Computer system and method

Abstract

To provide a computing system that is desirable for effectively executing a design support to increase the nature of biological resources including highly productive microorganisms and accordingly developing biological resources useful for humans without wasting research resources or research money.SOLUTION: In the computing system for supporting a design to increase functions of biological resources, a server computer registers the history of the design. The history of the design includes pair information of relational elements related to the nature of the biological resources and operations on the relational elements. The system searches a database on the basis of the pair information, acquires information related to the nature of the biological resources on the basis of the result of the search, which is additional information other than the relational elements, operates the correlation of the additional information to the relational elements, and evaluates the additional information on the basis of the operated correlation.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to a computer system, and more particularly to a computer system that supports the design of artificial improvements and modifications of biological functions, such as the creation of highly productive microorganisms. [Background technology]

[0002] In recent years, with the remarkable progress in biotechnology, such as gene modification and genome editing, synthetic biotechnology has attracted attention, which involves creating microorganisms with artificially enhanced substance production capabilities through genetic modification etc. The development of such highly productive microorganisms requires genetic modification techniques such as introducing new genes that the microorganism does not originally have, or enhancing, deleting or suppressing the expression of native genes in the cells, in order to express desired biological functions within the cells.

[0003] One of the conventional approaches has been to perform metabolic simulations by applying flux balance analysis based on the metabolic map of a certain organism, and to attempt to improve microorganisms by predicting the metabolic fluxes flowing through the metabolic map (e.g., JP 2005-58226 A).

[0004] On the other hand, a search device has been proposed to search for bioitems (genes) related to the improvement of biological functions, which stores a bioitem literature set for each bioitem, searches for keywords in each bioitem literature set, obtains the number of documents Nh containing the keyword in the bioitem literature set for each bioitem, selects bioitems with a literature count Nh of 1 or more as candidate bioitems, creates a literature count table for each candidate bioitem consisting of a) the number of documents Nh and / or b) the number of documents that do not contain the keyword but include the bioitem name, calculates a correlation score between the bioitem and the keyword for each candidate bioitem based on statistical calculations using the literature count table, and outputs the candidate bioitems based on the calculated correlation score (International Publication WO2007 / 126088). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2005-58226 [Patent Document 2] International Publication No. WO2007 / 126088 Summary of the Invention [Problem to be solved by the invention]

[0006] In metabolic simulations based on flux balance analysis, not only are genes not included in the metabolic model not taken into account, but the solutions are obtained using multidimensional equations, which can result in indefinite solutions or even inability to analyze, making them insufficient as support systems for developing highly productive microorganisms.

[0007] On the other hand, in a search device that calculates a correlation score between a bioitem and a keyword based on a keyword search and the number of documents hit by the search, and outputs candidate bioitems, there is a problem that when focusing on the biological functions of highly productive microorganisms in order to develop highly productive microorganisms, a large number of related genes are hit, making it impossible to narrow down the effective genes; on the other hand, when focusing on a specific gene, it is not possible to search for its effectiveness with respect to the desired biological function.

[0008] Therefore, a computer system that is useful as a support system for the development of highly productive microorganisms has yet to be realized. In reality, genetic modification of highly productive microorganisms has still to be achieved through a process of trial and error backed by the knowledge and experience of individual researchers, and the reality is that waste of research resources such as researchers and equipment, as well as research funds, cannot be avoided.

[0009] Therefore, the present invention aims to provide a computer system that efficiently executes design support for improving the properties of biological resources such as highly productive microorganisms, thereby being suitable for developing biological resources useful to humanity while avoiding waste of research resources, research funds, etc. [Means for solving the problem]

[0010] In order to achieve the above-mentioned object, the present invention provides a computer system that supports the design of improving the functionality of biological resources, the computer system comprising a controller that executes a program recorded in memory, the controller registers the design history, the design history including pair information of relational elements related to the properties of the biological resource and operations on the relational elements, searches a database based on the pair information, and based on the results of this search, obtains additional information related to the properties of the biological resource other than the relational elements, calculates the correlation of the additional information with the relational elements, and evaluates the additional information based on the calculated correlation. [Effects of the Invention]

