Apparatus for determining optimal stem cell culture medium combination, method for determining optimal combination by calculating differentiation success rate of culture medium combinations, and computer program

Abstract

Disclosed according to embodiments of the present disclosure a method for determining an optimal combination by calculating success rates for culture medium combinations, the method comprising the steps of: acquiring, by a determination apparatus, combinations of first culture medium conditions and a combination of second culture medium conditions including growth factors for a first stem cell, concentration values of the growth factors, and injection time points of the growth factors from a user; inputting, by the determination apparatus, the combinations of the first and the second culture media conditions and information about the first stem cell into an optimal condition determination model to generate a distribution of results for the combinations of the first and the second culture medium conditions and to output one optimal combination among the combinations of the first and the second culture media conditions; and outputting, by the determination apparatus, output data to visually output the optimal combination of conditions for the first stem cells.

Description

Device for determining optimal stem cell culture medium combinations, method for determining optimal combinations by calculating differentiation success rates for culture medium combinations, and computer program

[0001] An embodiment of the present disclosure relates to a device for determining an optimal stem cell culture medium combination, a method for calculating survival rates for culture medium combinations to determine an optimal combination, and a computer program, wherein the device is characterized in that, for a plurality of combinations of culture medium conditions input by a user, the device determines an optimal combination of culture medium conditions by comparing combinations of a plurality of culture medium conditions using reproducibility and differentiation success rates of tumor cells cultured with the combinations of culture medium conditions.

[0002] Cancer is one of the most common causes of death worldwide. Approximately 10 million new cases occur each year, accounting for approximately 12% of all deaths, making it the third leading cause of death.

[0003] To develop treatments for cancer, analysis and research on stem cells are necessary, so sample stem cells are cultured and used for research and analysis.

[0004] Cultured stem cells can be used to analyze the genetic or molecular characteristics of tumors. Furthermore, cultured stem cells can be utilized for new drug testing, studying tumor growth and metastasis mechanisms, and screening assays for drug discovery and testing. Furthermore, cultured stem cells can be used to create cancer models for in vitro and in vivo experiments, such as patient-derived organoids and xenografts.

[0005] Culture medium conditions are crucial for culturing stem cells. The amount of cells cultured varies greatly depending on the culture medium conditions. Stem cell culture medium conditions vary depending on various factors, including the type of stem cell, the desired experimental outcome, and the availability of resources. Stem cells cultured in inadequate medium conditions cannot be used for research and / or analysis.

[0006] Therefore, there is an emerging need to provide optimized culture medium conditions for the collected stem cells.

[0007] The embodiments disclosed herein aim to provide a device and method for determining a combination of optimal culture medium conditions for cells by using a result distribution for results such as differentiation success rate and reproducibility of cells cultured with a combination of multiple culture medium conditions input by a user.

[0008] According to embodiments of the present disclosure, a method may include a step in which a decision device obtains, from a user, a combination of first culture medium conditions including growth factors and concentration values ​​of the growth factors for first stem cells, and a combination of second culture medium conditions; a step in which the decision device inputs the combination of the first and second culture medium conditions and information on the first stem cell into an optimal condition determination model, and outputs a result distribution map for combinations of the first and second culture medium conditions and one optimal condition combination among the combinations of the first and second culture medium conditions; and a step in which the decision device outputs output data that visually outputs the optimal condition combination for the first stem cell.

[0009] The above optimal condition determination model may be a model trained using deep learning or machine learning, with inputs of combinations of multiple culture medium conditions and distributions of results cultured under multiple culture medium conditions for multiple stem cells.

[0010] The above optimal condition determination model can generate a result distribution by sampling experimental results cultured with each combination of culture medium conditions.

[0011] The above optimal condition decision model can generate distribution maps of results sampled using the Thompson sampling method.

[0012] The above optimal condition determination model may be a reinforcement learning model through a process of re-learning with the result values ​​obtained by increasing the number of samplings.

[0013] The above result distribution may be a visual representation of the differentiation success rate and number of occurrences obtained for a combination of culture medium conditions.

[0014] A computer program according to an embodiment of the present invention can be stored in a medium to execute any one of the methods according to an embodiment of the present invention using a computer.

[0015] In addition, other methods for implementing the present invention, other systems, and computer-readable recording media recording a computer program for executing the above methods are further provided.

