Information analysis method and information analysis device

Abstract

An information analysis method according to an embodiment of the present invention includes: setting, in a closed space 20 of which the environment can be controlled, a growth environment for a plant that is the object of genome-editing; preparing multiple kinds of variants in which one gene of the plant is genome-edited and raising the multiple kinds of variants simultaneously in the closed space; creating time-series data 110 in which first data indicating the raising environment of the multiple kinds of variants and second data indicating the growth state of the multiple kinds of variants are recorded in a temporally synchronized manner; and creating, using the time-series data 110 and gene information 111 that relates to the multiple kinds of variants, an analysis model 130 that represents the correlation between the gene information, the raising environment, and the growth state.

Description

[Technical Field]

[0001] The embodiments relate to an information analysis method and an information analysis device. [Background technology]

[0002] Efforts are being made to identify optimal growing environments for mutants and F1 varieties created through genetic recombination and genome editing by monitoring their growth data. Research is also being conducted to identify relationships between past growth data and cultivation conditions and predict growth outcomes. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2004-121093 [Patent Document 2] Japanese Patent Application Publication No. 2021-045063 [Patent Document 3] Japanese Patent Application Publication No. 2017-051118 [Non-patent literature]

[0004] [Non-Patent Document 1] Machiko Fukuda, "Expression of FT-like genes in leaves of leaf lettuce increases with flower bud development at the shoot apex," [online], National Agriculture and Food Research Organization, [searched November 30, 2021], Internet<URL: https: / / www.naro.go.jp / project / results / laboratory / vegetea / 2011 / 113a4_10_04.html> Summary of the Invention [Problem to be solved by the invention]

[0005] However, there were challenges in optimizing and improving the cultivation process, such as it being based on the skills and experience of the researcher, being difficult to apply when trait changes in response to environmental factors are nonlinear, and the inability to calculate optimal environmental conditions from gene expression levels.

[0006] Therefore, the object of the present invention is to establish a method for evaluating the effects of genome editing by combining "research results on breeding and seedling raising in plant factories" with "information and communication technology (data collection, data analysis, numerical simulation, etc.)." [Means for solving the problem]

[0007] An information analysis method according to an embodiment includes the steps of: setting a growth environment for a plant to be genome-edited in a closed space where the environment can be controlled; preparing multiple types of mutants in which one gene of the plant has been genome-edited; simultaneously growing the multiple types of mutants in the closed space; creating time series data in which first data indicating the growth environment of the multiple types of mutants and second data indicating the growth state of the multiple types of mutants are recorded in time synchronization; and using the genetic information and time series data related to the multiple types of mutants to create an analytical model that represents the correlation between the genetic information, the growth environment, and the growth state. [Effects of the Invention]

[0008] According to the information analysis method of the embodiment, it is possible to improve the productivity of superior plant varieties and create vegetation with desired environmental adaptability. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of an information analysis system according to the first embodiment. [Figure 2] FIG. 2 is a block diagram illustrating an example of the configuration of the information analyzing device according to the first embodiment. [Figure 3] FIG. 3 is a conceptual diagram showing an example of the configuration of the artificial plant cultivation site according to the first embodiment. [Figure 4] FIG. 4 is a block diagram illustrating an example of a functional configuration of the information analyzing apparatus according to the first embodiment. [Figure 5] FIG. 5 is a table showing an example of a data format of time-series data in the first embodiment. [Figure 6] FIG. 6 is a flowchart showing an example of analysis processing using the information analysis system according to the first embodiment. [Figure 7] FIG. 7 is a conceptual diagram of an optimization problem related to early prediction and confirmation of genome editing accuracy using an analytical model generated by the analysis processing according to the first embodiment. [Figure 8] FIG. 8 is a conceptual diagram showing an example of a growth simulation performed by the information analyzing device according to the first embodiment. [Figure 9] FIG. 9 is a conceptual diagram showing an example of the configuration of an artificial plant cultivation site according to the second embodiment. [Figure 10] FIG. 10 is a flowchart showing an example of analysis processing using the information analysis system according to the second embodiment. [Figure 11] FIG. 11 is a block diagram showing an example of the configuration of an artificial plant cultivation site according to the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments will be described with reference to the drawings. The embodiments illustrate devices and methods for embodying the technical ideas of the invention. The drawings are schematic or conceptual. In this specification, the same reference numerals are used to refer to components having substantially the same functions and configurations. Numbers and the like added to reference numerals are used to refer to the same reference numerals and to distinguish between similar elements.

[0011] <1> First embodiment The first embodiment relates to a method for predicting the growth outcome of a genome-edited plant (i.e., a mutant) when a certain growth environment is provided, and the changes in traits resulting from genome editing. An information analysis system 1 according to the first embodiment will be described below.

