WO2020148964A1 - Cell production support device, method, and program - Google Patents

Abstract

A cell production support device, method, and program in which information is acquired about whether it is possible to produce a target number of a desired cell. The present invention acquires information about whether it is possible to produce desired cells for each cell measurement, on the basis of measurement information relating to a measurement taken when cells are measured during any step out of: an initialization step for acquiring history information indicating a history of a utilized cell, and cell information about the desired cell for production, and initializing the history information, the cell information, and cells; a cultivation step for cultivating the cells; and a differentiation step for inducing differentiation in the cells.

Description

Cell generation support device, method, and program

The technology of the present disclosure relates to a cell generation support device, method, and program.

In recent years, pluripotent stem cells such as ES (Embryonic Stem) cells and iPS (Induced Pluripotent Stem) cells have the ability to differentiate into cells of various tissues, and are useful in regenerative medicine, drug development, disease elucidation, etc. It is attracting attention as a cell that can be applied. For example, when it is desired to generate a desired number of desired cells by differentiating iPS cells, cells such as blood cells 41 or skin cells 42 are collected from a cell donor 40 as shown in FIG. Initialization step T1 of generating iPS cells 43 by introducing a plurality of genes into the prepared cells, and culturing the generated iPS cells 43 (hereinafter, also referred to as expansion culture) to generate a desired number of iPS cells 46 It is necessary to go through the culturing step T2 and the differentiating step T3 in which the generated number of iPS cells 46 are induced to differentiate into nerve cells 47, cardiomyocytes 48, liver cells 49 and the like. Generally, the initialization process T1 and the culture process T2 require a total of about 2 months, and the differentiation process T3 requires a period of about 2 to 3 months.

In the culturing step T2, there is an operation called "passage" in which the medium is removed from the culture container containing the iPS cells 43 and the iPS cells 43 are transferred to a new medium. In the subculture operation, the proliferated iPS cells 43 are uniformly seeded in a plurality of culture vessels at a predetermined size and density, and cell division is performed. When substituting cultured cells, in particular for iPS cells that have been reprogrammed, in general, the person in charge of the work should visually observe cells that may proliferate while maintaining the undifferentiated state. Have been selected by. The person in charge selects cells, for example, by continuously culturing iPS cells 44 having a well-shaped cell line and removing iPS cells 45 having a non-well-shaped cell line. However, even when the person in charge selects cells, there are cases in which differentiation induction from iPS cells is not successful. If differentiation induction is not successful, it will be difficult to generate the target number of cells to be differentiated, which is an obstacle to industrialization.

Therefore, it is possible to select cells suitable for culturing and differentiation by individually measuring cells such as a method of selecting good or bad of iPS cells from an image, a method of measuring metabolites, and a method of measuring genes. Has been done. In Patent Document 1, two or more indexes relating to the morphology of cells generated by analyzing each image obtained by photographing the cells of each sample at two or more time points (at the time of prediction) with different culture times, A prediction model for predicting the quality of cells is disclosed using the actual measurement data of the prediction target acquired for each sample.

JP, 2009-44974, A

On the other hand, in the culturing step T2, even when the cells that are likely to proliferate while maintaining the undifferentiated state are selected and differentiation-induced, the differentiation induction is always successful and the target cells are generated. However, there are cases where it fails. The number of cells that can possibly become the cells to be generated is determined at the end of the culture step T2. For example, when it is found that the number of cells that can possibly become the cells to be generated is smaller than the target number of cells after entering the differentiation step T3, new cells are generated from scratch due to the insufficient amount. This is time consuming and costly. For example, when using cells collected from a healthy person as a drug discovery tool, it is desired to generate the target number of cells to be generated in the shortest possible time. Therefore, it is desired to obtain information as to whether or not a desired number of cells to be generated can be generated, and if generation is not possible, it is possible to newly generate cells as early as possible. With the technique described in Patent Document 1, it is possible to predict the quality of cells from actual measurement data, but it is not possible to acquire information as to whether or not it is possible to generate a desired number of cells to be generated.

The present disclosure has been made in view of the above circumstances, and an object of the present disclosure is to enable acquisition of information as to whether or not a desired number of cells can be generated.

The cell generation support device according to the present disclosure includes an information acquisition unit that acquires history information indicating a history of cells to be used and cell information of cells to be generated, and history information, cell information, and cells acquired by the information acquisition unit. Generated for each cell measurement based on the measurement information related to the measurement when measuring the cell in any of the initialization step, the cell culture step, and the cell differentiation induction step. A generation possibility information acquisition unit for acquiring information on whether or not a desired cell can be generated.

In the cell generation support device according to the present disclosure, the generative information acquisition unit derives information about whether or not a desired cell can be generated based on the history information and the cell information acquired by the information acquisition unit, and the measurement information. It may include a first derivation unit that does.

Further, in the cell generation support device according to the present disclosure, the first derivation unit, the history information indicating the history of the cells to be used, the cell information of the cells to be generated, the initialization step of initializing the cells, and the cells are cultured. In any one of the culturing step and the differentiating step of inducing differentiation of cells, a first set of information including measurement information related to measurement when measuring cells and a set of the first information are provided. The first learned model that has been machine-learned by using the learning information including a plurality of information sets including the information indicating whether the corresponding cells to be generated can be generated may be included.

Further, in the cell generation support device according to the present disclosure, the measurement information may be at least one of information related to the measurement unit and information indicating the measurement result obtained by the measurement by the measurement unit.

Further, in the cell generation support device according to the present disclosure, the information related to the measurement means includes information indicating one of the method used for measurement and the person in charge of measurement, and the information indicating the measurement result is measured. The information may include any one or more of a cell state, a medium state, and the presence or absence of bacteria.

Further, in the cell generation support device according to the present disclosure, the measured cell state includes cell shape, cell color, cell number, cell size, cell odor, cell gene expression, cell metabolite. The information of any one or more of the type and the protein of the cell may be included.

Further, in the cell generation support device according to the present disclosure, the history information is the name of the cell donor who is the holder of the cells to be used, the sex of the cell donor, the blood type of the cell donor, the race of the cell donor, The information may include any one or more of the age of the cell donor, the disease history of the cell donor, the immune information of the cell donor, and the disease history of the relatives of the cell donor.

Further, in the cell generation support device according to the present disclosure, the cell information is any one or more of the types of cells to be generated, the number of cells to be generated, the state of cells to be generated, and the number of days to be completed. May be included.