[0016] Other aspects, features and advantages other than those described above will become apparent from the following drawings, claims and detailed description of the invention.

[0017] According to any one of the aforementioned problem solving means, for a plurality of combinations of culture medium conditions input by a user, a distribution of results for results such as differentiation success rate and reproducibility of cells cultured with the combinations of culture medium conditions can be used to determine a combination of optimal culture medium conditions for stem cells.

[0018] Figure 1 is a block diagram of a device for determining the optimal organoid culture medium combination according to embodiments of the present disclosure.

[0019] Figure 2 is a block diagram of the optimal condition determination unit.

[0020] Figure 3 is a block diagram of a learning device that learns an optimal condition decision model.

[0021] Figure 4 is an exemplary diagram of combinations of culture medium conditions for stem cells to be differentiated into vascular cells.

[0022] FIG. 5a, FIG. 5b, and FIG. 5c are exemplary drawings of result distributions generated according to embodiments of the present disclosure.

[0023] Figure 6 is a flowchart of an optimal condition output method according to embodiments of the present disclosure.

[0024] Figure 7 is a flowchart of an optimal condition output method according to other embodiments of the present disclosure.

[0025] The configuration and operation of the present invention will be described in detail with reference to embodiments of the present invention illustrated in the attached drawings below.

[0026] The present invention is capable of various modifications and embodiments. Specific embodiments are illustrated in the drawings and described in detail in the detailed description. The effects and features of the present invention, as well as the methods for achieving them, will become clearer with reference to the embodiments described in detail below, along with the drawings. However, the present invention is not limited to the embodiments disclosed below and can be implemented in various forms.

[0027] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. When describing with reference to the drawings, identical or corresponding components are given the same drawing reference numerals, and redundant descriptions thereof will be omitted.

[0028] Hereinafter, the term “upper” or “upper” may include not only things that are directly above in contact, but also things that are above in a non-contact manner.

[0029] In the following examples, the terms first, second, etc. are not used in a limiting sense, but are used for the purpose of distinguishing one component from another.

[0030] In the examples below, singular expressions include plural expressions unless the context clearly indicates otherwise.

[0031] In the following examples, terms such as “include” or “have” mean that a feature or component described in the specification is present, and do not preclude the possibility that one or more other features or components may be added.

[0032] For convenience of explanation, the sizes of components in the drawings may be exaggerated or reduced. For example, the sizes and thicknesses of each component shown in the drawings are arbitrarily indicated for convenience of explanation, and thus the present invention is not necessarily limited to what is shown.

[0033] Additionally, terms such as “…part”, “…area”, etc. described in this specification may mean a unit that processes at least one function or operation.

[0034] Stem cell culture can be used in the treatment of diseases such as cancer. Cultured stem cells can be used to study characteristics such as tumor initiation, growth, and resistance. Cultured stem cells can be used to conduct high-throughput drug screening to study compounds that can target cancer cells. Cultured stem cells can be used to establish patient-derived cancer cell lines and conduct tests for drug sensitivity. Cultured stem cells can also be used to study intratumoral heterogeneity.

[0035] As described above, stem cells, which are utilized in various ways, may have different differentiation success rates depending on changes in the concentrations of culture conditions. Since stem cells can differentiate into different cell types depending on growth factors, concentrations, media, etc., culture conditions for stem cells may include growth factors, concentrations, media, and timing.

[0036] In this specification, genomic data may include WES (Whole exome sequencing) and sequencing data, and may include data obtained by various analysis methods.

[0037] WES (Whole exome sequencing) sequences approximately 50 Mbp at approximately 100X, specifically obtaining only the exome region, and can obtain a relatively greater read depth than WGS (Whole genome sequencing). It is a method of analyzing only the genome of the exon region that directly codes for proteins. In the case of humans, exons account for less than 2% of the entire genome, so it is cheaper to acquire than WGS and the data capacity is also reduced. Even when analyzing WGS, variants in non-coding regions are difficult to interpret and may produce highly meaningful results. To solve this problem, analysis using WES is more economical and can obtain information at a higher depth.

[0038] RNA is an abbreviation for ribonucleic acid, and together they are called nucleic acids. Nucleic acids are polymers composed of multiple nucleotides. The nucleotides that make up DNA (deoxyribonucleic acid) are specifically called deoxyribonucleotides, and the nucleotides that make up RNA (ribonucleic acid) are specifically called ribonucleotides.