[0012] <1-1> Configuration <1-1-1> Configuration of Information Analysis System 1 1 is a block diagram showing an example of the configuration of an information analysis system 1 according to the first embodiment. As shown in FIG. 1, the information analysis system 1 includes, for example, an information analysis device 10, an artificial plant cultivation site 20, and an environment control device 30.

[0013] The information analysis device 10 is a computer capable of creating an analytical model for plant genome editing by analyzing and learning input data. The information analysis device 10 is configured to be able to communicate with both the artificial plant cultivation site 20 and the environmental control device 30. The network used for communication between the information analysis device 10, the artificial plant cultivation site 20, and the environmental control device 30 may use wireless communication or wired communication. The "analysis model" may also be called a "learning model" or a "growth model."

[0014] The artificial plant cultivation site 20 is a closed space used for growing plants. The artificial plant cultivation site 20 is configured to be able to artificially control the environment (i.e., the plant growth environment) by controlling the environmental control device 30. The artificial plant cultivation site 20 can transmit data related to the growth status and growth environment to the information analysis device 10. The artificial plant cultivation site 20 may also be called a "plant factory," a "closed plant cultivation space," a "completely closed plant factory," or a "completely closed plant factory."

[0015] The environmental control device 30 is a computer that comprehensively controls a plurality of control devices for controlling the environment of the artificial plant cultivation site 20. The environmental control device 30 controls the plurality of control devices based on the control of the information analysis device 10, and forms a desired growth environment in the artificial plant cultivation site 20. The environmental control device 30 can control, for example, the amount of light, air temperature, water temperature, amount of water supply, carbon dioxide concentration, nutrients, etc. in the artificial plant cultivation site 20. The environmental control device 30 may be installed in the artificial plant cultivation site 20.

[0016] <1-1-2> Configuration of information analysis device 10 Fig. 2 is a block diagram showing an example of the configuration of the information analyzing device 10 according to the first embodiment. As shown in Fig. 2, the information analyzing device 10 includes, for example, a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage device 14, and a communication interface 15.

[0017] The CPU 11 is an integrated circuit that can execute various programs and controls the overall operation of the information analyzing device 10.

[0018] The ROM 12 is a non-volatile semiconductor memory that stores programs and control data for controlling the information analyzing device 10.

[0019] The RAM 13 is, for example, a volatile semiconductor memory, and is used as a work area for the CPU 11.

[0020] The storage device 14 is a non-volatile storage device that stores, for example, the system software of the information analyzing device 10 and data acquired via a network.

[0021] The communication interface 15 is a communication circuit configured to be connectable to a network. The information analysis device 10 transfers data (information) received via the communication interface 15 to the RAM 13 or the storage device 14, and can output the analysis results of the data to an external device via the communication interface 15.

[0022] The information analyzing device 10 can realize the functional configuration described below by executing a program. The hardware configuration of the information analyzing device 10 may be other configurations. A display, an input interface, a removable storage device, etc. may be connected to the information analyzing device 10. The information analyzing device 10 may display simulation results, etc. on the display.

[0023] <1-1-3> Configuration of artificial plant cultivation site 20 FIG. 3 is a conceptual diagram showing an example of the configuration of the artificial plant cultivation site 20 according to the first embodiment. As shown in FIG. 3, the artificial plant cultivation site 20 cultivates, in the same cultivation environment, multiple types of plants (mutants) in which different genes (genome-edited strains) have been genome-edited. Specifically, the artificial plant cultivation site 20 cultivates plants that are the subject of genome editing, for example, multiple types of mutants V1, V2, and V3 in which one different gene has been genome-edited. The multiple types of mutants V1, V2, and V3 are arranged so as to be distinguishable within the artificial plant cultivation site 20. The number of types of mutants grown in the artificial plant cultivation site 20 need only be multiple, and is not limited to three.

[0024] The artificial plant cultivation site 20 further includes, for example, an environmental information monitor 21, a growth status monitor 22, and a lighting device 31. The environmental information monitor 21 and the growth status monitor 22 are each connected to the information analysis device 10. The lighting device 31 is connected to the environmental control device 30.

[0025] The environmental information monitor 21 is a device that monitors the environmental conditions within the artificial plant cultivation site 20. The environmental information monitor 21 includes, for example, a light amount sensor, an air temperature sensor, a water temperature sensor, a water supply amount sensor, a humidity sensor, a carbon dioxide (CO2) concentration sensor, and a nutrient sensor. The environmental information monitor 21 transmits environmental information (environmental parameters or variables) of the artificial plant cultivation site 20 detected by the various sensors to the information analyzing device 10 as environmental information data 210. That is, the environmental information data 210 includes any one of information on the amount of light, air temperature, water temperature, amount of water supply, carbon dioxide concentration, nutrients, and time.