In addition, the cell generation support device according to the present disclosure may include a notification unit that notifies information about whether or not a cell to be generated can be generated.

Further, in the cell generation support device according to the present disclosure, when the generative information acquisition unit acquires generative information, culture conditions for culturing cells based on the history information and the cell information acquired by the information acquisition unit. It may further include a culture condition acquisition unit for acquiring.

Further, in the cell generation support device according to the present disclosure, the culture condition acquisition unit may be used in any one of an initialization step of initializing cells, a culture step of culturing cells, and a differentiation step of inducing differentiation of cells. The updated culture condition updated based on the measurement information related to the measurement when measuring cells may be acquired as the culture condition.

Further, in the cell generation support device according to the present disclosure, the culture condition acquisition unit updates the culture condition acquisition unit based on the measurement information and the second derivation unit that derives the culture condition based on the history information and the cell information acquired by the information acquisition unit. A third derivation unit that derives the updated culture condition as the culture condition may be included.

In the cell generation support device according to the present disclosure, the second derivation unit includes a second information set including history information indicating the history of cells to be used and cell information of cells to be generated, and the second information. The second derivation model includes a second trained model machine-learned using learning information including a plurality of information sets with culture conditions corresponding to the set, and the third derivation unit initializes the cell, In any one of the culturing step of culturing and the differentiating step of inducing differentiation of cells, a third set of information including measurement information and culturing conditions related to measurement when measuring cells, and this third set The third learned model machine-learned using learning information including a plurality of information sets with updated culture conditions corresponding to the information set of

Further, in the cell generation support device according to the present disclosure, the second learned model and the third learned model may be configured by one learned model.

Further, in the cell generation support device according to the present disclosure, the culture conditions include the type of cells used, the number of cells used, the type of container used, the type of medium used, the type of additive used, the timing of treatment, Also, any one or more of the information regarding the person in charge may be included.

Further, in the cell generation support device according to the present disclosure, the processing timing is any one of seeding timing, passage timing, medium replacement timing, additive addition timing, and cell inspection timing. It may include one or more timings.

In the cell generation support method according to the present disclosure, history information indicating the history of cells to be used and cell information of cells to be generated are acquired, and history information, cell information, and an initialization step of initializing cells, and culturing cells Whether or not the desired cell can be generated for each cell measurement, based on the measurement information related to the measurement when measuring the cell in any one of the culture step and the differentiation step of inducing the differentiation of cells Get information.

The cell generation support method according to the present disclosure may be provided as a program that causes a computer to execute the method.

Another cell generation support device according to the present disclosure includes a memory that stores instructions to be executed by a computer, and a processor configured to execute the stored instructions, the processor indicating a history of cells to be used. Acquiring history information and cell information of cells to be generated, history information, cell information, and any one of an initialization step of initializing cells, a culture step of culturing cells, and a differentiation step of inducing differentiation of cells Based on the measurement information related to the measurement when measuring the cells in the process, a process of acquiring information on whether or not the desired cell can be generated is executed for each measurement of the cell.

According to an embodiment of the present disclosure, it is possible to acquire information as to whether or not a desired number of cells to be generated can be generated.

The figure which shows schematic structure of the cell production assistance apparatus by 1st Embodiment of this indication. A figure showing an example of career information by a 1st embodiment of this indication. The figure which shows an example of the cell information by the 1st Embodiment of this indication. The figure showing an example of measurement information by a 1st embodiment of this indication. The figure which shows the schematic structure of the production|generation possible information acquisition part by the 1st Embodiment of this indication. The figure for demonstrating the trained model by 1st Embodiment of this indication. The figure for demonstrating the 1st derivation|leading-out part by 1st Embodiment of this indication. The flowchart which shows the process performed in the cell production assistance apparatus of the 1st Embodiment of this indication. The figure for demonstrating the trained model by 1st Embodiment of this indication. The figure which shows the schematic structure of the cell production assistance apparatus by 2nd Embodiment of this indication. The figure which shows an example of the culture conditions by the 2nd Embodiment of this indication. The figure which shows schematic structure of the culture condition acquisition part by 2nd Embodiment of this indication. The figure for demonstrating the trained model by 2nd Embodiment of this indication. The figure for demonstrating the 2nd derivation|leading-out part by 2nd Embodiment of this indication. The figure for further explaining the trained model according to the second embodiment of the present disclosure. The figure for demonstrating the 3rd derivation|leading-out part by 2nd Embodiment of this indication. The flowchart which shows the process performed in the cell production assistance apparatus of the 2nd Embodiment of this indication. The figure for demonstrating the process performed in the cell production assistance apparatus of the 2nd Embodiment of this indication. Diagram for explaining each step of generating cells

Hereinafter, a first embodiment of the present disclosure will be described with reference to the drawings. FIG. 1 is a diagram showing a schematic configuration of a cell generation support device according to the first embodiment of the present disclosure. FIG. 1 shows that the cell generation support program is installed in the non-volatile memory 12. The cell generation support program may be installed in the storage 13. Further, the memory 12 may be a volatile memory such as a DRAM (Dynamic Random Access Memory) or an SRAM (Static Random Access Memory). In that case, the cell generation support program called from the CPU 12 is temporarily stored in the memory 12 May be stored and executed. In addition, in the first embodiment of the present disclosure, the cell generation support device includes the learned model. Therefore, only the cell generation support device is shown in FIG. As shown in FIG. 1, the cell generation support device 1 includes a CPU (Central Processing Unit) 11, a memory 12, and a storage 13 as a standard computer configuration. Further, the cell generation support device 1 is connected to a notification unit 14 for notifying a culture condition C, which will be described later, and an input device (hereinafter referred to as an input unit) 15 such as a keyboard and a mouse.

The notification unit 14 according to the embodiment of the present disclosure includes, for example, a display that visually displays the culture condition C and the information F indicating whether or not a desired cell can be generated. The notification unit 14 includes a display for visually displaying a message and the like, a sound reproducing device for audibly displaying when a sound is output, a printer for recording on a recording medium such as paper and the like for permanent visual display, a communication means such as an email or a telephone, and the like. It means an indicator light or the like, and may be a combination of at least two or more of the display, the audio reproducing device, the printer, the communication means, and the display light. In addition, in one embodiment of the present disclosure, the notification unit 14 is an external device of the cell generation support device 1, but the technology of the present disclosure is not limited to this, and the notification unit 14 is included in a part of the cell generation support device 1. It may be.

