A method, system, device and medium for strain modification based on multi-objective optimization

Abstract

The present application discloses a strain modification method, system, device and medium based on multi-objective optimization. This method establishes a mathematical model for strain modification according to the tasks and requirements of strain modification; proposes a search method for strain modification targets based on multi-objective optimization according to the mathematical model of strain modification; constructs an initial population according to the multi-objective intelligent optimization task described by the mathematical model of strain modification; obtains the Nth parental population; performs crossover and mutation on the Nth parental population to obtain the Nth mutant population; calculates the updated performance index of the Nth mutant population, and determines the (N + 1)th parental population according to the updated performance index; if the (N + 1)th parental population meets the preset conditions, then generates an optimal strain modification plan according to the (N + 1)th parental population, which can utilize multi-objective programming to obtain a strain that can both produce a large amount of target products and achieve steady-state growth, thereby improving the yield of specific compounds and realizing the enhancement of production performance.

Description

Technical Field

[0001] The present application relates to the technical field of optimizing strain modification, and in particular, to a strain modification method, system, device and medium based on multi-objective optimization. Background Art

[0002] With the continuous improvement of information technology, in modern biotechnology, by changing the natural metabolic pathway of microorganisms, the yield of specific compounds can be increased, or new pathways can be introduced to produce non-natural products. In the current strain modification technology, the existing genetic algorithm optimization only considers the influence of single factors and cannot achieve the best modification effect. Moreover, by using the existing technology to modify and cultivate strains in different ways, the reaction network cannot be widely modified, and there are technical problems such as long experimental period, unsatisfactory cultivation effect and low efficiency in the feasibility and efficiency of experimental optimization means. Summary of the Invention

[0003] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of protection of the claims.

[0004] The main purpose of the embodiments of the present disclosure is to propose a strain modification method, system, device and storage medium based on multi-objective optimization, which can optimize cell growth and growth-coupled synthesis, identify and screen high-quality optimization strategies, and improve production yield and production performance.

[0005] The first aspect of the embodiments of the present application provides a strain modification method based on multi-objective optimization for a central controller, and the method includes:

[0006] Establish a mathematical model for the strain modification according to the tasks and requirements of the strain modification;

[0007] According to the mathematical model of the strain modification, propose a search method for strain modification targets based on multi-objective optimization, so as to reduce the number of strain reactions as candidate variables in the multi-objective intelligent optimization task described by the mathematical model of the strain modification according to the search method;

[0008] Construct an initial population according to the multi-objective intelligent optimization task described by the mathematical model of the strain modification;

[0009] Obtain the Nth parental population; if N is 1, the Nth parental population is selected from the initial population, and if N is a positive integer greater than 1, the Nth parental population is generated based on the (N - 1)th parental population;

[0010] Obtain the Nth mutant population by cross-mutation of the Nth parental population;

[0011] Calculate the updated performance index of the Nth mutant population, and determine the (N + 1)th parental population according to the updated performance index; if the (N + 1)th parental population meets the preset conditions, generate an optimal strain modification scheme according to the (N + 1)th parental population;

[0012] Among them, the process of selecting the Nth parental population from the initial population includes:

[0013] Randomly select two individuals of the initial population from the initial population; calculate the fitness of the individuals of the initial population respectively, and take the individual with the larger fitness as the individual of the Nth leading parental population; among them, if the fitness of the two individuals of the initial population is the same, calculate the crowding distance of the individuals of the initial population respectively, and take the individual with the larger crowding distance as the individual of the Nth leading parental population;

[0014] Calculate the fitness of all individuals in the initial population that are not individuals of the Nth leading parental population respectively; calculate the selection probability of all individuals in the initial population that are not individuals of the Nth leading parental population according to the fitness of the individuals in the initial population that are not individuals of the Nth leading parental population; use the roulette wheel method to determine the individuals of the Nth subsequent parental population according to the selection probability of the individuals in the initial population that are not individuals of the Nth leading parental population;

[0015] Take the Nth leading parental population and the Nth subsequent parental population as the Nth parental population.

[0016] The embodiment of the present application provides a strain modification method based on multi-objective optimization. By establishing a mathematical model of strain modification according to the tasks and requirements of strain modification; proposing a search method for strain modification targets based on multi-objective optimization according to the mathematical model of strain modification; constructing an initial population according to the multi-objective intelligent optimization task described by the mathematical model of strain modification; obtaining the Nth parental population; performing crossover and mutation on the Nth parental population to obtain the Nth mutant population; calculating the updated performance index of the Nth mutant population, and determining the (N + 1)th parental population according to the updated performance index; if the (N + 1)th parental population meets the preset conditions, generate an optimal strain modification scheme according to the (N + 1)th parental population, which can utilize multi-objective programming to obtain a strain that can not only produce a large amount of target products but also achieve steady-state growth, thereby increasing the yield of specific compounds and realizing the enhancement of production performance.

[0017] In some embodiments of the present application, the mathematical model of strain modification includes:

[0018] Set a metabolic network of a strain containing m metabolites and n reactions, and the metabolic space of the metabolic network is defined as:

[0019] FS(v) = {v ∈ R n | Sv = 0, lb ≤ v ≤ ub};

[0020] Among them, FS(v) represents the metabolic space of v, S is the stoichiometric coefficient matrix of m rows and n columns of reactions, v is the reaction rate vector on the metabolic network, and lb and ub are the range values of v;

[0021] Construct two optimization objectives that satisfy the following conditions: maximize the yield of a given product P under the worst case, max min w P , w P is the yield of product P after strain modification and the coupling strength between the maximum yield and strain growth, max min Cs:

[0022]

[0023]

[0024] u ∈ FS(u), w ∈ FS(w);

[0025]

[0026] Among them, u and w respectively represent the rate vectors of the wild type and the perturbed steady state. F > 1 is the change multiple between u and w defined by the user, and J is the set of reactions that can be modified. are binary 0-1 variables, representing the up-regulation, down-regulation, and knockout operations of j ∈ J respectively. K is the maximum value of the preset modification quantity. The constraint ensures that the candidate reaction can only perform one of the operations of up-regulation, down-regulation, and knockout. max or max(A(B = C)) means calculating the maximum or minimum value of A when B = C, w g and w p are the growth rate and production rate of the perturbed network under the considered design strategy respectively. is the maximum growth rate.