[0026] The growth status monitor 22 is a device that monitors the growth process of each of the multiple varieties of mutants V1, V2, and V3 within the artificial plant cultivation site 20. The growth status monitor 22 includes, for example, a camera and an analysis device. The growth status monitor 22 has a function of analyzing images acquired by the camera using the analysis device. The environmental information monitor 21 and the growth status monitor 22 may share sensors, etc. The growth status monitor 22 may analyze information indicating the growth level of each mutant, such as the total photosynthetic rate, dark respiration rate, chlorophyll fluorescence, stomatal conductance, leaf area / leaf number distribution, leaf inclination angle distribution, root structure and distribution, chemical component distribution, net photosynthetic rate, transpiration rate, respiration rate, carbon dioxide application rate, water supply rate, water absorption rate, and power consumption. The growth status monitor 22 then transmits information indicating the growth status of each of the multiple varieties of mutants to the information analysis device 10 as growth information data 220. That is, the growth information data 220 includes any of the following information: total photosynthetic rate, dark respiration rate, chlorophyll fluorescence, stomatal conductance, leaf area / leaf number distribution, leaf inclination angle distribution, root structure and distribution, chemical component distribution, net photosynthetic rate, transpiration rate, respiration rate, carbon dioxide application rate, water supply rate, water absorption rate, and electricity.

[0027] The lighting device 31 is a light installed in the artificial plant cultivation site 20. The lighting device 31 irradiates light onto each of the multiple types of mutants V1, V2, and V3. The amount of light irradiated by the lighting device 31 can be controlled by the environmental control device 30. The artificial plant cultivation site 20 may be equipped with other control devices. An example of the other control devices will be described in the third embodiment.

[0028] <1-1-4> Functional configuration of the information analysis device 10 4 is a block diagram showing an example of the functional configuration of the information analysis device 10 according to the first embodiment. As shown in FIG. 4, the information analysis device 10 includes, for example, a time-series data generation unit 100, time-series data 110, genetic information 111, an analytical model generation unit 120, an analytical model 130, and a simulation execution unit 140.

[0029] The time-series data generating unit 100 synchronizes and records the recording times of the environmental information data 210 input from the environmental information monitor 21 and the growth information data 220 input from the growth status monitor 22, thereby generating time-series data 110. In other words, the time-series data generating unit 100 synchronizes the environmental information (environmental parameters and variables) with the growth process of the cultivated and grown plants, and accumulates them in the time-series data 110. The time-series data 110 is used as training data for creating an analysis model 130 for a growth simulation.

[0030] The analytical model generation unit 120 generates or updates the analytical model 130 by learning using the time-series data 110 and genetic information 111. The genetic information 111 includes genetic information of a mutant species grown in the artificial plant cultivation site 20 before and after genome editing. The analytical model 130 is a model for evaluating the growth process of a mutant from genetic information and environmental information. The simulation execution unit 140 executes a growth simulation of a plant (mutant) based on the analytical model 130. In the growth simulation, when the genetic information of a mutant and growth environmental information are input, the simulation execution unit 140 simulates the growth process of the mutant based on the analytical model 130.

[0031] <1-1-5> Data format for time series data 110 FIG. 5 is a table showing an example of a data format of the time-series data 110 in the first embodiment. As shown in FIG. 5, the time-series data 110 records, for example, time-series data of the recording time, CO2 input amount, light intensity (light amount), temperature, nutritional level, growth level, and residual CO2 amount. In this example, the artificial plant cultivation site 20 inputs carbon dioxide and measures the residual amount of carbon dioxide (residual CO2 amount) within the artificial plant cultivation site 20. The information analyzing device 10 then records this environmental information (environmental information data 210) and growth process (growth information data 220) as the time-series data 110. The artificial plant cultivation site 20 may also measure the amount of carbon dioxide absorbed by mutants and record the measured amount in the time-series data 110. The time-series data 110 may also be recorded together with genetic information 111.

[0032] <1-2>Analysis processing Next, an analysis process using the information analysis system 1 according to the first embodiment will be described. The information analysis device 10 can execute the analysis process for each cultivation cycle. In this specification, a "cultivation cycle" corresponds to a cycle in which seeds or seedlings of a plant (mutant) are grown in an artificial plant cultivation site 20 and the growth of the plant is completed (e.g., harvested or discarded). Specifically, "one cultivation cycle" corresponds to a series of processes including the growth of a genome-edited plant (i.e., a mutant) and the creation of an analysis model 130 based on the growth results.

[0033] Fig. 6 is a flowchart showing an example of an analysis process using the information analysis system 1 according to the first embodiment. Fig. 6 shows a process executed for each cultivation cycle. The analysis process of the information analysis system 1 according to the first embodiment will be described below with reference to Fig. 6.