The storage 13 comprises a storage device such as a hard disk or SSD (Solid State Drive). The storage 13 stores various information including history information A indicating the history of cells to be used and information necessary for the process of the cell generation support device 1, acquired from an external data server (not shown) via the network. Remembered

Further, the memory 12 stores a cell generation support program and a learned model. The cell generation support program, as processing to be executed by the CPU 11, information acquisition processing for acquiring history information indicating the history of cells to be used and cell information of cells to be generated, and history information, cell information, and an initial stage of initializing cells. Cells to be generated for each cell measurement based on measurement information related to measurement when measuring cells in any one of the process of culturing cells, the step of culturing cells, and the step of differentiating cells Defines a generation possibility information acquisition process for acquiring information on whether or not generation is possible.

The computer functions as the information acquisition unit 21 and the creatable information acquisition unit 23 by the CPU 11 executing these processes according to the cell generation support program. In the present embodiment, the CPU 11 executes the function of each unit by the cell generation support program. However, as a general-purpose processor that executes software and functions as various processing units, in addition to the CPU 11, A programmable logic device (PLD), which is a processor whose circuit configuration can be changed after manufacture of an FPGA (Field Programmable Gate Array) or the like, can be used. Further, the processing of each unit may be executed by a dedicated electric circuit or the like, which is a processor having a circuit configuration specifically designed for executing a specific processing such as an ASIC (Application Specific Integrated Circuit). When the memory 12 is a volatile memory, the cell generation support program called by the CPU 12 and the learned model may be temporarily stored in the memory 12 and executed.

One processing unit may be configured by one of these various processors, or may be a combination of two or more processors of the same type or different types (for example, a plurality of FPGAs or a combination of CPU and FPGA). It may be configured. Further, the plurality of processing units may be configured by one processor. As an example of configuring a plurality of processing units by one processor, firstly, as represented by a computer such as a client and a server, one processor is configured by a combination of one or more CPUs and software. There is a form in which the processor functions as a plurality of processing units. Secondly, as represented by a system on chip (SoC) and the like, there is a form in which a processor that realizes the function of the entire system including a plurality of processing units by one IC (Integrated Circuit) chip is used. is there. As described above, the various processing units are configured by using one or more of the above various processors as a hardware structure.

Further, the hardware structure of these various processors is, more specifically, an electric circuit in which circuit elements such as semiconductor elements are combined.

The information acquisition unit 21 acquires history information A indicating the history of cells to be used and cell information B of cells to be generated. As an example, when the identification information written on the container in which the cells to be used are stored is input from the input unit 15, the information acquisition unit 21 outputs the history information A of the cell provider corresponding to the input identification information. , Get from external server.

FIG. 2 is a diagram showing an example of the history information A according to the first embodiment of the present disclosure. The history information A is information indicating the history of the cells used. The history information A is, for example, as shown in FIG. 2, the name of the cell donor who is the holder of the cells to be used, the sex of the cell donor, the blood type of the cell donor, the race of the cell donor, and the cell donor. Information on the age of the person, the disease history of the cell donor, the immune information of the cell donor, and the disease history of the relatives of the cell donor. In one embodiment of the present disclosure, the relatives of the cell donor are, for example, relatives within the third degree of relatives of the cell provider himself. Note that the technology of the present disclosure is not limited to this, and may include, for example, a blood relative farther than the third degree relative. Further, in the present disclosure, as an example, the history information A indicates the information illustrated in FIG. 2. However, the technology of the present disclosure is not limited to this, and any one or more of the information illustrated in FIG. The information may be included.

FIG. 3 is a diagram showing an example of the cell information B according to the first embodiment of the present disclosure. The cell information B is information on cells to be generated. As shown in FIG. 3 as an example, the cell information B includes information on the type B1 of cells to be generated, the target cell number B2, and the state B3 of cells to be generated. The cell type B1 is information on the types of cells to be generated, such as iPS cells, cardiomyocytes, and nerve cells. The target cell number B2 is information indicating the number of cells to be generated, such as 100,000 cells and 100 cells. The cell state B3 is information indicating what one wants to do with the cell, such as wanting to culture and differentiating.

When the cell type B1 and the target cell number B2 are set in advance, they are not changed until the initialization step T1, the culture step T2, and the differentiation step T3 shown in FIG. 19 are all completed. The cell state B3 is "I want to initialize" in the initial step T1, "I want to expand culture" and "I want to sort" in the culture step T2, and "I want to differentiate" in the differentiation step T3. It is changed by the person in charge for each process. In the technique of the present disclosure, as shown in FIG. 3 as an example, the cell information B is information indicating the cell type B1, the target cell number B2, the cell state B3, and the number of days B4 at which generation is desired to be completed. The technology of the present disclosure is not limited to this, and the cell information B may be information including any one or more of these pieces of information.

The generable information acquiring unit 23 includes the history information A acquired by the information acquiring unit 21, the cell information B, an initialization step T1 for initializing the cells, a culturing step T2 for culturing the cells, and a differentiation step for inducing differentiation of the cells. Based on the measurement information D related to the measurement when measuring the cell in any step of T3, the information F indicating whether or not the desired cell can be generated is acquired for each measurement of the cell.

Here, the measurement information D will be described. FIG. 4 is a diagram showing an example of the measurement information D according to the first embodiment of the present disclosure. The measurement information D is information related to the measurement when measuring the cells in the inspection of the cells. As shown in FIG. 4, the measurement information D1 is information D1 and the information D2 indicating the measurement result obtained by the measurement by the measurement means. is there.

The information D1 on the measuring means includes information on the method used for the measurement and information on the person in charge of the measurement. As the method used for the measurement, there is information on the type of the measuring device and the presence/absence of use of the measuring device, and there are a phase contrast microscope, a bright field microscope, visual inspection, and the like. Further, as the information of the person in charge of measurement, there are information such as the names of the respective measurers such as the measurer A and the measurer B and the mature years of the measurement.

The information D2 indicating the measurement result obtained by the measurement includes information on the measured cell state, medium state, and presence/absence of bacteria. The measured cell state is the inspection value of the cell, and the shape of the cell, the color of the cell, the number of cells, the size of the cell, the odor of the cell, the gene expression of the cell, the kind of the metabolite of the cell, the type of the cell The information on proteins, the size of the holes made in the cells, etc. In the present disclosure, cell protein information is information on whether or not a protein is synthesized, and cell gene expression refers to a process in which gene information is converted into structure and function in the cell. In the technology of the present disclosure, as an example, a protein is measured using a fluorescent probe, and gene expression is measured using a gene amplification agent of a target obtained by grinding cells. Note that the present disclosure is not limited to this, and a known measurement method can be used.