[0039] FIG. 1 is a block diagram of an optimal organoid culture medium combination determination device (100, hereinafter, determination device) according to embodiments of the present disclosure.

[0040] The decision device (100) according to embodiments of the present disclosure can determine optimal culture medium conditions for stem cells. The decision device (100) can determine optimal culture medium conditions by considering cells to be differentiated. The decision device (100) can obtain, through various methods, result values ​​such as differentiation success rate and reproducibility of cells cultured with combinations of culture medium conditions input by a user, and can generate a result distribution map of result values ​​for a plurality of combinations of culture medium conditions to determine an optimal combination of culture medium conditions among the combinations of culture medium conditions. The decision device (100) can generate a result distribution map by sampling result values ​​for combinations of culture medium conditions. The decision device (100) can determine an optimal combination of culture medium conditions for a given stem cell by updating result values ​​for combinations of culture medium conditions to update the result distribution map. At this time, the result device (100) can update the result distribution using compensation for the Bayesian probability distribution.

[0041] The decision device (100) can obtain data on combinations of culture medium conditions for a given stem cell from user input. The decision device (100) can generate a plurality of combinations of culture medium conditions by changing the concentration values ​​of each growth factor based on the growth factors input by the user. The culture medium conditions can include growth factors, concentration values ​​of the growth factors, information on supplements added during culturing, the timing (time point) of adding each growth factor, the medium, etc. The decision device (100) can obtain result values ​​of the results of culturing stem cells using combinations of culture medium conditions from a culture experiment or a database, generate result distributions for each combination of conditions, and determine an optimal combination of culture medium conditions for stem cells using the generated result distributions. At this time, the result distributions for combinations of a plurality of culture medium conditions can be resampled using result values ​​of additional culture experiments. The decision device (100) can determine an optimal combination of stem cell culture medium conditions using an optimal condition determination model learned from the results of culture experiments performed under combinations of culture medium conditions. The decision device (100) can store information about the differentiation process for cells to be differentiated in advance. Based on the information about the differentiation process, the decision device (100) can generate combinations in which the injection times of growth factors are randomly set.

[0042] An optimal condition decision model can be trained to output a distribution of results for culture medium conditions by taking as input a combination of conditions including stem cell information, growth factors, growth factor concentrations, and injection points, and outputting results for culture medium conditions. The optimal condition decision model can be constructed to sample the distribution of results for each condition and output the optimal combination of conditions. The optimal decision model can periodically update the distribution of results for each culture medium condition by updating the input data.

[0043] Information about stem cells may include the type of stem cell, the type of cell to be differentiated (insulin-producing cells, retinal cells, fat cells, heart muscle cells, skin cells, bone cells, etc.), information about the subject of the stem cell (age, sex, etc.), and information about mutations.

[0044] Examples of growth factors in the culture medium conditions may further include VEGF (Vascular Endothelial Growth Factor), EGF (Epidermal Growth Factor), BMPs (Bone Morphogenetic Proteins), fatty acids, temperature, pH value, oxygen concentration, glucose concentration, illumination conditions, and concentration values. For example, the culture medium conditions for differentiating induced pluripotent stem cells (iPSCs) into neural cells (hypothalamic neural cells) may include BDNF (Brain-Derived Neurotrophic Factor) and may include the timing of injection of BDNF (Brain-Derived Neurotrophic Factor). The differentiation process of stem cells into vascular cells may include a cell aggregation step, a mesoderm induction step, a vascular lineage induction step, a vascular network formation step, and a cell maturation step. The timing of injection of the growth factor can be selected by each stage (cell aggregation, mesodermal differentiation, vascular lineage differentiation, vascular network formation, and cell maturation), and by the date of injection within the stage (e.g., day 1, day 2). The cell differentiation process can be set differently for each cell to be differentiated.

[0045] As illustrated in FIG. 1, the decision device (100) according to some embodiments may include a processor (110), an input / output unit (130), a memory (140), a communication unit (150), and an optimal condition determination unit (120). However, not all of the components illustrated in FIG. 1 are essential components of the decision device (100). The decision device (100) may be implemented with more components than the components illustrated in FIG. 1, or with fewer components than the components illustrated in FIG. 1. The decision device (100) may be a user terminal, a server, a network system, or a separate device.