[0027] In some embodiments of the present application, reducing the number of strain reactions as candidate variables in the multi-objective intelligent optimization task described by the mathematical model of the strain modification according to the search method includes:

[0028] According to the search method, eliminate the strain reactions in the metabolic network that do not meet the preset conditions;

[0029] Compress the strain reactions to obtain candidate variables in the multi-objective intelligent optimization task described by the mathematical model of the strain modification;

[0030] After the search method for strain modification targets based on multi-objective optimization is proposed, it further includes:

[0031] Chromosomally encode the strain reactions according to the metabolic network to obtain a plurality of strain chromosomes, where the strain chromosomes include at least one of the strain reactions.

[0032] In some embodiments of the present application, the obtaining of the Nth mutant population by cross-varying the Nth parental population includes:

[0033] Randomly select a chromosomal position from the individuals of the Nth parental population as the crossover point;

[0034] Exchange the chromosomal segments of the individuals of the Nth parental population at the crossover point to obtain the processed individuals of the Nth parental population;

[0035] Randomly select at least one chromosomal position from the processed individuals of the Nth parental population as the mutation point;

[0036] Mutate the corresponding chromosomes of the individuals of the processed Nth parental population at all the mutation points to obtain the individuals of the Nth offspring population;

[0037] Combine the Nth parental population and the Nth offspring population to obtain the Nth mutant population.

[0038] In some embodiments of the present application, the calculating of the updated performance metric of the Nth mutant population and determining the (N + 1)th parental population according to the updated performance metric includes:

[0039] Calculate the objective values of the individuals in all the Nth mutant populations according to the mathematical model of strain modification;

[0040] Rank the individuals of the Nth mutant population according to their objective values to determine the non-dominated levels of each individual in the Nth mutant population;

[0041] Calculate the crowding distance of the individuals in each non-dominated level;

[0042] Determine the individuals of the (N + 1)th parental population according to the non-dominated levels of the individuals of the Nth mutant population. In the case where the number of individuals in the non-dominated level is greater than the size of the (N + 1)th parental population, select the individuals with the highest crowding distance in the non-dominated level as the individuals of the (N + 1)th parental population.

[0043] In some embodiments of the present application, the calculating of the updated performance metric of the Nth mutant population and determining the (N + 1)th parental population according to the updated performance metric includes:

[0044] Calculate the objective values of the individuals in all the Nth mutant populations according to the metabolic network;

[0045] Rank the individuals of the Nth mutated population according to the objective values of the individuals in the Nth mutated population, and determine the non-dominated levels of the individuals in each of the Nth mutated populations;

[0046] Calculate the crowding distance of the individuals in each of the non-dominated levels;

[0047] Determine the individuals of the (N + 1)th parental population according to the non-dominated levels of the individuals in the Nth mutated population. In the case where the number of individuals in the non-dominated level is greater than the size of the (N + 1)th parental population, use the individuals with the highest crowding distance in the non-dominated level as the individuals of the (N + 1)th parental population.

[0048] In some embodiments of the present application, the formula for calculating the crowding distance includes:

[0049]

[0050] CD first,n = CD end,n = 1;

[0051]

[0052] where y i-1,n represents the nth objective value of the (i - 1)th individual, y i+1,n represents the nth objective value of the (i + 1)th individual, y first,n represents the nth objective value of the first individual in the current parental population, y end,n represents the nth objective value of the last individual in the current parental population, CD i,n represents the crowding distance of the nth objective value y i,n of the ith individual, CD i represents the total value of the crowding distance of the ith individual.

[0053] To achieve the above object, a second aspect of the embodiments of the present invention provides a strain modification system based on multi-objective optimization, the system includes:

[0054] A building module, configured to establish a mathematical model of the strain modification according to the tasks and requirements of the strain modification;

[0055] An optimization module, configured to propose a search method for strain modification targets based on multi-objective optimization according to the mathematical model of the strain modification, so as to reduce the number of strain reactions as candidate variables in the multi-objective intelligent optimization task described by the mathematical model of the strain modification;

[0056] A construction module, configured to construct an initial population according to the multi-objective intelligent optimization task described by the mathematical model of the strain modification;

[0057] An acquisition module, configured to acquire the Nth parental population; if N is 1, the Nth parental population is selected from the initial population, and if N is a positive integer greater than 1, the Nth parental population is generated based on the (N - 1)th parental population;

[0058] An iteration module, configured to perform crossover and mutation according to the Nth parental population to obtain the Nth mutant population;

[0059] An update module, configured to calculate the update performance index of the Nth mutant population, and determine the (N + 1)th parental population according to the update performance index; if the (N + 1)th parental population meets the preset conditions, generate an optimal strain transformation plan according to the (N + 1)th parental population;

[0060] Wherein, the process of selecting the Nth parental population from the initial population includes:

[0061] Randomly select two individuals of the initial population from the initial population; calculate the fitness of the individuals of the initial population respectively, and use the individual with the larger fitness as the individual of the Nth leading parental population; wherein, if the fitnesses of the two individuals of the initial population are the same, calculate the crowding distance of the individuals of the initial population respectively, and use the individual with the larger crowding distance as the individual of the Nth leading parental population;

[0062] Calculate the fitness of all individuals in the initial population that are not individuals of the Nth leading parental population respectively; calculate the selection probability of all individuals in the initial population that are not individuals of the Nth leading parental population according to the fitness of the individuals in the initial population that are not individuals of the Nth leading parental population; use the roulette wheel method to determine the individuals of the Nth subsequent parental population according to the selection probability of the individuals in the initial population that are not individuals of the Nth leading parental population;

[0063] Use the Nth leading parental population and the Nth subsequent parental population as the Nth parental population.