In addition, the state of the medium includes information such as the color of the medium, metabolites of cells contained in the medium, and the concentration of gas dissolved in the medium. By measuring the color of the medium, the amount of carbon dioxide dissolved in the medium can be measured. As the gas concentration, for example, the carbon concentration and nitrogen concentration in the medium are measured. This makes it possible to detect whether or not the cells have been normally cultured.

The presence/absence of the bacterium is information such as whether the cell has the bacterium, the medium has the bacterium, the cells and the medium have the bacterium, and the bacterium does not have the bacterium.

Note that in the present disclosure, the information illustrated in FIG. 4 is the measurement information D as one embodiment, but the technology of the present disclosure is not limited to this, and the measurement information D includes any one or more of the information illustrated in FIG. 4. Any information can be included.

As an example, in the initialization step T1 shown in FIG. 19, when the measurement is performed during the initialization, a method used for the measurement is, for example, a phase contrast microscope. That is, in the initialization step T1, blood cells 41 and skin cells 42 are perforated, and a drug is introduced into the perforated holes. In this case, the cells may not be successfully initialized unless the holes are formed in the size, shape, and depth necessary for containing the drug. Therefore, for example, the size of the hole is measured from the image obtained by imaging the perforated cell 41 and the skin cell 42 with a phase contrast microscope. The method used for measurement in the initialization step T1 is not limited to the phase-contrast microscope, and any method may be used as long as it can measure holes.

Further, as an example, in the culture step T2 shown in FIG. 19, as a method used for the measurement performed when selecting the iPS cells, there is, for example, a method using a phase contrast microscope. An iPS cell 43 is imaged using a phase-contrast microscope to acquire a captured image, and the size and shape of the iPS cell 43 in the acquired captured image are measured to form an iPS having a well-shaped shape, that is, a differentiation-inducible shape. When the iPS cell 44 is the cell 44, the iPS cell 44 is expanded and continuously cultivated. For example, when the iPS cell 44 has an irregular shape, that is, the iPS cell 44 has a shape that cannot induce differentiation, By removing 44, the cells are sorted. Note that the method of using the iPS cells 43 for measurement is not limited to the phase contrast microscope, and may be the visual observation of the measurer or any other method.

FIG. 5 is a diagram showing a schematic configuration of the createable information acquisition unit 23 according to the first embodiment of the present disclosure. The generable information acquisition unit 23 includes a first derivation unit 29, as shown in FIG. The first derivation unit 29 derives, based on the history information A and the cell information B, and the measurement information D, the information F indicating whether or not a desired cell can be generated for each cell measurement. In the present embodiment, as an example, the first derivation unit 29 includes a learned model M that has been machine-learned using learning information.

FIG. 6 is a diagram for explaining the learned model M according to the first embodiment of the present disclosure. As shown in FIG. 6, the learned model M corresponds to the history information A indicating the history of the cells to be used, the cell information B of the cells to be generated, the information set Q of the measurement information D, and the information set Q. Machine learning is performed using learning information including a plurality of information sets J with the information F indicating whether or not cells to be generated can be generated. That is, the learned model M is machine-learned so as to output information F indicating whether or not a desired cell can be generated based on the history information A and the cell information B, and the measurement information D. The learned model M is, for example, a target number of cells to be generated when generating cells of the cell information B from cells of the cell provider X having the information set Q of the cell provider X, that is, the history information A. The model for obtaining the trained model M is also trained together with the result of whether or not it has been generated. As a result, when the history information A and the cell information B and the measurement information D are input to the learned model M, the cells to be generated can be generated for the history information A and the cell information B and the measurement information D. Learning is performed so that the information F indicating whether or not it is output.

As an embodiment of the present disclosure, a machine learning algorithm in the learned model M can use, for example, a neural network (NN (Neural Network)) on which deep learning (deep learning) is performed. However, the technology of the present disclosure is not limited to this, and examples thereof include a support vector machine (SVM), a convolutional neural network (CNN), a convolutional neural network (CNN), and a recurrent. A known machine learning algorithm such as a neural network (RNN (Recurrent Neural Network)) can be appropriately used.

FIG. 7 is a diagram for explaining the first derivation unit 29 according to the first embodiment of the present disclosure. The first derivation unit 29 has, as an example, the learned model M described above. As a result, as shown in FIG. 7, when the cells of the cell information B are generated from the cells of the cell provider X having the history information A, the history information A, the cell information B, and the measurement information D are input. Information about whether or not culture exceeding a predetermined threshold value can be continued, that is, information F about whether or not cells desired to be generated can be generated is output. When the history information A, the cell information B, and the measurement information D are input to the learned model M, the learned model M outputs a probability that a target quality and a target number of cells can be generated. When the probability of occurrence exceeds a predetermined probability threshold value, the cells desired to be generated can be generated, and when the probability threshold value is less than or equal to the threshold value of the probability, it is output that the cells desired to be generated cannot be generated. The learned model M may output the probability that a desired cell can be generated for each number of cells. In this case, the number of cells that exceeds a threshold of a predetermined probability is output as the number of cells that can be generated, and the number of cells that is less than or equal to the threshold of the probability is a cell that is insufficient to reach the target number of cells. May be output as the number of

Next, a process performed in the cell generation support device 1 according to the first embodiment of the present disclosure will be described. FIG. 8 is a diagram for explaining processing performed in the cell generation supporting apparatus 1 according to the first embodiment of the present disclosure, and FIG. 9 shows processing performed in the cell generation supporting apparatus 1 according to the first embodiment of the present disclosure. It is a flowchart shown. First, the information acquisition unit 21 acquires history information A indicating the history of cells to be used and cell information B of cells to be generated (step ST1).

Next, based on the information D1 regarding the measurement means, the measurement of cells is determined in advance in each step of an initialization step T1 for initializing cells, a culture step T2 for culturing cells, and a differentiation step T3 for inducing differentiation of cells. Timing, that is, cell inspection timing. In the cell generation support device 1, the CPU 11 determines a predetermined timing in each step of the initialization step T1 for initializing cells, the culture step T2 for culturing cells, and the differentiation step T3 for inducing differentiation of cells, that is, the determination. Based on the timing, it is determined whether or not the generatable information acquisition unit 23 has acquired new measurement information D related to measurement when measuring cells (step ST2). As an example, the determination timing can be set based on the cell inspection timing, and the determination timing can be set between a certain cell inspection timing and the next cell inspection timing.