[0046] According to one embodiment of the present disclosure, the processor (110) typically controls the overall operation of the decision device (100). For example, the processor (110) may control the components included in the decision device (100) by executing a program stored in the decision device (100).

[0047] According to one embodiment of the present disclosure, the processor (110) can determine optimal culture medium conditions for stem cells. The processor (110) can determine optimal culture medium conditions to be applied to the differentiation process by considering the type of cell to be differentiated. The culture medium conditions can be changed for each stage of the differentiation process. The culture medium conditions can include a change timing. The processor (110) can obtain, through various methods, result values ​​such as differentiation success rate and reproducibility of stem cells cultured with combinations of culture medium conditions input by a user, and generate a result distribution map of result values ​​for a plurality of combinations of culture medium conditions to determine an optimal combination of culture medium conditions among the combinations of culture medium conditions. The processor (110) can generate a result distribution map by sampling result values ​​for combinations of culture medium conditions. The processor (110) can determine an optimal combination of culture medium conditions for a given stem cell by updating the result values ​​for combinations of culture medium conditions to update the result distribution map. At this time, the processor (110) can update the result distribution using compensation for the Bayesian probability distribution.

[0048] The processor (110) can obtain data on combinations of culture medium conditions for a given stem cell from user input. In another embodiment, the processor (110) can generate combinations of multiple culture medium conditions by changing the concentration value of each growth factor based on the growth factors input by the user. In yet another embodiment, the processor (110) can generate combinations of multiple culture medium conditions including stepwise changes in the concentration value of each growth factor based on the growth factors input by the user. The processor (110) can obtain result values ​​of the results of culturing stem cells using combinations of culture medium conditions from culture experiments or a database, generate result distributions for each condition combination, and determine an optimal combination of stem cell culture medium conditions using the generated result distributions. At this time, the result distributions for combinations of multiple culture medium conditions can be resampled using result values ​​of additional culture experiments. The processor (110) can determine an optimal combination of culture medium conditions for stem cells using an optimal condition determination model learned from results of culture experiments performed with combinations of culture medium conditions. The processor (110) can store information about the differentiation process for cells to be differentiated in advance. The processor (110) can generate combinations in which injection times of growth factors are randomly set based on the information about the differentiation process. The optimal condition determination model can be learned to output a result distribution map for culture medium conditions by inputting a combination of conditions including information about stem cells, growth factors and concentration values ​​for the growth factors, injection times, etc., and result values ​​for culture medium conditions. The optimal condition determination model can be constructed to sample the result distribution map for each condition and output an optimal combination of conditions. The optimal decision model can update the result distribution map for each culture medium condition by periodically updating the input data.

[0049] The processor (110) is a component for controlling the overall operation of the decision device (100). Specifically, the processor (110) controls the overall operation of the decision device (100) using various programs stored in the storage medium (150) of the decision device (100). For example, the processor (110) may include a CPU, RAM, ROM, and a system bus. Here, the ROM is a component in which a command set for system booting is stored, and the CPU copies the stored operating system of the decision device (100) to the RAM according to the command stored in the ROM and executes the O / S to boot the system. When the system booting is complete, the CPU can copy various stored applications to the RAM and execute them to perform various operations. Although the service provision server (100) has been described as including only one CPU in the above, it may be implemented with multiple CPUs (or DSPs, SoCs, etc.) during implementation.

[0050] According to one embodiment of the present invention, the processor (110) may be implemented as a digital signal processor (DSP), a microprocessor, or a time controller (TCON) that processes digital signals. However, the present invention is not limited thereto, and may include one or more of a central processing unit (CPU), a micro controller unit (MCU), a micro processing unit (MPU), a controller, an application processor (AP), a communication processor (CP), or an ARM processor, or may be defined by the corresponding terms. In addition, the processor (110) may be implemented as a system on chip (SoC) or large scale integration (LSI) having a built-in processing algorithm, or may be implemented in the form of a field programmable gate array (FPGA).

[0051] According to one embodiment of the present disclosure, the input / output unit (130) can display an interface generated by the memory (140) of the decision device (100). According to one embodiment of the present invention, the input / output unit (130) can display a user interface for an input user input. The input / output unit (130) can output stored graphic data, visual data, auditory data, and vibration data under the control of the memory (140).