[0064] To achieve the above object, a third aspect of the embodiments of the present invention provides an electronic device, including: at least one control processor and a memory for communicatively connecting with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the above-mentioned strain transformation method based on multi-objective optimization.

[0065] To achieve the above object, a fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to make a computer execute the above-mentioned strain transformation method based on multi-objective optimization.

[0066] It is understandable that the beneficial effects of the second to fourth aspects compared with the related art are the same as those of the first aspect compared with the related art. For the relevant descriptions, reference can be made to the relevant descriptions in the first aspect, and details will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The above and / or additional aspects and advantages of the present application will become obvious and easy to understand from the description of the embodiments in conjunction with the following drawings, where:

[0068] Figure 1 is a schematic flowchart of a strain modification method based on multi-objective optimization provided by an embodiment of the present application;

[0069] Figure 2 is an iterative schematic diagram of a strain modification method based on multi-objective optimization provided by an embodiment of the present application;

[0070] Figure 3 is the Pareto front of the final population generated by an embodiment provided by the present application;

[0071] Figure 4 is a schematic structural diagram of a strain modification system based on multi-objective optimization provided by an embodiment of the present application;

[0072] Figure 5 is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application and should not be construed as a limitation to the present application.

[0074] In the description of the present application, if the first, second, etc. are described, it is only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence of the indicated technical features.

[0075] In the description of the present application, it should be understood that the orientation descriptions such as up and down indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the indicated device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0076] In the description of this application, it should be noted that unless otherwise clearly defined, terms such as "setting", "installation", "connection", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in this application in combination with the specific content of the technical solution.

[0077] First, several nouns involved in this application are analyzed:

[0078] In the field of strain cultivation design, a multi-objective optimized strain refers to using multi-objective programming to improve the production capacity of strain cultivation and optimize the strain cultivation effect.

[0079] Microbial cell factories (MCFs) are biological systems modified by modern biotechnology means, which can efficiently produce target products. It mainly involves changing the natural metabolic pathways of microorganisms to increase the yield of specific compounds or introducing new pathways to produce non-natural products.

[0080] In actual industrial production, the feasibility of microbial cell factories requires extensive modification of the reaction network to enhance their production performance. However, in current strain modification technologies, existing genetic algorithm optimization only considers the influence of single factors and cannot achieve the best modification effect. Moreover, by using existing technologies to modify and cultivate strains differently, it is impossible to extensively modify the reaction network, and there are technical problems such as long experimental periods, unsatisfactory cultivation effects, and low efficiency in the feasibility and efficiency of experimental modification means.

[0081] Based on this, the embodiments of this application provide a strain modification method, system, electronic device, and medium based on multi-objective optimization, aiming to modify cell growth and growth-coupled synthesis, identify and screen high-quality optimization strategies, and improve production yield and production performance.

[0082] The strain modification method, system, electronic device, and medium based on multi-objective optimization provided by the embodiments of this application are specifically described through the following embodiments. First, the strain modification method based on multi-objective optimization in the embodiments of this application is described.

[0083] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0084] The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0085] The strain transformation method based on multi-objective optimization provided by the embodiments of the present application relates to the technical field of strain transformation. The strain transformation method based on multi-objective optimization provided by the embodiments of the present application can be applied to a terminal, or to a server side, or can also be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that realizes the strain transformation based on multi-objective optimization, etc., but is not limited to the above forms.

[0086] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet-type devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, small computers, large computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0087] It should be noted that in each specific embodiment of the present application, when it comes to relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first. Moreover, the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of the present application need to obtain the user's sensitive personal information, the user's separate permission or separate consent will be obtained through methods such as pop-up windows or redirecting to a confirmation page. After clearly obtaining the user's separate permission or separate consent, the necessary user-related data for the normal operation of the embodiments of the present application will be obtained.

[0088] To this end, referring to Figure 1 , an embodiment of the present application provides a strain modification method based on multi-objective optimization. This method is applied to a central controller. The controller can be a server, an electronic device, a mobile terminal, etc., which is not specifically limited here. The method includes the following steps S110 to S160:

[0089] Step S110: Establish a mathematical model for strain modification according to the tasks and requirements of strain modification.

[0090] In this step, first, according to the tasks and requirements of strain modification, determine the metabolites produced by the strain under specific conditions through laboratory techniques (such as mass spectrometry), or use existing bioinformatics databases (such as KEGG, MetaCyc, etc.) to query its possible metabolic pathways and participating enzymes according to the strain name or genomic information, obtain strain metabolites and strain reactions, and then establish a mathematical model for strain modification.