[0034] First, a growth environment is set in the artificial plant cultivation site 20 (S10). In setting the growth environment, for example, the intensity of the light from the lighting device 31 is set as a first condition, the temperature in the artificial plant cultivation site 20 is set as a second condition, and the amount of fertilizer to be given to each mutant in the artificial plant cultivation site 20 is set as a third condition.

[0035] Next, multiple types of mutants are prepared in which only one gene has been genome-edited (S11). The type of gene to be genome-edited can be selected arbitrarily. The type of gene to be genome-edited may be selected based on the results of an analysis process of a past cultivation cycle. The multiple types of mutants are placed in an distinguishable manner within the artificial plant cultivation site 20. For example, seeds or seedlings of the multiple types of mutants are placed in multiple areas within the artificial plant cultivation site 20.

[0036] Next, while simultaneously growing multiple types of variants, time-series data 110 relating to the growth environment and the growth of each variant is acquired (S12). Specifically, the environmental control device 30 controls each control device in the artificial plant cultivation site 20 to realize the growth environment based on the conditions of the growth environment set in S10. More specifically, the environmental control device 30 grows each variant by controlling and supplying water, carbon dioxide, light, nutrients, and other growth environmental conditions to the target variant in a closed plant cultivation and growth space. At this time, the environmental information monitor 21 and the growth status monitor 22 monitor the growth rate, respiration rate, and other characteristics of each variant during the growth process. The generated environmental information data 210 and growth information data 220 are then synchronized and accumulated as time-series data 110 in the information analysis device 10. In other words, in the growth process of multiple types of mutants, the environmental information monitor 21 and the growth status monitor 22 transmit environmental information data 210 and growth information data 220, respectively, to the information analysis device 10, and the time series data generation unit 100 of the information analysis device 10 generates time series data 110 using the received environmental information data 210 and growth information data 220.

[0037] Next, an analytical model 130 that represents the correlation between genetic information, growth environment, and growth status is created from the time-series data 110 and genetic information 111 (S13). Specifically, the analytical model generation unit 120 performs machine learning on the growth and growth correlation of each mutant from the time-series data 110 and genetic information 111, and creates the analytical model 130.

[0038] Next, a growth simulation is performed using the analysis model 130 (S14). In the growth simulation, the simulation execution unit 140 evaluates differences in growth rate and the like for different mutants of the genome-edited strain.

[0039] Next, the results of the growth simulation are fed back to genome editing and the setting of the growth environment (S15). When the process of S15 is completed, the information analysis device 10 ends the analysis process corresponding to one cycle of the cultivation period.

[0040] The information analysis device 10 changes the growth environment settings for each cultivation cycle and can update the analysis model 130 based on new time-series data 110 and genetic information 111 obtained by collecting environmental information data 210 and growth information data 220. In the next cultivation cycle, after receiving feedback from the results of the analysis process of one cycle described above, it is possible to cultivate a different series of edited strains with different functions in the same growth environment, or to cultivate the same genome-edited strain series in a different growth environment. The analysis model 130 updated by repeating the analysis process including feedback can be used to search for the optimal combination of genome-edited genes (genome-edited strains) and growth environment that maximizes growth.

[0041] Note that the amount of plant genetic information, the set growth environment conditions, and the measurement results of the plant's growth process obtained in one cultivation cycle are finite. Therefore, if machine learning is performed using only actual measurement results, errors may occur due to insufficient data volume or data variability used by the analysis model generation unit 120 for learning. Therefore, the information analysis device 10 may use logistic function approximation, autoregression, multiple regression, etc. to formulate causal relationships from cultivation data and correct training data using causal relationship equations. This allows the information analysis device 10 to improve the accuracy of growth simulations.

[0042] 7 is a conceptual diagram of an optimization problem related to early prediction and confirmation of genome editing accuracy using the analytical model 130 generated by the analytical processing according to the first embodiment. In the graph shown in FIG. 7, the X-axis corresponds to the growth environment, the Y-axis corresponds to the genome, and the Z-axis corresponds to the growth level. Note that in FIG. 7, the relationship between the growth environment, the genome, and the growth level is expressed three-dimensionally, but in reality, the growth environment axis, the genome axis, and the growth level axis are each multidimensional.

[0043] This example illustrates a problem of experimentally finding an optimal solution using growth rate as an evaluation function and the genome axis and the growth environment axis as variables. The results of such growth simulations are fed back into genome editing and growth environment settings, allowing for automatic improvement of genome editing parameters and growth environment parameters. In order to accelerate the convergence of prediction results in the process of discovering a globally optimal solution (global solution) that maximizes growth rate, rather than a locally high local solution, on a plane composed of growth environment variations and genome editing variations, it is preferable to use machine learning or numerical simulation.

