culture tank 30a to the second culture tank 30b. Figure 7 is an explanatory diagram showing the state during the transfer of the cells to be cultured from the first culture tank 30a to the second culture tank 30b. Figure 8 is an explanatory diagram showing the state after the cells to be cultured have been transferred from the first culture tank 30a to the second culture tank 30b.

[0043] As shown in FIG. 6, before transfer, the valves (gas supply valve 31, transfer valves 32a and 32b, and gas discharge valve 33) of the first fermenter 30a and the second fermenter 30b are all closed. 7, the gas supply valve 31 on the first fermentation tank 30a side (before transfer) is opened to allow gas to pass through the first fermentation tank 30a, thereby increasing the internal pressure of the first fermentation tank 30a. Next, the transfer valves 32a and 32b and the gas exhaust valve 33 are opened. This starts the transfer of the culture solution CM to the second fermentation tank 30b. Then, as shown in FIG. 8, after the culture solution CM has been completely transferred to the second culture tank 30b, all the valves (gas supply valve 31, transfer valves 32a and 32b, and gas discharge valve 33) are closed. It is desirable that the first culture tank 30a, which has been emptied by this transfer, be equipped with a mechanism for aseptically cleaning the inside of the first culture tank 30a. For example, a sterile buffer solution is added from the outside, stirred in the first culture tank 30a, and then discharged outside the first culture tank 30a.

[0044] When the process is optimized for scale-up using the first fermenter 30a and the second fermenter 30b described above, data is collected through a culture experiment as follows.

[0045] 2. Data collection through culture experiments A culture experiment is performed using the cells to be used, with the first explanatory variable and second explanatory variable selected in "1. Selection of Objective Variable and Explanatory Variable" above. The first explanatory variable is varied as in the past, with each parameter varied. The objective variable under those conditions is then measured. The second explanatory variable is a parameter that has not been measured in small-scale fermentors in the past for optimizing the culture process. Examples of methods for measuring each second explanatory variable and the device functions for measurement are described below. An example of the second explanatory variable and data collection using it is shown in Figure 4.

[0046] Shear stress Shear stress is distributed within the culture tank 30 and is difficult to measure directly. Therefore, the shear stress distribution when the agitation rotation speed is changed is quantified by fluid analysis (simulation) based on the structure of the culture tank 30 used in the culture experiment. Conventionally, tilted paddle-type agitation blades are often used, but in this case, even at the maximum rotation speed, the shear stress does not reach the magnitude required for scale-up. Therefore, it is desirable to increase the diameter of the agitation blades and use vertically long flat paddles or disk turbine blades.

[0047] Nutrient concentration Nutrient concentration is controlled by measuring the nutrient concentration in the culture solution CM and adding a nutrient-containing medium to achieve a set value based on that value. Examples of nutrients include glucose, glutamine, and glutamic acid. Nutrient concentration can be measured by sampling the culture solution CM and using high-performance liquid chromatography (HPLC) or liquid chromatography mass spectrometry (LC-MS), or by in-line sensing using Raman spectroscopy. The values ​​measured in this way are input into a calculation device or calculation unit 12, which calculates the concentration. The concentration is controlled by appropriately operating the addition pump and the culture solution CM extraction pump according to the calculated concentration.

[0048] Waste concentration The waste product concentration is controlled by measuring the waste product concentration in the culture medium CM and adding medium containing substances equivalent to the waste products (i.e., substances that are the basis of the waste products) to achieve a set value based on that value. Examples of waste products include lactic acid and ammonia. The waste product concentration can be measured by sampling the culture medium CM using HPLC or LC-MS, or by in-line sensing using Raman spectroscopy. The values ​​measured in this way are input into a calculation device or calculation unit 12, which calculates the concentration. The concentration is controlled by appropriately operating the addition pump and the culture medium CM extraction pump depending on the concentration.

[0049] ·Viable cell number concentration The viable cell concentration is controlled by measuring the viable cell concentration in the culture medium CM and adjusting it to a set value based on that value. If the viable cell concentration is higher than the set value, the culture medium CM containing the viable cells is extracted using the bleeding mechanism 36, and an equal volume of medium is added using the feed mechanism 34 to lower the concentration. If the viable cell concentration is lower than the set value, the cells are cultured until they grow to the set viable cell number. The viable cell count can be measured by sampling the culture medium CM and counting the cells using a microscope or an image, or by counting inline using capacitance or turbidity.