[0011] According to the present invention, it is possible to provide a computer system that is suitable for efficiently carrying out design support for improving the properties of biological resources such as highly productive microorganisms, thereby developing biological resources that are useful to humanity while avoiding waste of research resources, research funds, etc. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram of an embodiment of a computer system according to the present invention. [Figure 2] FIG. 2 shows an example of a data structure in the design history database of the computer system of FIG. [Figure 3] FIG. 3 is an example of a management table for managing a group of data in the design history database. [Figure 4]FIG. 4 shows an example of a user interface in a user computer of a computer system. [Figure 5] FIG. 5 is a flowchart illustrating the operation of the server computer of the computer system. [Figure 6] FIG. 6 is a block diagram illustrating the principle of candidate gene search. [Figure 7] FIG. 7 is an example of a candidate gene management table. [Figure 8] FIG. 8 is an example of a ranking table of candidate genes. [Figure 9] FIG. 9 is a block diagram illustrating an example of the relationship between a metabolic map, a target gene, and a candidate gene. [Figure 10] FIG. 10 is a block diagram of a computer system according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] Next, an embodiment of the present invention will be described. This embodiment is a computer system that supports the improvement of the function of highly productive microorganisms (artificial microorganisms) as biological resources. Specifically, the computer system analyzes the history (past information) of genetic modifications, including the introduction of new genes into highly productive microorganisms, the enhancement of the function of native genes, the disruption of native genes, and the suppression of native genes, and suggests to the user useful genes as candidate genes that could be candidates for improving the function of the artificial microorganisms, such as causing the highly productive microorganisms to produce useful substances or improving the production efficiency of useful substances.

[0014] The user can refer to the candidate genes suggested by the system and apply wet manipulation to the artificial microorganism to verify whether it has been effective in improving the function, activity, performance, properties, characteristics, or attributes of the artificial microorganism, which greatly reduces the burden on the user in searching for candidate genes.

[0015] Biological resources include animal and plant cells, microorganisms, bacteria, viruses, hormones, enzymes, tissues, organs, genes, chromosomes, DNA, RNA, and artificial microorganisms (high-productivity microorganisms), all of which possess some biological activity. Biological resources can also be referred to as biological materials or biological materials. Genes are fundamental elements involved in the functional modification of organisms, cells, and microorganisms. The metabolism of artificial microorganisms is modeled as a metabolic map, and genetic modification can lead to the creation, blocking, suppression, or activation of metabolic pathways within the metabolic map. Genetic modification can significantly alter metabolic function, resulting in, for example, diversifying the useful substances that can be produced by microbial fermentation and maximizing their yield. "Genetic modification" includes the introduction of novel genes, the suppression of native genes, the enhancement of native genes, and the removal of native genes.

[0016] Figure 1 is an example block diagram of a computer system. The computer system comprises a user computer 10, a server computer 12, and a communications network (Internet, LAN, etc.) connecting the two. The user computer 10 and the server computer 12 each comprise typical hardware resources such as a controller (CPU: control means), memory, and an input / output interface. Multiple databases are connected to the communications network, including a design history database 14 and a literature (related information) database and search engine 16. The computer system may also be configured using a personal computer.

[0017] The design history database 14 may exist for each item such as an organization, group, country, institution, region, or period. There may be one or more design history databases 14. The same may be true for the literature database and search engine 16.

[0018] The design history is the metabolic design history of a biological resource, and includes multiple pairs of genes (related elements) and their operations in the biological resource. Operations include operations to modify genes in order to improve the characteristics, properties, performance, function, or attributes of the biological resource. Specific examples include the introduction of new genes into highly productive microorganisms, and the enhancement, suppression, or deletion of existing genes.

[0019] Figure 2 shows an example of a data structure in the design history database 14. This data structure is explained as a tree structure in which multiple modification lists are connected by links. One block in Figure 2 corresponds to a modification list. As shown in Figure 2, the construction of an artificial microorganism is achieved by accumulating genetic modifications starting from a specified parent strain. One block (modification list) contains pair information for each modification generation, including the name of the gene that was the target of the modification and the operation performed on the gene. Multiple blocks are connected by links according to the modification generation.

[0020] Reference numeral 200 indicates the list of modifications for modified strain 1 (strain 001) in which the pdc gene has been added to the wild type (strain 000 parent strain), reference numeral 202 indicates the list of modifications for modified strain 2 (strain 002) in which the adhE gene has been deleted (Δ) from modified strain 1 (strain 001), and reference numeral 204 indicates the list of modifications for modified strain 3 (strain 003) in which the pps gene has been added to modified strain 2 (strain 002). Therefore, reference numeral 204 indicates a modified strain in which adhE has been deleted from the parent strain and pdc and pps have been added.