[0091] The following specifically explains the mathematical model for strain modification:

[0092] Assume that a strain has a metabolic network with m metabolites and n reactions. The metabolic space of the metabolic network is defined as:

[0093] FS(v) = {v ∈ r n | Sv = 0, lb ≤ v ≤ ub};

[0094] where FS(v) represents the metabolic space of v, S is the stoichiometric coefficient matrix of m rows and n columns of reactions, v is the reaction rate vector on the metabolic network, and lb and ub are the range values of v;

[0095] Construct two optimization objectives that satisfy the following conditions: maximize the yield of a given product P under the worst case max min w P2 , w P is the yield of product P after strain modification and maximize the coupling strength between the maximum yield and strain growth max min CS:

[0096]

[0097] The coupling strength constraint is expressed as:

[0098]

[0099] The rate increase constraint is expressed as:

[0100]

[0101] The rate decrease constraint is expressed as:

[0102]

[0103] The reaction knockout constraint is expressed as:

[0104]

[0105] The feasible operation constraint is expressed as:

[0106]

[0107] The operation quantity constraint is expressed as:

[0108]

[0109] The flux space constraint is expressed as:

[0110] u ∈ FS(u), w ∈ FS(w);

[0111] The operation variable constraint is expressed as:

[0112]

[0113] Where, is the production rate of the target metabolite, u and w represent the rate vectors of the wild type and the perturbed steady state respectively, F > 1 is the change multiple between u and w defined by the user, J is the set of reactions that can be modified, are binary 0-1 variables, representing the up-regulation, down-regulation and knockout operations of j ∈ J respectively, K is the maximum value of the preset modification quantity, and the constraint ensures that only one of the up-regulation, down-regulation and knockout operations can be performed on the candidate reaction, max or max(A(B = C)) means calculating the maximum or minimum value of A when B = C, w g and w p are the growth rate and production rate of the perturbed network under the considered design strategy respectively, is the maximum growth rate.

[0114] Step S120: According to the mathematical model of strain transformation, a search method for strain transformation targets based on multi-objective optimization is proposed to reduce the number of strain responses as candidate variables in the multi-objective intelligent optimization task described by the mathematical model of strain transformation according to the search method.

[0115] In this step, the target of strain transformation refers to the reaction of the main target of the final strain transformation determined according to the mathematical model of strain transformation. According to the mathematical model of strain transformation, a search method for strain transformation targets based on multi-objective optimization is proposed, which specifically includes a comprehensive analysis of the metabolic network of the strain, identifying all potential controllable reactions, and then reducing the number of strain reactions as candidate variables in the multi-objective intelligent optimization task described by the mathematical model of strain transformation based on the identified controllable reactions and analysis results. This can not only effectively reduce the number of strain reactions as candidate variables in the multi-objective intelligent optimization task, but also improve the production efficiency of the target product while ensuring cell homeostasis.

[0116] In some embodiments, in step S120, reducing the number of strain responses as candidate variables in the multi-objective intelligent optimization task described by the mathematical model of strain transformation according to the search method includes the following steps:

[0117] Step S210: Eliminate strain reactions that do not meet preset conditions in the metabolic network according to the search method;

[0118] Step S220: compress the strain response to obtain candidate variables in the multi-objective intelligent optimization task described by the mathematical model of strain transformation.

[0119] In this embodiment, according to the controllable reactions identified by the search method for strain transformation targets based on multi-objective optimization and the analysis results, the number of strain reactions as candidate variables in the multi-objective intelligent optimization task described by the mathematical model of strain transformation is determined to be reduced. First, those strain reactions that are always inactive or have constant flux values in the metabolic network, that is, strain reactions that do not affect cell homeostasis and product yield, are eliminated. Then, the linear reaction paths are compressed. Specifically, the situation where only one reaction product is the only substrate of another reaction can be merged to reduce the number of redundant reactions in the metabolic network, so that the model can be simplified to ensure that the stoichiometric matrix S of the original metabolic network is consistent with the simplified metabolic network, while reducing the number of elements in J and maintaining the consistency of the overall metabolic flow.

[0120] In some embodiments, after the search method for strain transformation targets based on multi-objective optimization is proposed in step S120, the following steps are also included:

[0121] Chromosome encoding of strain reactions is performed according to the metabolic network to obtain multiple strain chromosomes, and each strain chromosome contains at least one strain reaction.

[0122] In one embodiment, chromosome encoding is first performed. There are n regulatable reactions in the metabolic network. The chromosome is defined as a vector containing n elements, and each element corresponds to the operation of a reaction. Each reaction can be up-regulated, down-regulated, deactivated, or not perturbed. Therefore, each element of the chromosome can take one of four operations: 0 (no perturbation), 1 (up-regulation), 2 (down-regulation), 3 (knockout). By encoding the chromosome, the problem can be iteratively solved by an improved non-0-1 encoding genetic algorithm, and it is beneficial to be implemented on a computer device.

[0123] In this step, chromosome encoding of strain reactions according to the metabolic network is to convert each regulatable strain reaction into a number or symbol for processing in the genetic algorithm, so that the problem can be iteratively solved by an improved non-0-1 encoding genetic algorithm, and it is beneficial to be implemented on a computer device.

[0124] Step S130: Construct an initial population according to the multi-objective intelligent optimization task described by the mathematical model of strain modification.

[0125] Step S140: Obtain the Nth parental population; if N is 1, the Nth parental population is selected from the initial population; if N is a positive integer greater than 1, the Nth parental population is generated based on the (N - 1)th parental population.

[0126] In this step, the 1st parental population is selected from the initial population. When N is a positive integer greater than 1, the Nth parental population is generated based on the (N - 1)th parental population.

[0127] The following explains the process of selecting the Nth parental population from the initial population, including:

[0128] Randomly select two individuals from the initial population; calculate the fitness of the individuals in the initial population respectively, and take the individual with the larger fitness as the individual of the Nth front parental population; among them, if the fitness of the two individuals in the initial population is the same, calculate the crowding distance of the individuals in the initial population respectively, and take the individual with the larger crowding distance as the individual of the Nth front parental population.

[0129] Calculate the fitness of all individuals in the initial population that are not individuals of the Nth front parental population respectively; calculate the selection probability of all individuals in the initial population that are not individuals of the Nth front parental population according to their fitness; use the roulette wheel method to determine the individuals of the Nth subsequent parental population according to the selection probability of all individuals in the initial population that are not individuals of the Nth front parental population.

[0130] Use the Nth front parent population and the Nth subsequent parent population as the Nth parent population.