[0044] FIG. 8 is a conceptual diagram illustrating an example of a growth simulation performed by the information analysis device 10 according to the first embodiment. FIG. 8 shows the relationship between the age of a plant and the amount of CO2 absorbed (the tree-age dependence of carbon dioxide absorption) before and after plant improvement. As shown in FIG. 8, in a certain growth environment, the photosynthetic efficiency of the improved plant is improved compared to the plant before improvement. It is estimated that the improved plant will exhibit trait changes that result in longer long-term health and a longer lifespan than the plant before improvement. The growth simulation can also predict genome editing targets required for such trait changes and gene sequences after genome editing. This allows users to reproduce and verify optimal genetic information and growth environment conditions based on the prediction results of the growth simulation in real space.

[0045] Here, we will explain a method for selecting superior varieties by retrospectively evaluating the growth process of plants (i.e., evaluating in the time domain). First, for selected seeds or seedlings, the growth process (growth information data 220) and the cultivation process including environmental settings (environmental information data 210) are recorded. Then, the information analysis device 10 learns the correlation between the environment and growth. This enables the selection of superior seeds or seedlings suitable for obtaining desired traits. Furthermore, by recording the growth process of the selected seeds or seedlings as time-series data 110, the information analysis device 10 can analyze environmental and cultivation conditions that result in favorable or undesired trait changes. For example, by comparing the growth processes of different seeds or seedlings under the same environmental and cultivation conditions, the information analysis device 10 can accurately compare the point at which advantageous differences appear.

[0046] <1-3> Effects of the first embodiment The information analysis system 1 according to the first embodiment includes a closed space (artificial plant cultivation site 20) that can artificially realize a variety of growth environments without being affected by environmental changes outside the space. The information analysis device 10 synchronizes environmental data obtained when environmental conditions in the closed space are changed with plant growth data and collects them as time-series data 110, thereby creating an analysis model 130 that can simulate the growth process in any growth environment based on the correlation between the environment and growth.

[0047] This allows the simulation execution unit 140 to perform a virtual growth simulation using the analysis model 130, selecting an arbitrary growth period, and estimate the plant's growth process in response to environmental conditions. Additionally, by comparing the growth process before and after genome editing, the effectiveness of genome editing and other techniques can be evaluated. Furthermore, the analysis model generation unit 120 creates the analysis model 130 through machine learning of the correlation between the environmental conditions for multiple mutants that differ in only one gene and the growth and development of each mutant. This allows the simulation execution unit 140 to estimate the plant's growth process in response to environmental conditions based on the genetic characteristics of each mutant. Furthermore, by feeding back the simulation results to the cultivation methods and environmental settings in the actual cultivation space, the optimal environmental conditions for the growth of a plant with a certain genetic characteristic can be efficiently and accurately discovered.

[0048] In addition, in the information analysis system 1 according to the first embodiment, multiple types of mutants (with different edited genes) in which only one gene has been genome-edited are prepared for the target plant to be grown and cultivated, and the multiple types of mutants are grown in a closed space under the same environmental conditions, and a set of growth data and environmental data for each mutant is obtained.

[0049] This allows the information analysis device 10 to create an analytical model 130 that represents the correlation between the environment and growth, as well as the correlation with genetic information. Furthermore, the information analysis device 10 can use this analytical model 130 to perform a growth simulation for any growth period and feed back the simulation results to the cultivation method and environmental settings in the actual cultivation space. Furthermore, the information analysis device 10 can predict plant mutations in a certain growth environment when genetic modifications equivalent to the mutations that occur when a plant adapts to a certain environmental change are performed. As a result, the information analysis device 10 can efficiently and accurately search for genetic characteristics optimal for a certain growth environment and select genome editing targets required to obtain those genetic characteristics.

[0050] As described above, the information analysis system 1 according to the first embodiment (1) can create a desired growth environment in a closed space and control environmental parameters, (2) establish a method for evaluating whether changes in traits are due to the effects of genome editing or whether the plant has adapted to the environment, (3) numerically model plant growth information and environmental information (e.g., quality, growth rate, CO2 absorption), (4) establish a method for acquiring time-domain plant growth data (time-series data 110 synchronized with environmental parameters), (5) select superior varieties through virtual growth simulations, search for ideal growth environments, and select optimal varieties in a given growth environment, and (6) perform growth simulations that incorporate genome editing techniques and automatically improve the growth environment by feeding back the results to control the growth environment to achieve the desired growth process. Therefore, the information analysis system 1 according to the first embodiment can improve the productivity of superior plant varieties and create vegetation with desired environmental adaptability.

[0051] <2> Second embodiment The second embodiment relates to an information analysis system 1 that executes analysis processing similar to that of the first embodiment using an artificial plant cultivation site 20 having a plurality of growth environment cells. Below, the differences between the information analysis system 1 according to the second embodiment and the first embodiment will be described.