[0050] [3] Analysis A quantitative analysis is performed on the target variable to be maximized or minimized. The target variable corresponds to, for example, the productivity (concentration, yield relative to input cost, etc.) or quality (activity or molecular structure of the useful substance) of the useful substance. The productivity is quantitatively evaluated by sampling the culture solution CM in the culture tank 30 and measuring the concentrations of components in the culture solution CM. Similarly, the quality is quantitatively evaluated by sampling the culture solution CM and analyzing it with HPLC, LC-MS, or the like. In addition to the above-mentioned analysis by sampling, the inside of the culture tank 30 can also be measured using an in-line sensor using pH, DO concentration, turbidity, capacitance, etc. Also, by measuring the exhaust gas and input gas from the culture tank 30, it is possible to measure and analyze the cell state, and these measurement results can also be used as the target variable.

[0051] [4] Input to the calculation unit (including recording) For the calculation of "(5) Calculation for proposing the next experimental conditions," the results of "(1) Setting the experimental conditions" and "(3) Analysis" are input to the calculation unit 12. When inputting these results to the calculation unit 12, these results can be recorded and used as learning data.

[0052] [5] Calculations for proposing the next experimental conditions As an optimization method using machine learning, Bayesian optimization can be used when there is one objective variable, and multi-objective optimization can be used when there are multiple objective variables. The above method constructs a model based on training data and presents explanatory variables that result in a higher (or lower) state of the dependent variable, but it is desirable to display or record the estimated values ​​obtained by the model at this time.

[0053] An example of analysis using multi-objective optimization is described below. 9A to 9D are graphs of functions used in test calculations in multi-objective optimization. Note that FIGS. 9A and 9C are three-dimensional representations of the graphs. FIG. 9B is a two-dimensional representation of the graph in FIG. 9A. FIG. 9D is a two-dimensional representation of the graph in FIG. 9C. FIG. 10 is a graph showing an example of a Pareto front for objective variable M (e.g., productivity) and objective variable N (e.g., quality). Note that the Pareto front refers to a line, or in higher dimensions, a surface, formed by a set of Pareto optimal solutions. In FIG. 10, the line (not shown) connecting the optimal solutions (circles) is the Pareto front.

[0054] Assume that there are two objective variables (productivity and quality), and they have the relationships shown in FIGS. 9A to 9D according to the settings of the first explanatory variable and the second explanatory variable of the culture conditions. That is, assume that productivity and quality increase and decrease variously according to the culture conditions, and at certain conditions, the maximum value can be obtained or a value above the level can be obtained, but at other conditions, a value below the level can be obtained. Regarding this, when performing multi-objective optimization, as shown in FIG. 10, the Pareto front indicated by the optimal solution (○) can be obtained by trial experiments with a small number of experimental runs. By using this Pareto front, it becomes possible for the user to select the culture conditions that maximize each of them within the balance of productivity and quality.

[0055] 〔6〕Presentation of the following experimental conditions Display the results calculated in "〔5〕Calculation for the following experimental condition proposal" or record them in "〔7〕End condition judgment".

[0056] 〔7〕End condition judgment The end judgment ends when the values of the objective variables preset by the user are satisfied. If not satisfied, it returns to 〔1〕 and starts the next cycle with "〔6〕Presentation of the following experimental conditions" as the new input value. For example, if the user has preset the objective variable M (e.g., productivity) to be x or more and the objective variable N (impurity concentration) to be y or less, when the values of the objective variables in the result of "〔3〕Analysis" are Objective variable M ≥ x and objective variable N ≤ y it ends, and when objective variable M < x or objective variable N > y it returns to 〔1〕.

[0057] On the other hand, the user does not have to specify the value of the objective variable at the end; instead, they can specify either the maximum or minimum value for the objective variable. In this case, if the objective variable in the "(3) Analysis" results in the (N-1)th cycle is deemed equivalent to the Nth cycle, the process can be considered to have ended. The user can specify what constitutes equivalence (the percentage difference). Furthermore, the equivalence determination (the percentage difference) can be set to a difference value that is not considered significant due to variability in the analysis or experiment. The end condition can also be when the culture conditions in "(1) Setting Experimental Conditions" and "(6) Presentation of Next Experimental Conditions" are unchanged. Furthermore, the process can be considered complete when the objective variable estimated from the model in "(5) Calculation for proposing the next experimental conditions" matches the objective variable value in the "(3) Analysis" result within a range that is not significantly different. The above explanation is based on an example in which there are two types of objective variables, but the same procedure can be carried out in cases in which there is one type of objective variable or three or more types of objective variables.