[0131] In one embodiment, an improved non-dominated sorting genetic algorithm II (NSGA-II) is established to form an initial design population and perform an initial evaluation. Then, an initial population is established by random generation. Additionally, considering that it is difficult to modify a large number of reactions in the metabolic network in practical applications, the non-zero entries are set as the K value in the formula At the same time, considering that knocking out too many reactions may lead to cell death, to increase the possibility of generating feasible solutions, the number of knocked-out reactions is limited to at most 3, making the initially generated population reasonable and realizable.

[0132] Furthermore, to select individuals for crossover and mutation, a method combining roulette wheel selection and tournament selection is adopted. Among them, the Nth front parent population represents the parent population to be selected in the first 20% of the iterations. The 2-tournament selection method is applied, and selection is based on the ranking of the chromosomes in the non-dominated sorting. If there is a tie, the crowding distance of the chromosomes is compared. The Nth subsequent parent population represents the parent population to be selected in the subsequent 80% of the iterations. Roulette wheel selection is used, and based on the fitness of the chromosomes (defined as the crowding distance of the chromosomes), the fitness of the boundary individuals is set to 1.

[0133] By using a combination of tournament and roulette wheel selection in the selection of crossover individuals, high-quality chromosomes have a higher crossover probability, thus guiding the population iteration towards a better solution. At the same time, it is set that there is at least one non-zero term at the crossover site, ensuring the effectiveness of the crossover.

[0134] The following explains the formula for calculating the crowding distance, including:

[0135]

[0136] CD first,n = CD end,n = 1;

[0137]

[0138] where y i-1,n represents the nth objective value of the (i - 1)th individual, y i+1,n represents the nth objective value of the (i + 1)th individual, y first,n represents the nth objective value of the first individual in the current parent population, y end,n represents the nth objective value of the last individual in the current parent population, CD i,n represents the crowding distance of the nth objective value y i,n of the ith individual, CDi Represents the total crowding distance value in the i-th individual.

[0139] In one embodiment, each individual in the initial population is separately evaluated according to the fitness value (i.e., cell growth rate and yield of the target reaction), and combined with crowding distance calculation and non-dominated sorting, and the results will be used for paired selection to perform the crossover operation.

[0140] Step S150: Cross and mutate according to the N-th parental population to obtain the N-th mutated population.

[0141] In this step, first cross and mutate according to the N-th parental population to obtain the N-th offspring population, and then combine the N-th parental population with the N-th offspring population to obtain the N-th mutated population.

[0142] In some embodiments, crossing and mutating according to the N-th parental population in step S150 to obtain the N-th mutated population includes the following steps:

[0143] Step S310: Randomly select a chromosome position from the individuals of the N-th parental population as the crossover point;

[0144] Step S320: Exchange the chromosome segments of the individuals of the N-th parental population at the crossover point to obtain the processed individuals of the N-th parental population;

[0145] Step S330: Randomly select at least one chromosome position from the processed individuals of the N-th parental population as the mutation point, and mutate the corresponding chromosomes of the processed individuals of the N-th parental population at all mutation points to obtain the individuals of the N-th offspring population;

[0146] Step S340: Combine the N-th parental population with the N-th offspring population to obtain the N-th mutated population.

[0147] In this embodiment, the crossover operation adopts a simple single-point crossover method. A chromosome position is randomly selected from the individuals of the N-th parental population as the crossover point, and the chromosome segments of the individuals of the N-th parental population are exchanged at the crossover point to obtain the processed individuals of the N-th parental population. When crossing, it is necessary to ensure that at least one non-zero position participates in the crossing to ensure the effectiveness of the crossover operation.

[0148] Furthermore, the mutation operation adopts multi-point mutation to generate mutant chromosomes. At least one chromosome position is randomly selected from the processed individuals of the N-th parental population as the mutation point, and the corresponding chromosomes of the processed individuals of the N-th parental population are mutated at all mutation points to obtain the individuals of the N-th offspring population; and then the N-th parental population is combined with the N-th offspring population to obtain the N-th mutated population.

[0149] In one embodiment, the process of mutating the chromosomes corresponding to the individuals of the processed Nth parental population in step S330 includes the following steps:

[0150] Step S410: Randomly generate a first mutation value;

[0151] Step S420: When the first mutation value is less than or equal to the preset mutation probability, replace the chromosome corresponding to the individual of the processed Nth parental population with a second mutation value to obtain the individual of the mutated Nth parental population; when the first mutation value is greater than the preset mutation probability, use the individual of the processed Nth parental population as the individual of the mutated Nth parental population, where the second mutation value is a randomly generated value within a preset range;

[0152] Step S430: Determine the non-zero elements of the individuals in the mutated Nth parental population according to the individuals of the mutated Nth parental population;

[0153] Step S440: When the number of non-zero elements is greater than the preset number, randomly replace at least one non-zero element in the individual of the mutated Nth parental population with a zero element until the number of non-zero elements is less than or equal to the preset number to obtain the individual of the Nth offspring population;

[0154] Step S450: When the number of non-zero elements is less than or equal to the preset number, use the individual of the mutated Nth parental population as the individual of the Nth offspring population.

[0155] In this embodiment, the first mutation value is preferably a random number within the range of 0 to 1. When the first mutation value is less than or equal to the preset mutation probability, replace the chromosome corresponding to the individual of the processed Nth parental population with a second mutation value to obtain the individual of the mutated Nth parental population; when the first mutation value is greater than the preset mutation probability, use the individual of the processed Nth parental population as the individual of the mutated Nth parental population, where the second mutation value is preferably a random integer between 0 and 3. For the newly obtained individual after mutation, count the number of non-zero elements on its chromosome. If the number of non-zero elements exceeds the preset maximum number limit, randomly select at least one non-zero element and set it to 0 until the number of non-zero elements meets the requirements. When the number of non-zero elements is less than or equal to the preset number, use the individual of the mutated Nth parental population as the individual of the Nth offspring population to ensure that the mutation operation does not damage the basic structure of the chromosome.