[0021] 3 shows an example of a management table for managing data groups in the design history database 14. The management table includes a strain information table for managing microbial strains, a gene modification information table for managing genetic modification information for the strains, a basis information table for managing the basis for modification, and an experiment information table. The strain information table includes a strain ID, a parent strain ID, and detailed strain information. The gene modification information table includes a strain ID, a modification ID, a target gene name, an operation name for the target gene, and operation information, and is in 1:N correspondence with the strain information table, with the strain ID as a key.

[0022] The evidence information table includes an alteration ID, an evidence information ID, and evidence information, and corresponds 1:N to the evidence information table using the alteration ID as a key. The experiment information table includes a strain ID and experiment detail information, and corresponds 1:N to the strain information table using the strain ID as a key.

[0023] The literature database and search engine 16 is a collection of data related to biological resources, and the attributes of the data are not limited to text data, image data, audio data, etc., and the types of data are mainly literature data such as papers, patents, books, magazines, etc., but are not limited to these.

[0024] The user computer 10 has an interface for inputting (S1) a list of modifications as design history into the design history database 14. Figure 4 shows an example of a graphic user interface, which includes a screen 400 showing a tree structure of the design history and a screen 402 for inputting data into the tree structure.

[0025] The example of the input screen is an input screen for box 404 in the tree structure, and shows that ADH2 is selected as the target gene, disruption (deletion) is selected as the operation for the target gene, and the target effect (20 mM increase) of the target compound (ethanol) for the target microorganism (Saccharomyces cerevisiae: yeast) is entered. When the user clicks Submit, the input information is registered in box 404 in the tree structure.

[0026] The user computer 10 requests (S2) the server computer 12 to propose candidate genes for genetic modification. Based on this request, the server computer 12 references (S3) the design history database, retrieves (S3A) the modification list, and issues (S4) a search query to the literature database and search engine 16.

[0027] The literature database and search engine 16 searches the database based on the search query and outputs the search results to the server computer 12 (S5). The server computer 12 evaluates the search results and, based on the evaluation results, outputs suggestions for candidate genes to be targeted for genetic modification to the user computer 10 (S6).

[0028] Next, the operation of the server computer 12 will be explained again with reference to the flowchart (FIG. 5). The controller of the server computer 12 executes this flowchart in accordance with the program stored in memory. When the controller receives a proposal request (S2) from the user computer 10, it starts the flowchart.

[0029] The controller reads the modification list 14A from the design history database 14 (S500). The controller may read all modification lists belonging to the database, or may read a predetermined range of modification lists by dividing the modification list by creation date and time, specifying the target gene, specifying the type of operation, etc. The user computer 10 may output a request based on a user input trigger, or based on a time-limited trigger such as when a predetermined number of modification lists have been reached, or every week or month.

[0030] In S502, the controller extracts pair information of a gene name and a corresponding operation name as search information from each of the multiple modification lists, and outputs search queries based on the pair information to the search literature database & search engine 16 in order (S504). The controller receives a list of related literature as search results from the literature database & search engine 16 (S506). Related literature is literature in which the morphemes of the "target gene name" and the corresponding "operation name" of the pair information exist.

[0031] The controller processes the morphemes of the relevant literature and extracts genes other than the target gene from the morphemes as candidate genes for improving the function of the artificial microorganism. Figure 6 is a block diagram explaining the principle of candidate gene search. Reference numeral 600 denotes a metabolic map of a highly productive microorganism.

[0032] As mentioned above, reference numeral 14A is a list of modifications made to the metabolic map. This modification list indicates that the target gene is "geneA" and the manipulation of the target gene is "disruption." This list indicates that by disrupting "geneA," the pathway corresponding to "X" in the metabolic map 600 is inhibited.

[0033] The literature database and search engine 16 extracts relevant documents containing pair information ("geneA" & "destruction") from multiple documents. Documents that contain either "geneA" or "destruction," or neither, are non-relevant documents. In Figure 6, document A is a relevant document, and document B is a non-relevant document.

[0034] The controller analyzes the morphemes of the related literature, extracts genes other than the target gene (gene A) as candidate genes (S508), and registers the candidate genes in management table 602 (S510). An example of this table is shown in Figure 7. Genes in the X direction are "candidate genes," and genes in the Y direction are "target genes."

[0035] In S510, the controller registers the number of co-occurrences of the candidate genes in the management table 602. The "number of co-occurrences of the candidate genes" is the number of documents in which the target gene and the candidate gene appear together. In FIG. 7, the cell indicated by 700 indicates that the number of documents in which adhA (target gene) and pflB (candidate gene) appear together is "2." The larger the number of co-occurrences, the larger the number of documents, i.e., the higher the correlation between the two.