When it is determined in step ST2 that the generable information acquisition unit 23 has not acquired new measurement information D (step ST2; NO), the CPU 11 shifts the processing to step ST6, and all steps are completed. It is determined whether or not (step ST6).

On the other hand, when the CPU 11 determines in step ST2 that the new measurement information D has been acquired (step ST2; YES), the producible information acquisition unit 23 acquires the new measurement information D as shown in FIG. Based on the history information A, the cell information B, and the measurement information D output by inputting to the learned model M, the information F indicating whether or not the desired cell can be generated is acquired (step ST3).

Next, the CPU 11 determines whether or not the cells to be generated can be generated (step ST4). If it can be generated (step ST4; YES), the notification unit 14 notifies that it can be generated, and the cell culture is continued. Specifically, the notification unit 14 displays on the display that cells can be generated (step ST5). The notification unit 14 may display the number of cells that are still insufficient.

Next, the CPU 11 determines whether or not all steps have been completed (step ST6). When all the steps are not completed (step ST6; NO), the CPU 11 shifts the processing to step ST2 and performs the subsequent processing. That is, based on the timing determined in advance, it is determined whether or not the generatable information acquisition unit 23 has acquired new measurement information D related to measurement when measuring cells (step ST2). On the other hand, when all the steps are completed (step ST6; YES), the cell generation support device 1 ends the series of processes.

On the other hand, in step ST4, when the generation is not possible (step ST4; NO), the notification unit 14 notifies the culture stop and stops the cell culture. Specifically, the notification unit 14 displays the culture stop on the display (step ST7). Then, the cell generation support device 1 ends the series of processes.

As described above, according to the cell generation support device 1 of the first embodiment of the present disclosure, the history information A indicating the history of the cells to be used and the cell information B of the cells to be generated are acquired, and the history information A and the cell information are acquired. B, based on measurement information D related to measurement when measuring cells in any one of an initialization step of initializing cells, a culture step of culturing cells, and a differentiation step of inducing differentiation of cells Then, since the information F indicating whether or not the cells to be generated can be generated is acquired for each cell measurement, it is possible to acquire the information regarding whether or not the desired number of cells can be generated.

As described above, since it is possible to obtain the information F indicating whether or not a desired cell can be generated for each measurement of cells, the person in charge of generating the cell is notified of the fact that the cell can be generated when the cell is notified that the cell can be generated. On the other hand, the culture can be continued, and when it is notified that the cells cannot be generated, the cell culture can be stopped and restarted from the beginning, or the generation of an insufficient number of cells can be started. As a result, unnecessary time can be shortened and unnecessary culture and differentiation induction can be eliminated as compared with the conventional case, so that cost loss can be prevented. In the present embodiment, the creatable information acquisition unit 23 acquires the information F indicating whether or not the desired cell can be created, based on the history information A, the cell information B, and the new measurement information D. Without being limited to this, the generative information acquisition unit 23 wants to generate based on the history information A, the cell information B, and all the measurement information D acquired from the start of the culture until the new measurement information D is acquired. The information F indicating whether or not cells can be generated may be acquired.

Next, a second embodiment of the present disclosure will be described. FIG. 10 is a diagram showing a schematic configuration of a cell generation support device 1-2 according to the second embodiment of the present disclosure. FIG. 10 is realized by installing a cell generation support program. The cell generation support apparatus 1-2 according to the second embodiment of the present disclosure is the cell generation support apparatus 1 according to the first embodiment described above. The same components as those of the above are given the same reference numerals, and the description thereof will be omitted. Only different points will be described in detail.

As shown in FIG. 10, the cell generation support device 1-2 further includes a culture condition acquisition unit 22 in addition to the cell generation support device 1 of FIG. The culture condition acquisition unit 22 acquires a culture condition C for culturing cells based on the history information A and the cell information B. FIG. 11 is a diagram showing an example of the culture condition C according to the second embodiment of the present disclosure. The culture condition C is a culture condition for producing a desired number of cells to be produced. Here, it is desirable that the "cells to be generated" are cells having a target quality. Therefore, in the present embodiment, the culturing condition C is a culturing condition for producing the desired number of cells to be produced with a desired quality. The culture condition C indicates which cells are used, how many cells are used, which medium is used, which medium is used, which additive is added to generate the cells, and the desired number of cells can be generated. .. Specifically, the culture condition C is, as shown in FIG. 11 as an example, the type of cells used, the number of cells used, the type of container used, the type of medium used, the type of additive used, and the treatment. Includes information about the timing of, and the person in charge.

The types of cells used are, for example, cell A, cell B, or cell A and cell B. For example, culture condition C includes the use of liver cells as cells A and vascular cells as cells B. Also, cell number, 10 ⁵ cells A, 10 ⁶ cells of cell B, and cell 10 ⁵ A and a cell B 10 ⁶ cells, etc., the number of specific uses for the cells contained in the culture condition C Be done. The type of container used is, for example, a petri dish, n well plates having 6 wells, m well plates having 24 wells, and 1 T75 flask. And are included in culture condition C. The culture conditions C include, as an example, the type of medium to be used, such as medium M1, medium M2, or a mixture of medium M1 and medium M2. When the medium is mixed, the culture condition C is set including the mixing ratio. The type of the additive to be used includes, for example, the additive C1, the additive C2, or the mixture of the additive C1 and the additive C2 in the culture condition C. When the additives are mixed, the culture condition C is set including the mixing ratio.

The timing of the treatment is, for example, the timing of seeding, the timing of subculture, the timing of exchanging the medium, the timing of adding additives, and the timing of inspecting cells. The culture condition C includes which treatment should be performed at which timing. In addition, the culture condition C includes information about the person in charge, such as who is in charge of the person A and person B. For example, the person A who is good at fine work and the person B who is not good at fine work may have different effects on cell generation even if the same process is performed. Therefore, the culture condition C includes which person in charge and which process should be performed.

Note that in the present disclosure, the condition shown in FIG. 11 as the embodiment is the culture condition C, but the technique of the present disclosure is not limited to this, and the culture condition C includes any one or more of the information shown in FIG. 11. Any information will do.