[0052] <2-1> Configuration of artificial plant cultivation site 20A FIG. 9 is a conceptual diagram showing an example of the configuration of an artificial plant cultivation site 20A according to the second embodiment. As shown in FIG. 9, the artificial plant cultivation site 20A cultivates the same mutants (i.e., plants with the same genome-edited gene and strain) in multiple cultivation environments. Specifically, the artificial plant cultivation site 20A includes multiple cultivation environment cells EC1, EC2, and EC3. Each of the multiple cultivation environment cells EC1, EC2, and EC3 is an independently provided closed space. In the artificial plant cultivation site 20A, an environmental control device 30, an environmental information monitor 21, a growth status monitor 22, and a lighting device 31 are provided for each of the multiple cultivation environment cells EC.

[0053] Specifically, the growth environment cell EC1 has a growth environment controlled by an environmental control device 30-1 and includes an environmental information monitor 21-1, a growth status monitor 22-1, and a lighting device 31-1. The growth environment cell EC2 has a growth environment controlled by an environmental control device 30-2 and includes an environmental information monitor 21-2, a growth status monitor 22-2, and a lighting device 31-2. The growth environment cell EC3 has a growth environment controlled by an environmental control device 30-3 and includes an environmental information monitor 21-3, a growth status monitor 22-3, and a lighting device 31-3. Each of the growth environment cells EC1, EC2, and EC3 grows, for example, a mutant V4.

[0054] Each environmental control device 30 can operate independently based on instructions from the information analyzing device 10. Environmental information monitors 21-1, 21-2, and 21-3 monitor environmental conditions in the growth environment cells EC1, EC2, and EC3, respectively. Growth status monitors 22-1, 22-2, and 22-3 monitor the growth process of mutant V4 in the growth environment cells EC1, EC2, and EC3, respectively. Each environmental information monitor 21 transmits the monitoring results to the information analyzing device 10 as environmental information data 210. Each growth status monitor 22 transmits the monitoring results to the information analyzing device 10 as growth information data 220. Lighting devices 31-1, 31-2, and 31-3 irradiate light onto mutant V4 in the growth environment cells EC1, EC2, and EC3, respectively.

[0055] The number of sets of growth environment cells EC, environmental control devices 30, environmental information monitors 21, growth status monitors 22, and lighting devices 31 provided in the artificial plant cultivation site 20A may be any number and is not limited to three. The information analysis system 1 according to the second embodiment may use the artificial plant cultivation site 20 of the first embodiment. In this case, each of the multiple artificial plant cultivation sites 20 grows the same mutant. Then, the information analysis device 10 sets a different growth environment for each artificial plant cultivation site 20. The other configurations of the information analysis system 1 according to the second embodiment are the same as those of the first embodiment.

[0056] <2-2>Analysis processing 10 is a flowchart showing an example of analysis processing using the information analysis system 1 according to the second embodiment. The analysis processing of the information analysis system 1 according to the second embodiment will be described below with reference to FIG.

[0057] First, different growth environments are set in each growth environment cell EC of the artificial plant cultivation site 20 (S20). In setting the growth environments, for example, the intensity of the light amount of the lighting device 31 is set as a first condition, the temperature in the artificial plant cultivation site 20 is set as a second condition, and the amount of fertilizer to be given to each mutant in the artificial plant cultivation site 20 is set as a third condition. Then, settings with different combinations of the first to third conditions are applied to each growth environment cell EC.

[0058] Next, one type of mutant V4 is prepared by genome editing only one gene (S21). The type of gene to be genome-edited can be selected arbitrarily. The type of gene to be genome-edited may be selected based on the results of an analysis process of a past cultivation cycle. The mutant V4 is placed in each of the multiple growth environment cells EC within the artificial plant cultivation site 20.

[0059] Next, while the mutant V4 is growing in each growth environment cell EC, time-series data 110 relating to the growth environment of each growth environment cell EC and the growth of the mutant V4 is acquired (S22). Specifically, the environmental control device 30 controls each control device in the artificial plant cultivation site 20A to realize the growth environment based on the conditions of the growth environment set in S20. At this time, for each growth environment cell EC, the environmental information monitor 21 and the growth status monitor 22 monitor the growth rate, respiration rate, etc. of each mutant during the growth process. The generated environmental information data 210 and growth information data 220 are then synchronized and stored as time-series data 110 in the information analysis device 10.

[0060] Next, an analytical model 130 that represents the correlation between genetic information, growth environment, and growth status is created from the time-series data 110 and genetic information 111 (S23). Specifically, the analytical model generation unit 120 performs machine learning on the growth and growth correlation of the mutant V4 from the time-series data 110 and genetic information 111, and creates the analytical model 130.

[0061] Next, a growth simulation is performed using the analytical model 130 (S24). In the growth simulation, the simulation execution unit 140 evaluates differences in growth rate and the like of mutant V4 grown in different growth environments.