[0036] If the candidate gene does not exist in the table, the controller registers it in the table and registers "+1" as the co-occurrence number in the cell. If the candidate gene exists in the table, the controller adds "+1" to the co-occurrence number in the cell and updates the table. The management table 602 may be recorded in the storage device of the server computer 12.

[0037] The controller is S512. Related literature When the processes of S508 and S510 are completed for all candidate genes, the process proceeds to the next step, S514. Related literature When the processing of S508-S512 is completed, the process proceeds to the next step, S516.

[0038] Furthermore, in S516, the controller completes S502-S514 for all modification lists in the design history database 14, and then proceeds to the next step, S518.

[0039] By going through steps S502 to S516, the controller completes the registration of the candidate genes and their co-occurrence counts in the management table 602. Note that co-occurrence may also be rephrased as frequency.

[0040] In S518, the controller evaluates which genes from among the candidate genes are superior in modifying the properties of the biological material based on the management table 602. To do this, the controller adds up the number of co-occurrences of each candidate gene in the table 602.

[0041] Reference numeral 702 in FIG. 7 indicates that the number of co-occurrences of the candidate gene (pflB) with each of multiple target genes is summed. The summed value explains the correlation, association, relationship, or affinity of the candidate gene with the design history database 14. The correlation degree of reference numeral 702 is "+13." The higher the correlation degree, the higher the affinity, association, etc. of the candidate gene with past genetic modification records in the design history database, indicating a higher suitability as a candidate gene.

[0042] The controller evaluates candidate genes by creating a ranking table of candidate genes based on the overall correlation of each of all candidate genes in the management table 602. FIG. 8 is an example of a ranking table 800. The ranking table 800 has a column of correlation scores for each candidate gene and a column of co-occurrence information for each target gene. The number in parentheses for each target gene is the number of co-occurrences with the target gene, i.e., the number of co-occurring documents. The controller may add links to the co-occurring documents to the numbers in parentheses.

[0043] Table 800 shows that the correlation score of the candidate gene (pflB) is "13" and that it is ranked second after the candidate gene (ldhA). Furthermore, among multiple target genes, "pdc" has the highest co-occurrence frequency, indicating that the correlation with "pdc" is higher than that of other target genes. Users can preferentially refer to literature that co-occurs with "pdc."

[0044] The controller outputs the ranking table 800 to the user computer 10 as suggestions for candidate genes (S6). The user computer 10 may notify the user of the ranking table 800, or may select and notify the top-ranked candidate genes. The user computer 10 may also select and notify genes with scores equal to or greater than a predetermined threshold as candidate genes.

[0045] 9 is a block diagram illustrating an example of the relationship between a metabolic map 900, a target gene 902, and a candidate gene 904. The metabolic map 900 includes a metabolic pathway for E. coli to produce ethanol from glucose. The metabolic map 900 includes a target gene 902 and operations for the target gene.

[0046] Furthermore, the metabolic map includes the deletion or suppression of "ldhA" (candidate gene: 902), a gene for producing an enzyme that catalyzes the metabolic pathway from pyruvate to D-lactate, and the enhancement of "pflB" (candidate gene: 902), a gene for producing an enzyme that catalyzes the metabolic pathway from pyruvate to acetyl-CoA.

[0047] The server computer 12 may weight multiple documents belonging to the document database and search engine 16 using a predictive coding technique. Researchers or experts flag a predetermined number of documents (teaching data) in advance as to whether they are relevant to improving biological materials. The AI ​​installed in the server computer 12 learns how to weight the morphemes of each document based on the flags.

[0048] Based on the learning results, the AI ​​scores the remaining documents in the literature database and search engine 16 and performs ranking of multiple documents. The server computer 12 can instruct the literature database and search engine 16 to search for documents within a predetermined ranking.

[0049] The controller can extract candidate genes from relevant documents by applying weights to the documents, thereby determining candidate genes that are more highly correlated with the gene of interest.

[0050] Although the controller sums up the co-occurrence frequencies with all target genes when determining the co-occurrence frequency of a candidate gene, it may also narrow down the target genes to specific target genes rather than all target genes. This narrowing down may be left to the user's selection, or the target genes for which the co-occurrence frequency is to be calculated may be narrowed down according to the frequency of the target genes in all modification lists in the design history database 14.