[0052] The input / output unit (130) may be implemented as a display panel of various forms. For example, the display panel may be implemented as a display technology of various forms, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), an active-matrix organic light-emitting diode (AM-OLED), a liquid crystal on silicon (LCoS), or a digital light processing (DLP). In addition, the input / output unit (130) may be coupled to at least one of the front area, the side area, and the rear area of ​​the display panel in the form of a flexible display.

[0053] The input / output unit (130) may be implemented as a touch screen with a layer structure. The touch screen may have a function of detecting not only a display function but also a touch input location, a touched area, and even a touch input pressure, and may also have a function of detecting not only a real touch but also a proximity touch.

[0054] The input / output unit (130) may include a user interface for inputting various information into the decision device (100).

[0055] According to one embodiment of the present disclosure, the memory (140) may store a program for processing and controlling the processor (110) and / or the optimal condition determination unit (120), and may also store data input to or output from the determination device (100). According to one embodiment of the present disclosure, the memory (140) may store information on documents, and may also store information on keywords and questions. The memory (140) may include a database storing the above information.

[0056] According to one embodiment of the present disclosure, the memory (140) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a RAM (Random Access Memory), a SRAM (Static Random Access Memory), a ROM (Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), a PROM (Programmable Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk. In addition, according to one embodiment of the present disclosure, programs stored in the memory (140) may be classified into a plurality of modules according to their functions.

[0057] According to one embodiment of the present disclosure, the communication unit (150) can communicate with an external device of the processor (110). For example, the communication unit (150) can communicate with an external device, such as a payment server or an authentication server, under the control of the processor (110). In addition, the communication unit (150) can also obtain user information or user input through communication with an external interface.

[0058] The optimal condition determination unit (120) may include a data input unit (121), an optimal combination output unit (122), a result update unit (123), and an output data generation unit (124), as illustrated in FIG. 2.

[0059] The data input unit (121) can obtain a combination of multiple stem cell culture medium conditions from user input. The combination of culture medium conditions may include growth factors, concentration values, medium, injection times of growth factors, etc. For example, the combination of culture medium conditions may include CHIR, BMP4, concentration values ​​of CHIR, concentration values ​​of BMP4, injection times of CHIR, and injection times of BMP4, as illustrated in FIG. 4.

[0060] The data input unit (121) can input combinations of multiple conditions. The data input unit (121) can input combinations of conditions including growth factors. The data input unit (121) can input combinations of conditions including information about stem cells and growth factors.

[0061] The optimal combination output unit (122) can input a combination of multiple stem cell culture medium conditions into the optimal condition determination model and sample the result distribution for each success rate. The optimal condition determination model can generate a large amount of data on the results of culture for each combination of culture medium conditions. The optimal condition determination model can output an optimal condition combination based on the result data for each combination.

[0062] The result update unit (123) can update the result distributions using compensation data including the result values ​​for stem cells cultured under the optimal condition combination in the optimal decision model. The result update unit (123) can update the result distributions through random sampling based on the Bayesian probability distribution. The result values ​​for the stem cells can include the differentiation success rate of the cultured stem cells (the number of cells that have successfully differentiated / the total number of cells), the match rate of oncogenic mutations with the patient tissue, the reproducibility of the stem cells (comparison of the size, number, etc. with actual stem cells, correlation coefficient), tumor purity, etc. The result value for the stem cells can be at least one of the result values ​​for the stem cells, the average value, or the combined value of the result values ​​for the organoid cells. Here, the tumor purity can be a value determined using in-silico based tumor purity estimation algorithms. More specifically, tumor purity can be calculated using DNA-seq-based methods such as ABSOLUTE, Sequenza, and FACETS, and RNA-seq (expression)-based methods such as ESTIMATE and PUREE.

[0063] Here, the cancer mutation match rate with patient tissue can be calculated based on mutant genes defined in a previously registered database. Examples of such databases include, but are not limited to, OncoKB and COSMIC DB.

[0064] The success rate and / or reproducibility of stem cell differentiation can be input by the user based on the result data for cultured stem cells. The success rate and / or reproducibility of stem cell differentiation can be calculated using established rules, mathematical formulas, etc.