[0062] Next, the results of the growth simulation are fed back to genome editing and the setting of the growth environment (S25). When the process of S25 is completed, the information analysis device 10 ends the analysis process corresponding to one cycle of the cultivation period.

[0063] The information analysis device 10 changes the settings of the growth environment and genome editing target for each cultivation cycle and can update the analysis model 130 based on new time-series data 110 and genetic information 111 obtained by collecting environmental information data 210 and growth information data 220. In the next cultivation cycle, after receiving feedback from the results of the analysis process of one cycle described above, it is possible to cultivate a different series of edited strains with different functions in the same growth environment, or to cultivate the same genome-edited strain series in a different growth environment. The analysis model 130 updated by repeating the analysis process including feedback can be used to search for the optimal combination of genome-edited genes (genome-edited strains) and growth environment that maximizes growth. In other words, the method of using the growth simulation in the information analysis system 1 according to the second embodiment is the same as in the first embodiment.

[0064] <2-3> Effects of the second embodiment In the information analysis system 1 according to the second embodiment, one type of mutant in which only one gene has been genome-edited is prepared for a target plant to be grown and cultivated, the mutant is grown in a closed space under different environmental conditions, and growth data for the mutant and environmental data for each environmental condition are acquired as a set. This allows the information analysis device 10 according to the second embodiment to collect information on the correlation between the environment and growth of a given mutant more efficiently than in the first embodiment, and to create an analysis model 130 that represents the correlation with genetic information.

[0065] Furthermore, the information analysis system 1 according to the second embodiment feeds back the results of the growth simulation and processes the next cultivation cycle, thereby enabling efficient and highly accurate searches for genetic characteristics optimal for a certain growth environment and selection of genome editing targets required to obtain those genetic characteristics, as in the first embodiment. As a result, the information analysis system 1 according to the second embodiment, like the first embodiment, can improve the productivity of superior plant varieties and create vegetation with desired environmental adaptability.

[0066] <3> Third embodiment The third embodiment relates to an example of parameters in a production process in which the information analysis system 1 is used. The following describes the information analysis system 1 according to the third embodiment, with respect to differences from the first embodiment.

[0067] <3-1> Configuration of artificial plant cultivation site 20B Fig. 11 is a block diagram showing an example of the configuration of an artificial plant cultivation site 20B according to the third embodiment. As shown in Fig. 11, the artificial plant cultivation site 20B includes, for example, a lighting device 31, an air conditioning device 32, a hydroponics device 33, and a work machine 34. The lighting device 31, the air conditioning device 32, the hydroponics device 33, and the work machine 34 are each controlled by an environmental control device 30. The air conditioning device 32 is a control device that controls air conditioning within the artificial plant cultivation site 20. The hydroponics device 33 is a control device that adjusts the amount of nutrient solution supplied to plants (mutants) within the artificial plant cultivation site 20. The work machine 34 is a device that manages work related to cultivation within the artificial plant cultivation site 20.

[0068] <3-2> Quantification of parameters in the production process Next, referring to FIG. 11 , an example of quantifying parameters in a production process will be described. Resources input to the artificial plant cultivation site 20B include carbon dioxide, electricity, water, fertilizer, seeds, labor, cultivation area, and time. Electricity is used as a power source for the lighting devices 31, air conditioning devices 32, hydroponic cultivation devices 33, and work machines 34 within the artificial plant cultivation site 20B. The artificial plant cultivation site 20B is provided with a highly insulated, airtight, efficient, and clean environment. Products of the artificial plant cultivation site 20B include production value, oxygen, plant residues, waste heat, wastewater, and used consumables. Here, production value is expressed in terms of the vegetables obtained as products, and is expressed, for example, by multiplying the unit price by the production volume. Plant residues, waste heat, wastewater, and used consumables correspond to waste generated during production. Other configurations and operations of the information analysis system 1 according to the third embodiment are the same as those of the first embodiment.

[0069] <3-3> Effects of the third embodiment To evaluate the production efficiency of a plant factory, it is necessary to quantify the input resources and the output. For example, to obtain the maximum production volume and production value with the minimum input resources and amount, it is preferable to minimize waste. It is also preferable to reuse waste to the extent possible. Therefore, in the information analysis system 1 according to the third embodiment, the input resources and the output are each quantified (quantified) as shown in FIG. 11.

[0070] The information analysis system 1 according to the third embodiment searches for (predicts) growth conditions that can minimize or reuse waste materials, such as quantified plant residues, used consumables, waste heat, and wastewater, quantified in the closed space, in a growth simulation. This allows the information analysis system 1 according to the third embodiment to minimize waste materials, such as waste heat, wastewater, and used consumables, and maximize plant productivity. The third embodiment may be combined with the second embodiment.