[0051] Although the controller counts the number of co-occurrences per document, this is not limiting, and the controller may count per paragraph or sentence of the related document. The co-occurrence degree of the candidate gene may then be calculated based on the occurrence rate (the number of occurrences relative to the total number of morphemes) for each document. Furthermore, a candidate gene with a small number of morphemes between it and the target gene in the related document may be highly evaluated.

[0052] Next, a second embodiment will be described. Fig. 10 is a block diagram of the computer system. This embodiment differs from the embodiment of Fig. 1 in that a gene dictionary 1000 and an operation expression dictionary 1002 are connected to a server computer 12. In the gene dictionary 1000, genes with the same or similar functions are grouped under the same enzyme number.

[0053] The operation expression dictionary 1002 collects operation names that are synonyms or similar words (words with different forms but the same or similar meanings) of the operation names in the modification list. Synonyms may be defined to include synonyms. Synonyms and similar words may be defined as related data elements. For example, morphemes with the same meaning, such as "delete," "remove," "delete," and "erase," are synonyms of each other.

[0054] The controller refers to the gene dictionary 1000 (S7) to obtain a list of genes having the same enzyme numbers as the target genes in the modification list (S8), and further refers to the operation expression dictionary 1002 (S9) to obtain a list of operation names related to the operation names in the modification list (S10). The controller searches the database based on the gene list and operation list.

[0055] The controller determines, as relevant documents, documents that include both at least one gene name included in the gene list and at least one operation name included in the operation list. According to the second embodiment, it is possible to collect candidate genes from a wider range than in the first embodiment.

[0056] Next, a third embodiment will be described. This embodiment differs from the second embodiment in that a compound dictionary is further connected to the server computer 12, and that compounds to be modified are included in the modification list. Compounds to be modified are compounds whose production amount is to be increased by the artificial microorganism through gene introduction, suppression, etc.

[0057] The compound dictionary contains compounds related to the compound to be modified, such as precursors, derivatives, decomposition products, and identical compounds with different nomenclature. When the controller acquires the compound to be modified from the modification list, it references the compound dictionary to acquire a list of related compounds related to the modified compound.

[0058] The controller determines, as relevant literature, literature that includes at least one gene name included in the gene list, at least one operation name included in the operation list, and at least one related compound included in the compound list. According to the third embodiment, it is possible to collect a wider range of candidate genes that are more suitable for modifying artificial microorganisms than in the second embodiment.

[0059] The above-described embodiments are examples of the present invention, and the technical scope of the present invention is not limited to those described in the embodiments. In the above-described embodiments, technical matters described as functions of the computer system can be identified by the terms "means" and "element." The functions may be realized not only by software, but also by hardware, or a combination of hardware and software. [Explanation of symbols]

[0060] 10 User Computers 12 Server Computers 14 Design History Database 16 Literature (related information) databases and search engines

Claims

1. A computer system for supporting designs for improving the function of biological resources, comprising: The computer system includes a controller that executes a program stored in a memory, The controller: A history of the design is registered, the history of the design including pair information of a target gene for the biological resource and an operation on the target gene; Searching a database based on the pair information to extract a list of related documents containing the pair information; Extracting genes other than the target gene from the list of related literature as candidate genes for improving the function of the biological resource; determining a number of co-occurrences between the candidate gene and the target gene based on the list of related literature; and evaluating the candidate genes based on the number of co-occurrences; Computer system.

2. The biological resource is an artificial microorganism; 2. The computer system of claim 1.

3. the database comprises a plurality of documents; The controller: extracting the list of relevant documents from the plurality of documents; 2. The computer system of claim 1.

4. the controller determines the number of documents in which the target gene and the candidate gene are described as the co-occurrence count; 2. The computer system of claim 1.

5. The controller calculates the number of co-occurrences between each of the plurality of target genes and each of the plurality of candidate genes.

4. The computer system of claim 3.

6. The controller: summing up the number of co-occurrences of the candidate gene with each of the plurality of target genes; evaluating the plurality of candidate genes based on the sum; 6. The computer system of claim 5.

7. A method for computer-aided design to improve the functionality of biological resources, The computer includes: A history of the design is registered, the history of the design including pair information of a target gene for the biological resource and an operation on the target gene; Searching a database based on the pair information to extract a list of related documents containing the pair information; Extracting genes other than the target gene from the list of related literature as candidate genes for improving the function of the biological resource; determining a number of co-occurrences between the candidate gene and the target gene based on the list of related literature; and evaluating the candidate genes based on the number of co-occurrences; The method.

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