[0058] In the culture system 1 described above, the control unit 13 preferably determines the second culture vessel 30b to which the cells to be cultured are transferred based on the type and value of the second explanatory variable included in the additional experimental information. In this way, the culture experiment can be performed using the second culture vessel 30b that matches the type and value of the second explanatory variable included in the additional experimental information, thereby enabling the culture conditions to be suitably optimized.

[0059] Furthermore, in the culture system 1 described above, it is preferable that the control unit 13 performs control to change the first explanatory variable in the culture experiment performed in the first culture tank 30a when the explanatory variable to be changed in the culture experiment performed in the first culture tank 30a among the multiple culture tanks 30 in the additional experiment information is the first explanatory variable. In this case, the culture experiment is performed only in the first culture tank 30a, and the culture conditions can be optimized.

[0060] As described above, the culture system 1 allows for the culture to be continued by changing the culture tank 30 midway through experimental optimization using machine learning, enabling the optimization of culture conditions, including scale-up, in a short period of time and with a small number of culture experiments. This allows the culture system 1 to reduce the time and cost required for culture process development.

[0061] <Culture method> Next, a culture method according to one embodiment of the present invention (hereinafter, sometimes simply referred to as a "main culture method") will be described with reference to Figure 11. Figure 11 is a flow chart illustrating the contents of the main culture method.

[0062] This culture method is used to conduct experiments to explore culture conditions for cell culture. 11, the main culture method includes a calculation step S111 and a control step S112. The calculation step S111 corresponds to the above-mentioned calculation unit 12. The control step S112 corresponds to the above-mentioned control unit 13.

[0063] In the calculation step S111, values ​​of a plurality of explanatory variables are searched for such that the objective variable in the culture experiment satisfies a predetermined condition based on the experimental data of the culture experiment performed by changing the plurality of explanatory variables related to the culture conditions. In addition, in the calculation step S111, additional experimental information is output, which is information on the explanatory variables to be changed in the culture experiment based on the searched values ​​of the plurality of explanatory variables. As described above, the multiple explanatory variables include one or more first explanatory variables whose values ​​can be changed within a predetermined range in each of the multiple culture tanks 30. The multiple explanatory variables also include one or more second explanatory variables whose possible values ​​differ between at least one pair of culture tanks 30 included in the multiple culture tanks 30. In the calculation step S111, when searching for values ​​of multiple explanatory variables, Bayesian optimization can be used as a machine learning optimization method when there is one objective variable, and multi-objective optimization can be used when there are multiple objective variables.

[0064] In the control step S112, the culture conditions in the multiple culture tanks 30 for the culture experiment are controlled based on the additional experimental information output in the calculation step S111, and the transfer of cells to be cultured between the multiple culture tanks 30 is controlled according to the judgment described below. That is, in this control step S112, when the explanatory variable to be changed in the culture experiment to be performed in the first culture tank 30a among the plurality of culture tanks 30 in the additional experiment information is the second explanatory variable, a determination is made as to whether or not the cells to be cultured need to be transferred to a second culture tank 30b different from the first culture tank 30a among the plurality of culture tanks 30. Then, in control step S112, when it is determined that the transfer of the cells to be cultured is necessary from the first culture tank 30a to the second culture tank 30b is controlled.

[0065] According to this culture method, the culture vessel 30 can be changed midway through experimental optimization using machine learning, allowing for continued culture, and therefore, culture conditions, including scale-up, can be optimized in a short period of time with a small number of culture experiments. This allows this culture method to reduce the time and cost required for culture process development.

[0066] <Culture management device> Next, a culture management device according to one embodiment of the present invention will be described with reference to Fig. 12. Fig. 12 is an explanatory diagram illustrating an overview of the culture management device 120. The culture management device 120 has a configuration similar to that of the computer 10 of the culture system 1 described above, but differs in that it does not control the culture conditions in the multiple culture tanks 30.

[0067] 12 searches for culture conditions for cell culture. The culture management device 120 includes a calculation unit 121 and a control unit 131. As described above, the calculation unit 121 and the control unit 131 in the culture management device 120 are also configured with, for example, a CPU 11 that executes programs that control the operation of each configuration, function, and processing unit.