[0065] The result update unit (123) can update the optimal condition determination model to update the result distributions based on the compensation data.

[0066] The optimal combination output unit (122) can output the optimal condition combination using the optimal condition determination model by the result update unit (123). The optimal condition determination model can output result distributions for each combination of input conditions. The optimal condition determination model can output the optimal condition combination among the input combinations using these result distributions.

[0067] The output data generation unit (124) can generate output data including the optimal condition combination from the optimal combination output unit (122).

[0068] The decision device (100) according to embodiments of the present disclosure can output an optimal combination of conditions from among a plurality of combinations of conditions devised by a researcher. The decision device (100) can output optimal values ​​for concentration values ​​of growth factors for conditions for stem cells and injection timings of the growth factors (information on differentiation stages). The decision device (100) can output the injection timings of concentration values ​​of growth factors for conditions for stem cells as optimal timing values. The decision device (100) can output an optimal combination of conditions using an optimal condition decision model that updates a result distribution by reapplying the result data obtained by experimentally culturing with a combination of conditions. The result distribution can be generated by sampling result data for combinations of conditions. Methods for sampling the resulting data include, but are not limited to, Thomson Sampling, greedy methods, upper confidence bound (UCB), Bayesian optimization, Monte Carlo sampling, and bootstrapping. The resulting distribution can be generated by beta-distributing the resulting data for a combination of conditions.

[0069] Figure 3 is a block diagram of a learning device (300) that learns an optimal condition decision model.

[0070] The learning device (300) can train an optimal condition decision model. The optimal condition decision model can generate result distributions for each combination of input conditions using various sampling methods, and compare the result distributions to output the optimal condition combination. More specifically, the optimal condition decision model can generate result distributions using the Thompson sampling method.

[0071] The result distribution may be a two-dimensional data representation of the result values ​​cultured with each combination. The result distribution may be generated as two-dimensional data with the x-axis representing the success rate and the y-axis representing the density, as illustrated in Fig. 5a. The result distribution may be expressed as a sampling of the result values ​​cultured with each combination. The result values ​​for stem cells may include the differentiation success rate of cultured stem cells (the number of cells that have successfully differentiated / the total number of cells), the reproducibility of stem cells (comparison of size, number, etc. with actual stem cells, correlation coefficient), tumor purity, etc. The y-axis value of the result distribution may be at least one of the result values ​​for stem cells, the average value, or the combined value of the result values ​​for stem cells. Here, the tumor purity may be a value determined using in-silico-based tumor purity estimation algorithms. More specifically, tumor purity can be calculated using DNA-seq-based methods such as ABSOLUTE, Sequenza, and FACETS, and RNA-seq (expression)-based methods such as ESTIMATE and PUREE.

[0072] In an optional embodiment, the optimal condition determination model can generate combinations of multiple conditions by a process of randomly generating multiple combinations including several changed concentration values, injection timings of growth factors, etc. for the input growth factors.

[0073] The dataset generation unit (310) can generate training data sets using combinations of conditions including information about stem cells, growth factors and concentration values ​​for the growth factors, injection times of the growth factors, and culture result values. For example, training data sets can be generated using four combinations of conditions for stem cells of lung cancer cells, combinations applying different concentration values ​​and injection times of growth factors for stem cells of breast cancer cells, and so on. The dataset generation unit (310) can generate training data sets using combinations of different growth factors depending on the type of cell to be differentiated.

[0074] The model learning unit (320) uses training data sets as input and generates, for each combination, distributions of results sampled using a sampling method such as Thompson sampling, and can train an optimal condition determination model using the distributions of results for each combination generated in this way.

[0075] The optimal condition determination model can output an optimal condition combination by inputting combinations of conditions including information about stem cells, growth factors and concentration values ​​for the growth factors, and timing of changes in the concentration values.

[0076] The model learning unit (320) can learn an optimal condition determination model that generates a result distribution using data obtained by performing a culture experiment with a combination of input conditions for stem cells.

[0077] The data obtained through the culture experiment may be data stored in a registered database and / or data experimentally obtained by the research team.

[0078] The model learning unit (320) can learn a model that generates a result distribution by statistically sampling result values ​​for stem cells cultured under conditions including growth factors, concentration values ​​for growth factors, injection time points for growth factors, etc.