[0071] <4> others The flowcharts used to explain the analysis process in the above embodiment are merely examples. The flowcharts shown in Figures 6 and 10 may have their processing order changed or other processes added, as long as the same results as those in the embodiment are obtained. For example, the order of S10 and S11 may be reversed. In this specification, "synchronizing the recording time" may be referred to as "time synchronization." The information analysis device 10 may be referred to as a "server" or a "processing server." The CPU 11 may be referred to as a "processor." The ROM 12, RAM 13, and storage device 14 may each be referred to as a "storage circuit." The configurations of the information analysis device 10 and the artificial plant cultivation site 20 are merely examples. The CPU 11 may be an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or the like. The analysis process may be implemented by dedicated hardware. The analysis process may be a mixture of software-based and hardware-based processes, or only one of them. The "connection" may be a wired connection, a wireless connection, or a connection via a network, as long as communication is possible.

[0072] The present invention is not limited to the above-described embodiments, and various modifications can be made in the implementation stage without departing from the spirit of the invention. Furthermore, the embodiments may be implemented in appropriate combinations, in which case the combined effects can be obtained. Furthermore, the above-described embodiments include various inventions, and various inventions can be extracted by combining selected elements from the disclosed elements. For example, if the problem can be solved and the desired effect can be obtained even if some elements are deleted from all elements shown in the embodiments, the configuration from which these elements are deleted can be extracted as an invention. [Explanation of symbols]

[0073] 1. Information analysis system 10...Information analysis device 11...CPU 12...ROM 13...RAM 14...Storage device 15...Communication interface 20, 20A, 20B... Artificial plant cultivation site 21...Environmental information monitor 22...Growth status monitor 30...Environmental control device 31...Lighting equipment 32...Air conditioner 33... Hydroponic cultivation equipment 34...Work machinery 100...Time series data generation unit 110...Time series data 111...Genetic information 120...Analysis model generation unit 130...Analysis model 140...Simulation execution unit 210...Environmental Information Data 220...Growth information data EC: Growth environment cell V1, V2, V3, V4...mutants

Claims

1. In a closed space where the environment can be controlled, a growth environment for the plant to be genome edited is set up; Preparing multiple types of mutants in which one gene of the plant has been genome-edited, and simultaneously growing the multiple types of mutants in the closed space; creating time-series data in which first data indicating the growth environment of the plurality of mutant species and second data indicating the growth state of the plurality of mutant species are recorded in time synchronization; and creating an analytical model that represents a correlation between genetic information, growth environment, and growth state using genetic information and the time-series data regarding the plurality of mutants. Information analysis method.

2. In a closed space having a plurality of growth environment cells each having an independently controlled environment, a mutant of a plant that is the subject of genome editing and in which one gene has been genome-edited is grown in each of the plurality of growth environment cells in which different growth environments are set; creating time-series data in which first data indicating the growth environment of each of the plurality of growth environment cells and second data indicating the growth state of each of the plurality of growth environment cells are synchronously recorded; and creating an analytical model that represents a correlation between genetic information, growth environment, and growth state using genetic information about the mutant and the time-series data. Information analysis method.

3. the first data includes any one of light intensity, air temperature, water temperature, water supply amount, carbon dioxide concentration, nutrients, and time; 3. The information analysis method according to claim 1 or 2.

4. The second data includes any one of a total photosynthetic rate, a dark respiration rate, a chlorophyll fluorescence, a stomatal conductance, a leaf area / leaf number distribution, a leaf inclination angle distribution, a root structure and distribution, a chemical component distribution, a net photosynthetic rate, a transpiration rate, a respiration rate, a carbon dioxide application rate, a water supply rate, a water absorption rate, and an electric power. The information analysis method according to any one of claims 1 to 3.

5. and simulating the growth of the mutant using the analytical model to predict growing conditions that minimize the quantified plant residues, used consumables, waste heat, and waste water in the enclosed space. The information analysis method according to any one of claims 1 to 4.

6. and performing machine learning using the time-series data as training data to predict the adaptation of the mutant when the growth environment changes. The information analysis method according to any one of claims 1 to 5.

7. An information analysis device that analyzes data obtained by growing a mutant of a plant whose gene has been genome-edited in a closed space, a processor for executing an analysis process; a memory circuit that stores genetic information about the variant; In the analysis process, the processor when receiving first data indicating the growth environment of the mutant and second data indicating the growth state of the mutant, the first data and the second data are recorded in time synchronization to create time series data, and the time series data is stored in the memory circuit; creating an analytical model that represents the correlation between the genetic information, the growth environment, and the growth state using the genetic information and the time-series data; Information analysis device.

8. In the analysis process, the processor performs machine learning using the time-series data as training data to predict the adaptation of the mutant when the growth environment changes. The information analysis device according to claim 7 .

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