FIG. 12 is a diagram showing a schematic configuration of the culture condition acquisition unit 22 according to the second embodiment of the present disclosure. As shown in FIG. 12, the culture condition acquisition unit 22 includes a second derivation unit 30 and a third derivation unit 31. The second derivation unit 30 derives the culture condition C based on the history information A and the cell information B. In the present embodiment, as an example, the second derivation unit 30 includes a learned model M that has been machine-learned using learning information.

FIG. 13 is a diagram for explaining the learned model M according to the second embodiment of the present disclosure. As shown in FIG. 13, the learned model M includes a history information A indicating the history of cells to be used and an information set P of cell information B of cells to be generated, and a culture condition C corresponding to the information set P. Machine learning is performed using learning information including a plurality of information sets S. That is, the learned model M is machine-learned so as to output the culture condition C based on the history information A and the cell information B. The learned model M is cultured under the culture condition C when the cells of the cell information B are generated from the cells of the cell provider X having the history information A, that is, the information set P of the cell provider X. In this case, the model for obtaining the learned model M is also trained with the result of success or failure. As a result, when the history information A and the cell information B are input, the learned model M outputs the culture condition C capable of generating the cell indicated by the cell information B, with respect to the history information A and the cell information B. Learning is done to do.

FIG. 14 is a diagram for explaining the second derivation unit 30 according to the second embodiment of the present disclosure. The second derivation unit 30 has the learned model M shown in FIG. 13 described above as an example. As a result, as shown in FIG. 14, when the cells of the cell information B are generated from the cells of the cell provider X having the history information A, when the history information A and the cell information B are input, it is determined in advance. The culture condition exceeding the threshold value is output as the culture condition C suitable for the cells to be generated. The culture condition C is, as shown in FIG. 11, the type of cells used, the number of cells used, the type of container used, the type of medium used, the type of additive used, the timing of treatment, and the charge. It includes at least one of the information about the person. Therefore, for example, when the number of cells to be used is set as the culture condition C and the history information A and the cell information B are input to the learned model M, the learned model M has the target quality for each number of cells to be used. , And outputs the probability that a target number of cells can be generated, and the culture condition acquisition unit 22 uses a cell number that has the target quality and the highest probability that the target number of cells can be produced. It may be obtained as the number, that is, as the culture condition C most suitable for the cells to be generated.

The culture condition acquisition unit 22 according to the second embodiment of the present disclosure further performs measurement when measuring cells in any one of the initialization step T1, the culture step T2, and the differentiation step T3 shown in FIG. The updated culture condition E, which is an update of the culture condition C at the time when the measurement information D is acquired, is acquired as a new culture condition C based on the measurement information D related to.

FIG. 15 is a diagram for further explaining the learned model M according to the second embodiment of the present disclosure shown in FIG. 13. As shown in FIG. 15, the learned model M includes a set G of measurement information D and a culture condition C related to measurement when measuring cells, and an updated culture condition E corresponding to the set G of information. Machine learning is performed using learning information including a plurality of information sets R. That is, the learned model M is machine-learned so as to output the updated culture condition E based on the measurement information D and the culture condition C at the time when the measurement information D was acquired. It should be noted that the learned model M has already been learned together with the result of whether or not the culture was successful when the cell of the cell information B was generated from the cell of the cell provider X and the culture was performed under the updated culture condition E. Train the model to obtain model M. As a result, in the learned model M, when the new measurement information D and the culture condition C at the time when the new measurement information D is acquired are input, the cell information B indicates the new measurement information D. Learning is performed so as to output the updated culture condition E capable of generating cells.

Note that, as an embodiment of the present disclosure, the learned model M illustrated in FIG. 13 and the learned model M illustrated in FIG. 15 are the same model, but the technique of the present disclosure is not limited to this, and the technique illustrated in FIG. The learned model M shown may be a second learned model and the learned model M shown in FIG. 15 may be a third learned model. Further, in the learned model shown in FIG. 6, the learned model M shown in FIG. 13 and the learned model M shown in FIG. 15 may be the same model, or the learned model shown in FIG. The model may be a trained model and may be different from the above model.

As a second embodiment of the present disclosure, a machine learning algorithm in the learned model M can use, for example, a deep learning (NN) neural network (NN (Neural Network)). However, the technology of the present disclosure is not limited to this, and examples thereof include a support vector machine (SVM), a convolutional neural network (CNN), a convolutional neural network (CNN), and a recurrent. A known machine learning algorithm such as a neural network (RNN (Recurrent Neural Network)) can be appropriately used.

FIG. 16 is a diagram for explaining the third derivation unit 31 according to the second embodiment of the present disclosure. The third derivation unit 31 has the learned model M shown in FIG. 15 described above as an example. As a result, as shown in FIG. 16, when the cells of the cell information B are generated from the cells of the cell provider X having the history information A, the measurement information D measured at each measurement of the cells and the measurement information D are obtained. When the culture condition C at the time of acquisition is input, the culture condition exceeding a predetermined threshold value is output as the renewed culture condition E suitable for the cells to be generated. The updated culture condition E is, for example, when the number of cells to be used is the updated culture condition E, the measurement information D and the culture condition C at the time when the measurement information D is acquired are input to the learned model M. In this case, the learned model M outputs the probability that a target number of cells can be generated for each number of cells to be used, and the culture condition acquisition unit 22 outputs the target quality with the target quality. Alternatively, the number of cells having the highest probability of generating the number of cells may be acquired as the number of cells to be used, that is, the updated culture condition E most suitable for the cells to be generated.

Next, a process performed by the cell generation support device 1-2 according to the second embodiment of the present disclosure will be described. FIG. 17 is a flowchart showing a process performed by the cell generation support device 1-2 of the second embodiment of the present disclosure, and FIG. 18 is a process performed by the cell generation support device 1-2 of the second embodiment of the present disclosure. It is a figure for explaining. First, the information acquisition unit 21 acquires history information A indicating the history of cells to be used and cell information B of cells to be generated (step ST21).

Next, the culture condition acquisition unit 22 inputs the history information A and the cell information B acquired by the information acquisition unit 21 to the learned model M to set the culture condition C for culturing the cells, as shown in FIG. It is acquired (step ST22).

Then, based on the culture condition C acquired by the culture condition acquisition unit 22, that is, as shown in FIG. 11, the types of cells used, the number of cells used, the types of containers used, the types of medium used, and Based on the information on the type of additive, the timing of the treatment, and the person in charge, the person in charge starts the work of generating cells. First, as shown in FIG. 19, the person in charge collects cells based on the culture condition C from the cell donor 40, and the initialization step T1 of the collected cells is started.