[0068] 12, the culture management device 120 includes a CPU 11, a RAM 14, a ROM 15, an HDD 16, a communication I / F 17, an input / output I / F 18, and a media I / F 19. The communication I / F 17 is connected to a communication circuit 20. The input / output I / F 18 is connected to an input / output device 21. The media I / F 19 reads and writes data from a recording medium 22. The ROM 15 stores application programs executed by the CPU 11, various data, etc. The HDD 16 stores experimental data, various data, control programs, etc. as appropriate. The CPU 11 executes application programs loaded into the RAM 14 to realize various functions. The culture management device 120 may be configured with a single computer, or may be configured with multiple computers connected via a communication circuit 20.

[0069] The calculation unit 121 searches for values ​​of a plurality of explanatory variables that will cause the objective variable in the culture experiment to satisfy a predetermined condition, based on experimental data from a culture experiment in which the plurality of explanatory variables related to the culture conditions are changed. Furthermore, the calculation unit 121 outputs additional experimental information, which is information on the explanatory variables to be changed in the culture experiment, based on the searched values ​​of the plurality of explanatory variables. As described above, the multiple explanatory variables include one or more first explanatory variables whose values ​​can be changed within a predetermined range in each of the multiple culture tanks 30. The multiple explanatory variables also include one or more second explanatory variables whose possible values ​​differ between at least one pair of culture tanks 30 included in the multiple culture tanks 30. In the calculation unit 121, when searching for values ​​of multiple explanatory variables, Bayesian optimization can be used as a machine learning optimization method when there is one objective variable, and multi-objective optimization can be used when there are multiple objective variables.

[0070] The control unit 131 controls the transfer of cells to be cultured between a plurality of culture vessels 30 for a culture experiment. In other words, this control unit 131 determines whether or not it is necessary to transfer the cells to be cultured to a second culture tank 30b, which is different from the first culture tank 30a, among the multiple culture tanks 30, based at least on whether or not the explanatory variable to be changed in the culture experiment conducted in the first culture tank 30a among the multiple culture tanks 30 in the additional experimental information is the second explanatory variable described above.

[0071] The culture management device 120 allows for the culture to be continued by changing the culture tank 30 midway through experimental optimization using machine learning, making it possible to optimize culture conditions, including scale-up, in a short period of time and with a small number of culture experiments. This allows the culture management device 120 to reduce the time and cost required for culture process development. [Example]

[0072] [Optimization of culture conditions using CHO cells] Using the aforementioned culture-related process optimization system (culture system 1), we optimized the culture conditions for a CHO cell line that produces tissue plasminogen activator (tPA), a glycoprotein. Details are described below.

[0073] (Selection of response and explanatory variables) Temperature, pH, and DO concentration were selected as explanatory variables (first explanatory variables) that can be set to a wide range of values. Shear stress was selected as an explanatory variable (second explanatory variable) that has a narrower settable range. In descending order of shear stress, a culture tank 30 equipped with a disc turbine blade, a culture tank 30 equipped with a flat paddle, and a culture tank 30 equipped with a pitched paddle were prepared. Furthermore, antibody concentration was selected as the objective variable. The culture conditions were optimized using multi-objective optimization.

[0074] (Cells and media) For the culture experiments, tPA-producing Chinese hamster ovary cells (CHO cells; CRL-9606 cells) (adherent / suspension culture compatible) were purchased from the American Type Culture Collection (ATCC) and used. The medium used was Ham's F12 basal medium supplemented with fetal bovine serum (FBS) (final concentration 10%) and the antibiotics penicillin and streptomycin.

[0075] (Preparation of suspension cells (adaptation from adherent cells to suspension cells)) CHO cells cultured in an adherent state in a culture flask were detached with trypsin and centrifuged (room temperature, 500 × g, 5 minutes) to remove the trypsin solution. Then, the cells were diluted with Ham's F12 medium and cultured in a spinner flask at 1 × 10 5 The cells were cultured in a 100 mL incubator (37°C, 5% CO2, humidity 90% or higher) with stirring at a cell concentration of 1 / mL. In this experiment, cells that had been adapted to suspension cells using the above procedure were used.

[0076] (Culture operation) The culture conditions were optimized using a culture system 1 equipped with three of the 1 L-volume culture vessels 30. Aseptic sampling was carried out for each cycle, and the components of the culture solution CM were analyzed.

