A method for designing parallel construction of target strains to minimize total gene number

Abstract

The present application relates to the field of biotechnology, and in particular to optimization of genetic manipulation scheduling, and discloses a strain construction scheduling method for minimizing total genetic manipulation. Common genetic manipulation between different target strains determined by the method provides the possibility of reducing the cost and time of strain construction in a biological foundry. Increasing the offspring branch of the intermediate strain in the initial round of construction can effectively reduce the total number of constructed strains. The method helps to reduce the workload of large-scale parallel strain construction in biological foundry, thereby accelerating the design-construction-test-learn (DBTL) cycle of strain development.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, specifically to optimizing genetic manipulation scheduling, and discloses a method for strain construction scheduling that minimizes total gene manipulation. Background Technology

[0002] The parallel, standardized, and automated genetic operations supported by the biofoundry accelerate the strain construction process while improving reproducibility, reducing labor costs, and enabling the development of a wide range of strains.

[0003] However, current biofoundry processes still involve relatively low-depth physical processing (typically single gene mutations per strain). Combining the high-depth strain construction of traditional strains with the high-breadth advantage of biofoundries represents a future trend in biofoundry development. State-of-the-art mechanistic models and mixed-integer algorithms can design microbial strains for the overproduction of hundreds of products, providing multiple metabolic engineering strategies for each. This requires a biofoundry capable of constructing one hundred (or hundreds) target strains, each potentially carrying a dozen or more gene manipulations—a task requiring both high breadth and depth.

[0004] Different target strains often share common genes with identical operations, especially in central metabolic pathways. Constructing these strains separately requires repeatedly manipulating these common genes, necessitating the creation of numerous intermediate strains and resulting in a significant waste of materials and manpower. Summary of the Invention

[0005] In the process of constructing a series of strains, this invention considers using intermediate strains that share a common ancestor with different target strains during the construction process. This reduces the total number of intermediate strains required, thereby lowering the cost of strain construction. It requires an optimization algorithm to determine the optimal common ancestor strain and the parentage of the strains for iterative construction in each round. An intuitive strategy for determining the "common ancestor strain" is to cluster the target strains into several sets based on their shared genetic operations.

[0006] This invention designs a global optimization method to reduce total gene operations. It directly takes minimizing the total number of gene operations as its objective. The method sets three variables to construct the model: the execution of a gene operation, the number of rounds, and the target strain. Under a series of practical constraints, the optimization solver can provide a solution to determine which gene should be operated on on which intermediate strain, and in which round, on which target strain.

[0007] The method of this invention provides a rapidly generated initial solution, in which the algorithm considers the large-scale problem as a whole and directly minimizes the total operation. Therefore, integrating the method of this invention into biocasting practices can significantly reduce the cost, time, and manpower involved in the development of large-scale strains.

[0008] The overall approach to minimizing total operations is as follows: determining the timeline for strain construction can be transformed into a problem with two decisive variables. It is an indicator variable that determines whether or not it should be used in round r( In the target strain s () ) to perform g ( The modification is defined as follows: g includes the gene to be modified and the modification method, Rd is the set of operation rounds, and S is the set of all involved strains. It is the set of all modifications involved in order to construct an algorithm that seeks a solution with the minimum total number of intermediate strains.

[0009] This invention transforms real-world conditions into constraints, which are then implemented using relevant algorithms.

[0010] Therefore, the present invention provides a method for designing parallel construction of target strains to minimize the total number of gene operations, comprising the following steps:

[0011] Step 1: Obtain the genotypes of all starting strains. Obtain the genotypes of each target strain, including all genetic modifications made based on the starting strains. This step provides boundary constraints for the next optimization calculation.

[0012] Step 2: Under a series of practical constraints, using the minimization of total gene modification operations as the objective function, calculate the strain construction schedule:

[0013] That is, (1) logical variables The value of indicates whether g modification needs to be performed in the r-th round of modification to construct the target strain s, and .

[0014] (2) Logical variables The value of: whether the genotypes of the intermediate strains generated for constructing the target strain s1 and the target strain r2 in round r are the same. If they are the same, the two intermediate strains are 'common ancestor strains' and are merged into the same node on the scheduling tree;

[0015] This is achieved by executing the following algorithm:

[0016]

[0017] The optimization algorithm composed of the above formulas does not have a specific order in which the formulas are executed; together with the optimization objective, they form an inseparable whole.

[0018] Step 3: Organize the results obtained in Step 2, list the genetic differences between each sub-strain and its parent strain in the scheduling, and further include drawing the scheduling tree.