Next, based on the information D1 on the measurement means of the culture condition C, each step of an initialization step T1 in which cell measurement initializes cells, a culture step T2 in which cells are cultured, and a differentiation step T3 in which cells are induced to differentiate Is performed at a predetermined timing, that is, a cell inspection timing. In the cell generation support device 1-2, the CPU 11 sets a predetermined timing in each step of an initialization step T1 for initializing cells, a culture step T2 for culturing cells, and a differentiation step T3 for inducing differentiation of cells, That is, based on the determination timing, it is determined whether or not the culture condition acquisition unit 22 has acquired new measurement information D related to measurement when measuring cells (step ST23). As an example, the determination timing can be set based on the cell inspection timing, and the determination timing can be set between a certain cell inspection timing and the next cell inspection timing.

When the culture condition acquisition unit 22 determines in step ST23 that the new measurement information D has not been acquired (step ST23; NO), the CPU 11 shifts the processing to step ST30 and completes all the steps. It is determined whether or not (step ST30).

On the other hand, when it is determined in step ST23 that the culture condition acquisition unit 22 has acquired the new measurement information D (step ST23; YES), the creatable information acquisition unit 23 acquires the newly acquired information as shown in FIG. Based on the history information A and the cell information B and the measurement information D, which is output by inputting various measurement information D into the learned model M, the information F indicating whether or not the desired cell can be generated is acquired ( Step ST24).

Next, the CPU 11 determines whether or not the cells to be generated can be generated (step ST25). When it can be generated (step ST25; YES), the notification unit 14 notifies that it can be generated, and the cell culture is continued. Specifically, the notification unit 14 displays on the display that cells can be generated (step ST26). The notification unit 14 may display the number of cells that are still insufficient.

On the other hand, in step ST25, when the generation is not possible (step ST25; NO), the notification unit 14 notifies the stop of the culture and stops the culture of the cells. Specifically, the notification unit 14 displays the culture stop on the display (step ST27). Then, the cell generation support device 1 ends the series of processes.

Next, in step ST26, when it is displayed on the display that cells can be generated, the culture condition acquisition unit 22 then acquires the new measurement information D and the new measurement information as shown in FIG. The updated culture condition E output from the learned model M is acquired as a new culture condition C by inputting the culture condition C at the time when D is acquired to the learned model M (step ST28).

Next, the notification unit 14 notifies the culture condition C newly acquired by the culture condition acquisition unit 22. Specifically, the culture condition C is displayed on the display (step ST29).

In the subsequent steps, cells are generated based on the updated culture condition C. Next, the CPU 11 determines whether or not all steps have been completed (step ST30). When all the steps are not completed (step ST30; NO), the CPU 11 shifts the processing to step ST23 and performs the subsequent processing. That is, based on the cell inspection timing of the updated culture condition C, it is determined whether or not the culture condition acquisition unit 22 has acquired new measurement information D related to measurement when measuring cells (step ST23). .. On the other hand, when all the steps are completed (step ST30; YES), the cell generation support device 1 ends the series of processes.

As described above, according to the cell generation support device 1 of the second embodiment of the present disclosure, when the cell to be generated can be generated, the history information A indicating the history of the cell to be used and the cell information of the cell to be generated. B is acquired, and a culture condition C for culturing cells is acquired based on the acquired history information A and cell information B, and further initialization step T1 for initializing cells, culture step T2 for culturing cells, And in any step of the differentiation step T3 for inducing the differentiation of cells, the culture condition C at the time when the measurement information D is acquired is updated based on the measurement information D related to the measurement when measuring the cells. Since the updated culture condition E is acquired as the culture condition C, it is possible to acquire a more optimal culture condition C for generating the target number of cells to be generated each time the cells are measured.

In this way, the optimum culture condition C can be acquired for each measurement at the cell inspection timing based on the culture condition C. Therefore, the person in charge of generating the cell generates the cell based on the optimum culture condition C. It is possible to improve the possibility that the desired number of cells to be generated can be generated as compared with the conventional method.

In addition, in the above-described embodiment, the first derivation unit 29, the second derivation unit 30, and the third derivation unit 31 include the learned model M, but the technique of the present disclosure is not limited to this. Not limited. If the first derivation unit 29 can derive the information F indicating whether or not the desired cell can be generated based on the history information A, the cell information B, and the measurement information D, the first derivation unit 29 uses machine learning. Alternatively, a correspondence table between the history information A and the cell information B, the measurement information D, and the information F indicating whether or not the information can be generated, and a calculation formula may be used. Moreover, if the second derivation unit 30 can derive the culture condition C based on the history information A and the cell information B, the history information A and the cell information B and the culture condition can be obtained without using machine learning. You may use the correspondence table with C, a calculation formula, etc. Similarly to the second derivation unit 30, the third derivation unit 31 also derives the updated culture condition E based on the measurement information D and the culture condition C acquired when the measurement information D was acquired. If possible, any one of the correspondence table and the calculation formula may be used.

Further, the culture condition acquisition unit 22 of the above-described embodiment may further acquire, as the culture condition C, the updated culture condition E that is updated by adding weighting based on the information D1 regarding the measuring means. For example, the person A who is good at fine work and the person B who is not good at fine work may have different effects on cell generation even if the same process is performed. Also in the method used for measurement, the influence on the generation of cells may differ between the visual inspection by the person in charge and the measurement using the measuring device. Therefore, the weighting based on the information D1 regarding the measuring means, that is, the weighting based on the information D1 regarding the measuring means, which is more accurate in the measurement result, is added to the updated renewed culture condition E. The more optimal culture condition C can be obtained as compared with the above-described embodiment.

Further, the learned model M of the second embodiment described above has new measurement information D and the updated culture condition E derived from the culture condition C acquired at the time of acquisition of this measurement information D, and the target. The learning is performed so as to output the probability that a target number of cells can be generated and the output, but the present invention is not limited to this, and instead of the culture condition C, the history information A and the cell information B are included. The updated culture condition E may be derived from the information obtained.

Further, the notification unit 14 of the above-described embodiment displays that cells can be generated in the first embodiment, displays that cells can be generated, and culture condition C in the second embodiment. However, in addition to these, in addition to these, the probability that the cell indicated by the cell information B can be generated, the history information A, the cell information B, the acquired measurement information D, that is, the information D1 regarding the measurement means and the measurement information are obtained. You may make it display the information D2 which shows a measurement result, and the successive change etc. of the information D2 which shows the measurement result obtained by measurement.