[0156] In one embodiment, a randomly generated number r (ranging within [0, 1]) is compared with a predefined mutation probability Pm. If r ≤ Pm, mutation occurs at the corresponding position, and the original value is replaced with a random integer between 0 and 3. A feasibility check is performed on the mutated solution. In particular, if the number of non-zero elements in the mutated solution exceeds K, some non-zero elements are randomly selected and reset to zero until the total number of non-zero elements does not exceed K. After mutation, the original population and the mutated individuals are combined, and further operations such as non-dominated sorting and environmental selection are performed.

[0157] Step S160: Calculate the updated performance metrics of the Nth mutated population, and determine the (N + 1)th parental population according to the updated performance metrics; if the (N + 1)th parental population meets the preset conditions, generate the optimal transformation plan for the strain according to the (N + 1)th parental population.

[0158] In this step, calculate the objective values of the individuals in all the Nth mutated populations according to the metabolic network, perform non-dominated sorting on the individuals in the Nth mutated population according to the objective values of the individuals in the Nth mutated population, determine the non-dominated levels of the individuals in each Nth mutated population, calculate the crowding distance of the individuals in each non-dominated level, determine the individuals of the (N + 1)th parental population according to the non-dominated levels of the individuals in the Nth mutated population, and in the case where the number of individuals in the non-dominated level is greater than the size of the (N + 1)th parental population, use the individuals with the highest crowding distance in the non-dominated level as the individuals of the (N + 1)th parental population.

[0159] In some embodiments, calculating the updated performance metrics of the Nth mutated population in step S160 and determining the (N + 1)th parental population according to the updated performance metrics include the following steps:

[0160] Step S510: Calculate the objective values of the individuals in all the Nth mutated populations according to the metabolic network;

[0161] Step S520: Perform non-dominated sorting on the individuals in the Nth mutated population according to the objective values of the individuals in the Nth mutated population, and determine the non-dominated levels of the individuals in each Nth mutated population;

[0162] Step S530: Calculate the crowding distance of the individuals in each non-dominated level;

[0163] Step S540: Determine the individuals of the (N + 1)th parental population according to the non-dominated levels of the individuals in the Nth mutated population, and in the case where the number of individuals in the non-dominated level is greater than the size of the (N + 1)th parental population, use the individuals with the highest crowding distance in the non-dominated level as the individuals of the (N + 1)th parental population.

[0164] In this embodiment, non-dominated sorting and crowding sorting are constructed and executed to evaluate the individuals of the population after crossover and mutation, and excellent individuals are screened to form new offspring. By calculating the objective values of the individuals in the mutated population for quantitative evaluation and consideration, data support is provided for further screening to form a new parent generation.

[0165] For the nth objective value y of the ith individual in any parent population i,n , the crowding distance of this individual is calculated using the following formula:

[0166]

[0167] CD first,n = CD end,n = 1;

[0168]

[0169] where y i-1,n represents the nth objective value of the (i - 1)th individual, y i+1,n represents the nth objective value of the (i + 1)th individual, y first,n represents the nth objective value of the first individual in the current parent population, y end,n represents the nth objective value of the last individual in the current parent population, CD i,n represents the crowding distance of the nth objective value y i,n of the ith individual, CD i represents the total value of the crowding distance in the ith individual. The crowding degree value is used to distinguish individuals on the same front. Individuals with higher values are relatively more dispersed, and more diverse individuals can be preferentially considered when selecting.

[0170] Among them, the dominance relationship of an individual x1 over another individual x2 means that the objective value of x1 is greater than that of x2. Therefore, after determining the dominance relationship, individuals not dominated by any other individual are classified as first front individuals. Individuals only dominated by first front individuals are classified as second front individuals, and so on, to complete the front assignment of all individuals. Then, the individuals on each front are sorted in descending order of objective values.

[0171] Finally, individuals are selected according to the non-dominated front rank. When the set upper limit of the population quantity is exceeded, individuals with higher crowding degree are selected to form a new parent generation and enter the next cycle until the iteration requirements are met. Here, the iteration requirements are not limited.

[0172] In an implementation manner of this embodiment, the method proposed by the present invention is illustrated in combination with the case of overproducing succinic acid in Escherichia coli. We aim to apply our method to this task in order to identify reasonable design strategies to achieve high succinic acid production while ensuring the survival and growth of Escherichia coli.

[0173] The present invention proposes a design method for a coupled cell strain based on multi-objective transformation, comprising the following steps:

[0174] First, a metabolic model of E. coli is established. In this case, the latest E. coli genome-scale metabolic network model iML1515 is used. At the same time, a multi-objective mathematical optimization model is established as follows:

[0175]

[0176] u∈FS(u),w∈FS(w);

[0177]

[0178] in, is the production rate of succinate, u and w represent the flux vectors of the wild type and perturbed steady state, respectively, F>1 is the change factor between u and w defined by the user, which is set to 2 in this implementation, and J is the set of modifiable reactions. is a binary 0-1 variable, representing the upregulation, downregulation and knockout operations of j∈J respectively. K represents the maximum value of the number of modifications set manually, which is set to 10 in this implementation.

[0179] Furthermore, a simplified model was established to remove reactions that were always inactive or had constant flux values in the metabolic network, and to compress linear reactions. The iML1515 model contains 1515 genes connected to 2719 reactions, involving 1192 metabolites. After model compression and candidate reaction screening, we obtained 352 aggregate reactions as interference candidates. These 352 aggregate reactions constitute set J.