[0077] (Analysis of culture medium components) The sampled culture medium CM was used to (1) count the number of viable cells, (2) analyze the medium components (glucose, glutamine, lactate, and ammonia), and (3) quantify tPA protein. The analytical methods for these analyses are described below.

[0078] (1) Counting of live cells The number of viable cells was counted using a Vi-CELL viability cell detector (Beckman Coulter). The sampled culture medium (CM) was placed in the Vi-CELL, and live and dead cells were distinguished using trypan blue staining. Image data of the cells was acquired, and the number of viable cells was calculated by automatic counting.

[0079] (2) Analysis of medium components (glucose, glutamine, lactate, ammonia) Glucose, glutamine, lactate, and ammonia in the culture medium CM were measured using Bioplofile 100plus (Nova).

[0080] (3) Quantification of tPA protein The tPA protein was quantified using the tPA, Human, ELISA Kit (Funakoshi). The analytical method was based on enzyme-linked immunosorbent assay (ELISA), using mouse anti-tPA antibody as the capture antibody and biotinylated polyclonal anti-tPA antibody as the detection antibody. The substrate was tetramethylbenzidine, and the color reaction was carried out with streptavidin-peroxidase. The absorbance at a wavelength of 450 nm was measured using an absorption spectrophotometer.

[0081] (Optimization using a culture-related process optimization system) To maximize the antibody concentration, the culture conditions were optimized using the above culture method through repeated cycles (cycles [1] to [7] described above). The culture conditions before optimization (culture conditions for the first cycle) were a temperature of 37°C, pH 7.2, dissolved oxygen of 40%, and a pitched paddle stirrer (shear force). As the cycles were repeated, the culture vessel 30 used was changed depending on the range of shear stress, and the culture medium CM was also fed accordingly.

[0082] The difference between the antibody concentration at the 20th cycle and that at the 9th cycle was 5% or less, so the culture was terminated at the 20th cycle. The culture conditions at the 20th cycle (values ​​of the first explanatory variable and the second explanatory variable) were determined to be the optimal culture conditions (temperature 36°C, pH 7.0, dissolved oxygen 20%, stirring impeller (shear force) disc turbine impeller).

[0083] The results of the culture after optimizing the culture conditions are shown in Figure 13. Figure 13 is a graph showing the relationship between the number of days in culture (days) and the antibody concentration under the conditions before optimization (conditions for the first cycle) and after optimization (culture conditions for the 20th cycle). As shown in FIG. 13, an improvement in antibody concentration was confirmed under the optimized conditions compared to the pre-optimization conditions. In the above optimization, there are four explanatory variables. In the conventional method where optimization is performed at five levels, it is necessary to investigate 4^5 = 1024 experimental conditions through experiments, but with optimization using the culture system 1, the optimal conditions could be found in 20 experiments. The cost of consumables required for the experiment was reduced to 1 / 20 compared to the conventional method. In addition, the experimental period was reduced to 1 / 6 or less compared to the conventional method where eight consecutive culture tanks are used. It has been confirmed that the present invention can reduce the time and cost required for culture process development. [Explanation of symbols]

[0084] 1. Culture system 10. Computers 11 CPU 12, 121 Arithmetic section 13, 131 Control section 14 RAM 15 ROM 16 HDD 20 Communication Circuit 21 Input / Output Devices 22 Recording Media 30 Culture tank 30a First culture tank 30b Second culture tank 31 Gas supply valve 31a Piping 32a Transfer valve 32b Transfer valve 32c Piping 33 Gas exhaust valve 33a Piping 34 Feed mechanism 35 Cell separation mechanism 36 Bleeding mechanism 120 Culture management equipment CM culture solution

Claims

1. A culture system for conducting experiments to explore culture conditions in cell culture. a calculation unit that searches for values ​​of a plurality of explanatory variables that will cause a target variable in the culture experiment to satisfy a predetermined condition, based on experimental data of a culture experiment conducted by changing a plurality of explanatory variables related to the culture conditions, and outputs additional experimental information that is information on explanatory variables to be changed in the culture experiment, based on the searched values ​​of the plurality of explanatory variables; A plurality of culture vessels for the culture experiment; a control unit that controls the culture conditions in the plurality of culture vessels based on the additional experimental information output by the calculation unit, and also controls the transfer of cells to be cultured between the plurality of culture vessels in accordance with the determination; Equipped with The plurality of explanatory variables include one or more first explanatory variables whose values ​​can be changed within a predetermined range in each of the plurality of culture tanks, and one or more second explanatory variables whose possible values ​​differ between at least one pair of culture tanks included in the plurality of culture tanks, When the explanatory variable to be changed in the culture experiment performed in a first culture tank among the plurality of culture tanks in the additional experimental information is the second explanatory variable, the control unit determines whether or not it is necessary to transfer the cells to be cultured to a second culture tank among the plurality of culture tanks that is different from the first culture tank, and when it is determined that the transfer is necessary, controls the transfer of the cells to be cultured from the first culture tank to the second culture tank.