The processing described in the above embodiment is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be changed without departing from the spirit of the technology of the present disclosure.

Further, the cell generation support device 1 which is an embodiment of the technique of the present disclosure can be appropriately modified in design without departing from the gist of the technique of the present disclosure.

1 Cell creation support device

11 CPU

12 memories

13 Storage

14 Notification section

15 Input section

21 Information Acquisition Department

22 Culture condition acquisition unit

23 Generatable Information Acquisition Unit

29 First derivation unit

30 Second derivation unit

31 Third derivation part

40 cell donors

41 blood cells

42 skin cells

43,44,45,46 iPS cells

47 nerve cells

48 cardiomyocytes

49 Liver cells

A career information

B cell information

B1 cell type

B2 target cell number

B3 cell status

B4 Number of days to complete generation

C culture conditions

D measurement information

Information about D1 measuring means

Information indicating the measurement results obtained by D2 measurement

E Renewal culture conditions

M trained model (first trained model, second trained model, third trained model)

P information set (second information set)

Q information set (first information set)

G information set (third information set)

R information set

S information set

T1 initialization process

T1 initial process

T2 culture process

T3 differentiation process

X cell donor

Claims (18)

1. 
   An information acquisition unit that acquires history information indicating the history of cells to be used and cell information of cells to be generated,
   
   The history information obtained by the information acquisition unit, the cell information, and an initialization step of initializing cells, a culture step of culturing cells, and a differentiation step of inducing differentiation of cells A cell generation support device including a generation-capable information acquisition unit that acquires information on whether or not the desired cell can be generated for each cell measurement based on measurement information related to the measurement.
   
2. 
   The generable information acquisition unit includes a first derivation unit that derives the information on the generability based on the history information and the cell information acquired by the information acquisition unit, and the measurement information. The cell generation support device described in 1.
   
3. 
   The first derivation unit includes history information indicating a history of cells used, cell information of cells to be generated, an initialization step of initializing cells, a culture step of culturing cells, and a differentiation step of inducing differentiation of cells. In any one of the steps, whether or not it is possible to generate a first set of information including measurement information related to measurement when measuring cells and a cell to be generated corresponding to the first set of information The cell generation support device according to claim 2, further comprising a first learned model that has been machine-learned using learning information that includes a plurality of information sets with the information.
   
4. 
   The cell generation support device according to claim 1, wherein the measurement information is at least one of information related to the measurement unit and information indicating a measurement result obtained by the measurement by the measurement unit.
   
5. 
   The information related to the measuring means includes information indicating one of the method used for measurement and the person in charge of measurement,
   
   The cell generation support apparatus according to claim 4, wherein the information indicating the measurement result includes any one or more of the measured cell state, medium state, and presence/absence of bacteria.
   
6. 
   The measured cell state is any one of cell shape, cell color, cell number, cell size, cell odor, cell gene expression, cell metabolite type, and cell protein. The cell generation support device according to claim 5, including one or more pieces of information.
   
7. 
   The history information is the name of the cell donor who is the holder of the cells to be used, the sex of the cell donor, the blood type of the cell donor, the race of the cell donor, the age of the cell donor, the 7. The disease history of the cell donor, the immune information of the cell donor, and the disease history of the related person of the cell donor, including any one or more pieces of information. Cell generation support device.
   
8. 
   8. The cell information according to claim 1, wherein the cell information includes any one or more of a kind of cells to be generated, a number of cells to be generated, a state of cells to be generated, and a number of days to complete the generation. The cell generation support device described in 1.
   
9. 
   The cell generation support device according to claim 1, further comprising a notification unit that notifies information about whether or not the cells to be generated can be generated.
   
10. 
    A culture condition acquisition unit that acquires culture conditions for culturing cells based on the history information and the cell information acquired by the information acquisition unit when the generation-capable information acquisition unit acquires the generation-enabled information. The cell generation support device according to claim 1, further comprising:
    
11. 
    The culture condition acquisition unit is related to measurement when measuring cells in any one of an initialization step of initializing cells, a culture step of culturing cells, and a differentiation step of inducing differentiation of cells. The cell generation support device according to claim 10, wherein an updated culture condition updated based on measurement information is acquired as the culture condition.
    
12. 
    The culture condition acquisition unit,
    
    A second derivation unit that derives the culture condition based on the history information and the cell information acquired by the information acquisition unit, and a second derivation unit that derives the updated culture condition updated based on the measurement information as the culture condition. The cell generation support device according to claim 11, further comprising:
    
13. 
    The second derivation unit is an information set of a second set of information including history information indicating a history of cells to be used and cell information of cells to be generated, and culture conditions corresponding to the second set of information. A second trained model machine-learned using learning information including a plurality of
    
    The third derivation unit relates to measurement when measuring cells in any one of an initialization step of initializing cells, a culture step of culturing cells, and a differentiation step of inducing differentiation of cells. Machine learning is performed using the learning information including a plurality of third information sets including measurement information and culture conditions, and updated culture conditions corresponding to the third information set. The cell generation support device according to claim 12, which includes a model.
    
14. 
    The cell generation support device according to claim 13, wherein the second learned model and the third learned model are configured as one learned model.
    
15. 
    The culture condition is any one of the following: information on cells used, number of cells used, type of container used, type of culture medium used, type of additives used, treatment timing, and person in charge. The cell generation support device according to any one of claims 10 to 14, including the above.
    
16. 
    The timing of the treatment includes any one or more of a timing of seeding, a timing of subculture, a timing of exchanging a medium, a timing of adding an additive, and a timing of examining cells. Cell generation support device.
    
17. 
    Acquire the history information showing the history of the cells to be used and the cell information of the cells you want to generate,
    
    Related to measurement when measuring cells in any one of the history information, the cell information, and an initialization step of initializing cells, a culture step of culturing cells, and a differentiation step of inducing differentiation of cells A cell generation support method for acquiring information on whether or not the desired cell can be generated for each cell measurement based on the measured information.
    
18. 
    A procedure for acquiring history information indicating the history of cells to be used and cell information of cells to be generated,
    
    Related to measurement when measuring cells in any of the history information, the cell information, and an initialization step of initializing cells, a culture step of culturing cells, and a differentiation step of inducing differentiation of cells A cell generation support program that causes a computer to execute a procedure for acquiring information on whether or not the desired cell can be generated for each cell measurement based on the measurement information.

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