[0019] This invention also provides a system for parallel construction of target strains based on the method to minimize the total number of gene operations, comprising a data input module, a data processing module, and a result output module. Specifically, the data processing module executes the algorithm of the second step using a computer. The result output module includes a gene difference list and a scheduling tree drawing submodule.

[0020] Preferably, the results are displayed via a computer screen, a remote terminal, or a mobile terminal. More preferably, they are implemented in the form of a computer package or an internet platform, with the method completed by a processor and the results displayed in a visible manner.

[0021] The present invention also provides an apparatus comprising the system described above, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program, the computer program encoding the system described above, preferably further comprising a data input device such as a keyboard, an image recognition device, etc., and a display device such as a screen or a remote result display.

[0022] Preferably, it exists in the form of a computer package or an internet platform.

[0023] The inventive aspect of this invention lies in transforming the seemingly obvious problem of finding a common ancestor to reduce the number of operations into an optimization problem. This allows for the use of optimization solvers to address a large number of strains (hundreds of strains), achieving a globally optimal solution in a shorter time compared to the intuitive search for a common ancestor strain (i.e., no other solution has fewer operations). The common gene operations among different target strains identified by this method offer the possibility of reducing the cost and time of strain construction in biofoundries. Increasing the number of progeny branches of intermediate strains in the initial construction rounds effectively reduces the total number of strains to be constructed. This method helps alleviate the workload of large-scale parallel strain construction in biofoundry, thereby accelerating the design-build-test-learn (DBTL) cycle of strain development. Attached Figure Description

[0024] Figure 1 A schematic diagram of the individual target strain scheduling.

[0025] Figure 2A schematic diagram of the scheduling constructed using the method of the present invention for the target strain.

[0026] Figure 3 For the design tasks in Table 1, the strains designed using the method of this patent are used to construct a scheduling tree. Detailed Implementation

[0027] The following describes specific embodiments of the present invention in detail, but this does not constitute a limitation on the present invention.

[0028] Example 1: Total Operation Minimization Optimization Method

[0029] I. Principles of the Total Operation Minimization Optimization Method

[0030] Each strain is labeled 's', and a strain set S needs to be constructed from all the strains. Genotype G s G s It contains all the gene modifications g of this strain on the starting strain, so each gene modification Genetic modification is represented by abbreviations, such as R for gene knockout, R followed by the gene name (R_GND indicates knocking out the GND gene), O for overexpression, and D for attenuation, etc.

[0031] To minimize the total number of operations, under a series of practical constraints, the strain construction schedule needs to be calculated with the goal of minimizing the total number of gene modifications: that is, (1) logical variables. The value of: whether g modification needs to be performed in the r-th round of modification to construct the target strain s, and (2) logical variables. The value of : whether the genotypes of the intermediate strains generated for constructing the target strain s1 and the target strain s2 are the same in round r. If they are the same, the two intermediate strains are 'common ancestor strains' and are merged into the same node on the scheduling tree.

[0032] To complete the construction of each target strain, each gene g of strain s must be manipulated only once throughout the entire process:

[0033] ;

[0034] In each round, the operation applied to construct the target strain is no more than once, where Rd is the set of gene operation rounds:

[0035] ;

[0036] The processing capacity of a bio-foundry is limited, so the total number of operations per round must be limited, which can be the number of holes H on the plate.

[0037] ;

[0038] If two target strains s1 and s2 share an intermediate ancestor strain in round r, as a binary variable M s1,s2,r =1 means that all operations performed on the two target strains in the r-th round and before must be the same, which can be expressed as the following constraint:

[0039] ;

[0040] In the same round, two target strains can have at most one ancestor strain:

[0041] ;

[0042] If two target strains share an ancestral strain, the genetic target can only be manipulated no more than once in their common ancestor:

[0043] ;

[0044] When variables Given the above constraints, the optimization objective is set to minimize the total number of gene operations T in the construction of the target strain, which should be: The sum minus the number of times the two intermediate strains were combined, i.e., the total The sum of .

[0045] .

[0046] II. Steps of the Total Operation Minimization Optimization Method

[0047] Based on the above principles, the total operation minimization optimization method is as follows:

[0048] Step 1: Obtain the genotypes of all starting strains. Obtain the genotypes of each target strain, including all genetic modifications made based on the starting strains. This step provides boundary constraints for the next optimization calculation.