[0079] The model learning unit (320) can build an optimal condition determination model that compares the result distributions for each combination of conditions and outputs the optimal combination of conditions.

[0080] The model learning unit (320) can update the result distributions by changing the number of samplings performed for culture experiments based on a combination of conditions. For example, the number of samplings may increase as the training data set grows. Results obtained with an increased number of samplings allow for the determination of a combination of culture medium conditions for stem cells with a higher differentiation success rate and / or reproducibility compared to results obtained with a lower number of samplings.

[0081] As shown in Fig. 5a, the larger the x-axis value at the peak point of the result distribution and the larger the y-axis value, the more optimal the combination of culture medium conditions can be. In Fig. 5a, condition 1 is the optimal combination of conditions.

[0082] The model learning unit (320) can generate the result distributions of Fig. 5b by changing the number of samplings. According to the result distribution of Fig. 5b, considering the location of the peak, condition 1, which has the highest success rate and density, can be the optimal condition combination. The model learning unit (320) can generate the result distribution of Fig. 5c through a process of updating the beta distribution. According to the result distribution of Fig. 5c, it can be inferred that condition 2 is the most optimal condition combination. By increasing the number of random samplings and applying the beta distribution method, condition 2 can be output.

[0083] The model learning unit (320) can build an optimal condition determination model that outputs an optimal condition combination using the result distributions updated in this way.

[0084] The model learning unit (320) can periodically update the results of experimental cultivation using a combination of input conditions to update the result distribution.

[0085] The learning device (300) can generate an optimal condition determination model through the above learning process and transmit it to the optimal condition determination device (100) to output an optimal condition combination.

[0086] Figure 4 is an exemplary diagram of combinations of culture medium conditions for stem cells to be differentiated into vascular cells.

[0087] Figure 6 is a flowchart of an optimal condition output method according to embodiments of the present disclosure.

[0088] In S110, the decision device (100) can receive a combination of first culture medium conditions and a combination of second culture medium conditions for the first stem cells from the user. The combination of the first or second culture medium conditions can include one or more growth factors, concentration values ​​for each of the growth factors, injection timing of the growth factors, a medium, etc. Information about the medium can include information about one or more growth factors included in the medium. Information about injection timing of the growth factors can include information about the injection timing in the differentiation stage, etc.

[0089] In S120, the decision device (100) inputs a combination of first and second culture medium conditions and a first stem cell into an optimal condition determination model, and can output a result distribution for combinations of the first and second culture medium conditions and an optimal condition combination among the combinations of the first and second culture medium conditions.

[0090] The optimal condition determination model can acquire result data from experimental culture of first stem cells using a combination of first culture medium conditions through various routes, and can generate a first result distribution by sampling the result data thus acquired. The optimal condition determination model can acquire result data from experimental culture of first stem cells using a combination of second culture medium conditions through various routes, and can generate a second result distribution by sampling the result data thus acquired. The optimal condition determination model can compare the first and second result distributions to determine a result distribution having a higher result value, and output the combination of culture medium conditions of the result distribution as the optimal condition combination. The optimal condition determination model can update the first and second result distributions by updating the result data from experimental culture of the first stem cells.

[0091] In S130, the decision device (100) can output output data that visually displays the optimal condition combination for the first stem cell. There may be two or more combinations of input culture medium conditions. If the stem cell is changed, the output optimal condition combination may also change.

[0092] Through this, the method for determining optimal conditions according to embodiments of the present disclosure can determine a combination of culture medium conditions predicted to have optimal results (differentiation success rate, reproducibility, etc.) among several combinations of culture medium conditions suggested by a user. The method for determining optimal conditions can determine a combination of culture medium conditions predicted to have optimal results by updating the distribution of results of culture medium conditions by updating the results of experimental culture on first stem cells.

[0093] Figure 7 is a flowchart of an optimal condition output method according to other embodiments of the present disclosure.

[0094] In S210, the decision device (100) may receive first growth factors for second stem cells from the user. In this way, the decision device (100) may not input concentration values ​​for each growth factor or injection timing for each growth factor.