[0180] Further, the NSGA-II algorithm is run. The flowchart of the NSGA-II algorithm is as follows: Figure 2 As shown, in this embodiment, the population size is set to 100, and the transformation is performed for a maximum of 100 generations. The crossover rate is set to 0.9, and the mutation rate is 1 / L, where L is the total number of binary decision variables. The population is initialized and meets And remove the initial conditions that do not exceed three, and then perform the initial non-domination and crowding sorting to facilitate the initial crossover operation.

[0181] Furthermore, a crossover mutation operation is performed. Individuals that undergo crossover are calculated based on the crossover probability, and a combination of tournament and roulette wheel selection methods is used for individual selection. In the first 20% of the iterations, the 2-tournament selection method is applied, and selection is based on the ranking of the chromosomes in the non-dominated sorting. If there is a tie, the crowding distance of the chromosomes is compared. In the subsequent 80% of the iterations, roulette wheel selection is used, and based on the fitness of the chromosomes (defined as the crowding distance of the chromosomes), the fitness of the boundary individuals is set to 1. Single-point crossover occurs, and the effectiveness of the crossover is ensured, and 0-0 crossover is prohibited.

[0182] Furthermore, the mutation probability Pm is set to 1 / L, and a randomly generated number r (in the range [0, 1]) is compared with the predefined mutation probability Pm. If r ≤ Pm, mutation occurs at the corresponding position, and the original value is replaced with a random integer between 0 and 3. A feasibility check is performed on the mutated solution. In particular, if the number of non-zero elements in the mutated solution exceeds K, some non-zero elements are randomly selected and reset to zero until the total number of non-zero elements does not exceed K. After mutation, the original population and the mutated individuals are combined, and further operations such as non-dominated sorting and environmental selection are performed.

[0183] Furthermore, non-dominated sorting is performed. After determining the dominance relationship, individuals that are not dominated by any other individuals are classified as first-front individuals, and individuals that are only dominated by first-front individuals are classified as second-front individuals, and so on, to complete the front assignment of all individuals. After that, the individuals in each front are sorted in descending order of the objective value.

[0184] Finally, selection for population evolution is based on the non-dominated front rank. According to the iteration conditions, when the upper limit of the population size is exceeded, individuals with a higher crowding degree are selected to form a new parent generation and enter the next cycle until 100 iterations are completed. Among them, the Pareto front of the final population generated is as Figure 3 shown, and the modification status of the finally selected excellent individuals is shown in Table 1.

[0185] Table 1

[0186] Result Up-regulation Down-regulation Knockout Number of modifications 1 RBFK \ R5PP 2 2 DHORD5 ALLTN, DHORTS ACHBS, FLVR, PETNT181pp, R5PP 7

[0187] In this embodiment, first, a strain is modeled to establish a metabolic network model and an improved non-dominated sorting genetic algorithm is introduced. Through mathematical modeling, a multi-objective optimization problem model for this type of design is established. The initialization process emphasizes rationality and feasibility, ensuring both the survival of individuals and compliance with reality, and enabling the identification and screening of high-quality optimization strategies. Different from traditional cross-individual selection methods, a cross-individual selection method combining tournament and roulette wheel is designed to accelerate the convergence rate of the population. According to the results of non-dominated degree sorting and crowding degree sorting, individuals are selected to become the new parental generation, enabling the population to continuously evolve towards the optimal solution while maintaining a certain population diversity. Moreover, product production can be coupled with growth, and corresponding individual modification strategies can be provided to maintain cell homeostasis while increasing production, which has extensive applications in strain design and reflects its prospects in designing high-yield strains in the field of biomanufacturing.

[0188] As Figure 4 shown, some embodiments of the present application provide a strain modification system based on multi-objective optimization. The system includes a establishment module 410, an optimization module 420, a construction module 430, an acquisition module 440, an iteration module 450, and an update module 460. Specifically:

[0189] The establishment module 410 is used to establish a mathematical model for strain modification according to the tasks and requirements of strain modification;

[0190] The optimization module 420 is used to propose a search method for strain modification targets based on multi-objective optimization according to the mathematical model of strain modification, so as to reduce the number of strain reactions as candidate variables in the multi-objective intelligent optimization task described by the mathematical model of strain modification according to the search method;

[0191] The construction module 430 is used to construct an initial population according to the multi-objective intelligent optimization task described by the mathematical model of strain modification;

[0192] The acquisition module 440 is used to acquire the Nth parental population; if N is 1, the Nth parental population is selected from the initial population, and if N is a positive integer greater than 1, the Nth parental population is generated based on the (N - 1)th parental population;

[0193] The iteration module 450 is used to perform crossover and mutation on the Nth parental population to obtain the Nth mutant population;

[0194] The update module 460 is used to calculate the updated performance index of the Nth mutant population and determine the (N + 1)th parental population according to the updated performance index; if the (N + 1)th parental population meets the preset conditions, an optimal strain modification scheme is generated according to the (N + 1)th parental population;

[0195] Among them, the process of selecting the Nth parental population from the initial population includes:

[0196] Randomly select two individuals from the initial population; calculate the fitness of the individuals in the initial population respectively, and take the individual with the larger fitness as the individual of the Nth leading parental population; among them, if the fitness of the two individuals in the initial population is the same, calculate the crowding distance of the individuals in the initial population respectively, and take the individual with the larger crowding distance as the individual of the Nth leading parental population;

[0197] Calculate the fitness of all individuals in the initial population that are not individuals of the Nth leading parental population; calculate the selection probability of all individuals in the initial population that are not individuals of the Nth leading parental population according to their fitness; use the roulette wheel method to determine the individuals of the Nth subsequent parental population according to the selection probability of the individuals in the initial population that are not individuals of the Nth leading parental population;

[0198] Take the Nth leading parental population and the Nth subsequent parental population as the Nth parental population.