[0049] Step 2: Calculate whether modification g is needed in the r-th round of modification to construct the target strain s, and whether the strain produced by this modification has the same genotype as the strains produced by other modifications in this round. Treat strains with the same genotype as the same strain (i.e., 'common ancestor strain'). Execute the following algorithm:

[0050]

[0051] The variables can be obtained from the above. , The meaning of the values ​​of T and T is explained above.

[0052] Step 3: Organize the results obtained in Step 2, list the genetic differences between each substrain and its parent strain, and draw a scheduling tree based on the results. The scheduling diagram is shown below. Figure 2 Indicative representation.

[0053] Example 2: Application of the method of minimizing total operations

[0054] Table 1 lists 60 strains to be constructed and the gene target sites to be knocked out for each strain. Individual construction means that the first strain will be constructed entirely independently, and the scheduling is as follows: Figure 1 As shown in Table 1, constructing these strains individually would require 664 gene knockouts.

[0055] The method of this invention is used to optimize the scheduling. Each strain in Table 1 and its gene manipulation requirements (i.e., the genotype in the table) are used as the raw data for calculation (the starting strain is a wild-type strain unless otherwise specified). That is, the set of strains S to be constructed given in Table 1, and each strain Genotype G s G s It contains all the genetic modifications made to this strain from the starting strain, so each modification... For example, in the first row of Table 1, the genotype corresponding to strain s='M_leu-L_e' is... This is a set like this: {R_GND,R_HEX1,R_HEX7,R_LDH_D,R_PDH,R_PFL,R_POR5,R_POX,R_PPS}. For example, if g='R_GND', then g definitely belongs to the set. .

[0056] Thus, using the scheduling algorithm of this invention, the number of gene operations obtained is 370. In total, only 370 genes need to be knocked out (e.g., ...). Figure 3 As shown in the figure (the numbers in the figure are the strain numbers), the total number of gene knockouts was reduced by 44.28% compared to constructing these strains individually, thus obtaining all the strains listed in Table 1.

[0057] Table 1. Strains used for testing - construction tasks

[0058] (This task includes 60 strains, and all genes are composed of the knockout 'R' followed by the gene abbreviation.)

[0059]

[0060]

[0061] .

[0062] The above embodiments and application examples are merely illustrative. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention, and therefore all of them fall within the scope of the present invention.

Claims

1. A method for designing parallel constructions of target strains to minimize the total number of gene operations, comprising the following steps: Step 1: Obtain the genotypes of all starting strains and each target strain. The genotype of each target strain includes all genetic modifications made based on the starting strains. Step 2: Calculate the strain construction schedule with the objective function of minimizing the total gene modification operations; The total gene modification operation refers to all gene modifications performed from the starter strain to the completion of the target strain construction; In the second step, it is calculated whether g gene modification is required to construct the target strain s in the r-th round of modification, and whether the strain produced by this modification has the same genotype as the strains produced by other modifications in this round. Strains with the same genotype are considered as the same strain, i.e., 'common ancestor strain'. In the second step, the following algorithm is executed: ； The variables can be obtained from the above. , The value of the total number of gene operations T, in, The value: Whether g modification needs to be performed in the r-th round of modification to construct the target strain s, and The value of: whether the genotypes of the intermediate strains generated for constructing the target strain s1 and the target strain s2 are the same in round r. If they are the same, the two intermediate strains are 'common ancestor strains' and are merged into the same node on the scheduling tree; Where S represents the collection of strains, and G... s Rd is the genotype of each target strain s, Rd is the set of gene manipulation rounds, and H is the total number of gene manipulations in each round.

2. The method as described in claim 1, characterized in that, It also includes a third step: Organize the results obtained in the second step and list the genetic differences between each daughter strain and its parent strain in the schedule.

3. The method as described in claim 2, characterized in that, The third step also includes drawing a scheduling tree.

4. A system for designing target strains to minimize the total number of gene operations based on the method described in any one of claims 1 to 3, comprising a data entry module, a data processing module, and a result output module.

5. The system as described in claim 4, characterized in that, The data processing module executes the second step algorithm using a computer; the result output module includes a gene difference list and a scheduling tree drawing submodule.

6. The system as described in claim 4, characterized in that, The results are displayed via computer screens, remote terminals, and mobile terminals.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program, the computer program being encoded to implement the system as claimed in any one of claims 4 to 6.

8. The computer device as claimed in claim 7, characterized in that, It also includes data input devices such as keyboards and image recognition devices, as well as display devices such as screens or remote result displays.

9. The computer device as claimed in claim 7, characterized in that, It exists in the form of computer packages or internet platforms.

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