[0095] In S220, the decision device (100) can input first growth factors for the second stem cells into an optimal condition determination model to output an optimal condition combination for the second stem cells. The optimal condition determination model can generate a plurality of combinations of culture medium conditions for the second stem cells using previously input data and generate result distributions for each of the combinations of culture medium conditions generated in this way. The optimal condition determination model can output an optimal condition combination by comparing the result values ​​of the result distributions.

[0096] In this way, the decision device (100) can output an optimal condition combination among the combinations of culture medium conditions determined by itself by determining the concentration value for each growth factor or the injection time for each growth factor for input including growth factors.

[0097] In S230, the decision device (100) can output output data including the optimal condition combination output by the above process.

[0098] In addition, although the preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above, and various modifications can be made by a person having ordinary skill in the art to which the present invention pertains without departing from the gist of the present invention claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the present invention.

[0099] Therefore, the spirit of the present invention should not be limited to the embodiments described above, and not only the scope of the patent claims described below, but also all scopes equivalent to or equivalently modified from the scope of the patent claims are considered to fall within the scope of the spirit of the present invention.

Claims

1. A step in which a decision device obtains a combination of first culture medium conditions including growth factors for first stem cells and concentration values ​​of the growth factors, and a combination of second culture medium conditions from a user; A step in which the decision device inputs a combination of the first and second culture medium conditions and information about the first stem cell into an optimal condition decision model, and outputs a result distribution for combinations of the first and second culture medium conditions and an optimal condition combination among the combinations of the first and second culture medium conditions; A method for determining an optimal combination by calculating the survival rate for culture medium combinations, comprising: a step of outputting output data that visually outputs an optimal condition combination for the first stem cell by the decision device; 2. In paragraph 1, The above optimal condition decision model is, A method for determining an optimal combination by calculating the survival rate for combinations of culture media conditions for a plurality of stem cells and a distribution of results cultured under a plurality of culture media conditions, using a model trained using deep learning or machine learning.

3. In paragraph 2, The above optimal condition decision model is, A method of determining the optimal combination by calculating the survival rate for culture medium combinations, generating a result distribution by sampling the experimental results cultured under each combination of culture medium conditions.

4. In paragraph 3, The above optimal condition decision model is, A method of determining the optimal combination by calculating the survival rate for culture medium combinations, generating distributions of the results of sampling using the Thompson sampling method.

5. In paragraph 2, The above optimal condition decision model is, A method for determining the optimal combination by calculating the survival rate for combinations of culture media through reinforcement learning through a process of relearning with the obtained results by increasing the number of samplings.

6. In paragraph 1, The above result distribution is, A method for determining an optimal combination by calculating the survival rate for combinations of culture media, which is a visual representation of the differentiation success rate and number of occurrences obtained for combinations of culture media conditions.

7. Including memory, communication unit, and processor, The above processor, Obtain a combination of first culture medium conditions including growth factors for first stem cells and concentration values ​​of the growth factors, and a combination of second culture medium conditions from the user, By inputting the combination of the first and second culture medium conditions and information on the first stem cell into the optimal condition determination model, the result distribution for the combinations of the first and second culture medium conditions and one optimal condition combination among the combinations of the first and second culture medium conditions are output. An optimal stem cell culture medium combination determination device that outputs output data visually outputting the optimal condition combination for the first stem cell.

8. In paragraph 7, The above optimal condition decision model is, A device for determining an optimal stem cell culture medium combination, which is a model learned using deep learning or machine learning, with inputs of combinations of multiple culture medium conditions and distributions of results cultured under multiple culture medium conditions for multiple stem cells.

9. In paragraph 8, The above optimal condition decision model is, A device for determining the optimal stem cell culture medium combination, which samples experimental results cultured under each combination of culture medium conditions and generates a result distribution.

10. In paragraph 9, The above optimal condition decision model is, A device for determining the optimal stem cell culture medium combination, which generates distribution maps of sampling results using the Thompson sampling method.

11. In paragraph 8, The above optimal condition decision model is, A device for determining the optimal stem cell culture medium combination, which is a reinforcement learning device that learns again with the obtained results by increasing the number of sampling times.

12. In paragraph 7, The above result distribution is, A device for determining the optimal stem cell culture medium combination, which visually represents the differentiation success rate and the number of occurrences obtained for a combination of culture medium conditions.

13. A computer program stored in a computer-readable storage medium for executing any one of the methods of clauses 1 to 6 using a computer.

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