2. The culture system according to claim 1, wherein the control unit determines the second culture tank to which the cells to be cultured are transferred based on the type and value of the second explanatory variable included in the additional experimental information.

3. 2. The culture system according to claim 1, wherein when the explanatory variable to be changed in the culture experiment performed in a first culture tank among the plurality of culture tanks in the additional experiment information is the first explanatory variable, the control unit performs control to change the first explanatory variable in the culture experiment performed in the first culture tank.

4. The plurality of culture tanks include a gas supply valve that supplies gas to the first culture tank, a transfer valve that connects the first culture tank and the second culture tank, and a gas discharge valve that discharges gas from the second culture tank.

2. The culture system according to claim 1, wherein, when transferring the cells to be cultured from the first culture tank to the second culture tank, the control unit opens the gas supply valve to start supplying gas to the first culture tank, and then opens the transfer valve and the gas exhaust valve to transfer the cells to be cultured.

5. 2. The culture system according to claim 1, wherein the culture tank comprises a feeding mechanism, a cell separation mechanism, a bleeding mechanism, and mechanisms for controlling temperature, pH, and dissolved oxygen.

6. The culture system according to claim 1, wherein the second explanatory variable is at least one of shear stress and bubble diameter.

7. 7. The culture system according to claim 6, wherein the stirring blade for controlling the shear stress is at least one of a pitched paddle, a flat paddle, and a disk turbine blade.

8. A culture method for conducting experiments to explore culture conditions in cell culture, a calculation step of searching for values ​​of a plurality of explanatory variables that will cause a target variable in the culture experiment to satisfy a predetermined condition based on experimental data of the culture experiment conducted by changing a plurality of explanatory variables related to the culture conditions, and outputting additional experimental information that is information on explanatory variables to be changed in the culture experiment based on the searched values ​​of the plurality of explanatory variables; a control step of controlling culture conditions in a plurality of culture vessels for the culture experiment based on the additional experimental information output in the calculation step, and controlling the transfer of cells to be cultured between the plurality of culture vessels in accordance with the determination, The plurality of explanatory variables include one or more first explanatory variables whose values ​​can be changed within a predetermined range in each of the plurality of culture tanks, and one or more second explanatory variables whose possible values ​​differ between at least one pair of culture tanks included in the plurality of culture tanks, The control step determines whether or not the cells to be cultured need to be transferred to a second culture tank among the plurality of culture tanks that is different from the first culture tank when the explanatory variable to be changed in the culture experiment performed in a first culture tank among the plurality of culture tanks in the additional experimental information is the second explanatory variable, and controls the transfer of the cells to be cultured from the first culture tank to the second culture tank when it is determined that the transfer is necessary.

9. A culture management device that searches for culture conditions in cell culture, a calculation unit that searches for values ​​of a plurality of explanatory variables that will cause a target variable in the culture experiment to satisfy a predetermined condition, based on experimental data of a culture experiment conducted by changing a plurality of explanatory variables related to the culture conditions, and outputs additional experimental information that is information on explanatory variables to be changed in the culture experiment, based on the searched values ​​of the plurality of explanatory variables; a control unit that controls the transfer of cells to be cultured between a plurality of culture tanks for the culture experiment, The plurality of explanatory variables include one or more first explanatory variables whose values ​​can be changed within a predetermined range in each of the plurality of culture tanks, and one or more second explanatory variables whose possible values ​​differ between at least one pair of culture tanks included in the plurality of culture tanks, The control unit determines whether or not it is necessary to transfer the cells to be cultured to a second culture tank, which is different from the first culture tank, among the plurality of culture tanks, based at least on whether or not the explanatory variable to be changed in the culture experiment performed in a first culture tank among the plurality of culture tanks in the additional experiment information is the second explanatory variable.

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