[0199] In some embodiments, the establishment module 410 may include:

[0200] Set a metabolic network in which a strain contains m metabolites and n reactions, and the metabolic space of the metabolic network is defined as:

[0201] FS(v) = {v ∈ R n | Sv = 0, lb ≤ v ≤ ub};

[0202] Among them, FS(v) represents the metabolic space of v, S is the stoichiometric coefficient matrix of m rows and n columns of reactions, v is the reaction rate vector on the metabolic network, and lb and ub are the range values of v;

[0203] Construct two optimization objectives that satisfy the following conditions: maximize the yield of a given product P under the worst case max min w P , w P is the yield of product P after strain modification and the coupling strength between the maximum yield and strain growth max min Cs:

[0204]

[0205] u ∈ FS(u), w ∈ FS(w);

[0206]

[0207] Among them, u and w respectively represent the rate vectors of the reactions of the wild type and the perturbed steady state, F > 1 is the change multiple between u and w defined by the user, and J is the set of reactions that can be modified, is a binary 0-1 variable, representing the up-regulation, down-regulation, and knockout operations of j∈J respectively. K is the maximum value of the preset modification quantity, and the constraint condition ensures that only one of the up-regulation, down-regulation, and knockout operations can be performed on the candidate reaction. max or max(A(B = C)) refers to calculating the maximum or minimum value of A when B = C. w g and w p are the growth rate and production rate of the perturbed network under the considered design strategy respectively, is the maximum growth rate.

[0208] In some embodiments, the optimization module 420 may include: eliminating the strain reactions in the metabolic network that do not meet the preset conditions according to the search method.

[0209] In some embodiments, the optimization module 420 may include: compressing the strain reactions to obtain candidate variables in the multi-objective intelligent optimization task described by the mathematical model of strain transformation.

[0210] In some embodiments, the optimization module 420 may include: performing chromosome coding on the strain reactions according to the metabolic network to obtain multiple strain chromosomes, and the strain chromosomes contain at least one strain reaction.

[0211] In some embodiments, the iteration module 450 may include: randomly selecting a chromosome position from the individuals of the Nth parental population as the crossover point.

[0212] In some embodiments, the iteration module 450 may include: exchanging the chromosome segments of the individuals of the Nth parental population at the crossover point to obtain the processed individuals of the Nth parental population.

[0213] In some embodiments, the iteration module 450 may include: randomly selecting at least one chromosome position from the processed individuals of the Nth parental population as the mutation point.

[0214] In some embodiments, the iteration module 450 may include: mutating the corresponding chromosomes of the processed individuals of the Nth parental population at all mutation points to obtain the individuals of the Nth offspring population.

[0215] In some embodiments, the iteration module 450 may include: combining the Nth parental population and the Nth offspring population to obtain the Nth mutant population.

[0216] In some embodiments, the iteration module 450 may include: randomly generating a first mutation value.

[0217] In some embodiments, the iteration module 450 may include: when the first mutation value is less than or equal to a preset mutation probability, replacing the chromosome corresponding to the individual of the processed Nth parental population with a second mutation value to obtain the individual of the mutated Nth parental population; when the first mutation value is greater than the preset mutation probability, using the processed individual of the Nth parental population as the individual of the mutated Nth parental population, where the second mutation value is a randomly generated value within a preset range.

[0218] In some embodiments, the iteration module 450 may include: determining the non-zero elements of the individuals in the mutated Nth parental population according to the individuals in the mutated Nth parental population.

[0219] In some embodiments, the iteration module 450 may include: when the number of non-zero elements is greater than a preset number, randomly replacing at least one non-zero element in the individual of the mutated Nth parental population with a zero element until the number of non-zero elements is less than or equal to the preset number to obtain the individual of the Nth offspring population.

[0220] In some embodiments, the iteration module 450 may include: when the number of non-zero elements is less than or equal to the preset number, using the individual of the mutated Nth parental population as the individual of the Nth offspring population.

[0221] In some embodiments, the update module 460 may include: calculating the objective values of the individuals in all the Nth mutated populations according to the metabolic network.

[0222] In some embodiments, the update module 460 may include: performing non-dominated sorting on the individuals in the Nth mutated population according to the objective values of the individuals in the Nth mutated population to determine the non-dominated levels of each individual in the Nth mutated population.

[0223] In some embodiments, the update module 460 may include: calculating the crowding distance of the individuals in each non-dominated level.

[0224] In some embodiments, the update module 460 may include: determining the individuals of the (N + 1)th parental population according to the non-dominated levels of the individuals in the Nth mutated population. When the number of individuals in the non-dominated level is greater than the size of the (N + 1)th parental population, using the individual with the highest crowding distance in the non-dominated level as the individual of the (N + 1)th parental population.

[0225] In some embodiments, the update module 460 may include:

[0226]

[0227] CD first,n = CD end,n = 1;

[0228]

[0229] wherein, y i-1,n represents the nth target value of the (i - 1)th individual, and y i+1,n represents the nth target value of the (i + 1)th individual, and y first,n represents the nth target value of the first individual in the current parental population, and y end,n represents the nth target value of the last individual in the current parental population, CD i,n represents the nth target value y of the ith individual i,n of the crowding distance, CD i represents the total value of the crowding distance in the ith individual.

[0230] It should be noted that the strain modification system based on multi - objective optimization provided in this embodiment and the above - mentioned strain modification method based on multi - objective optimization are based on the same inventive concept. Therefore, the relevant content of the above - mentioned strain modification method based on multi - objective optimization also applies to the content of the strain modification system based on multi - objective optimization. Therefore, it will not be elaborated here.

[0231] To solve the technical problems of long time consumption, poor modification effect and low efficiency in the prior art, the system establishes a mathematical model for strain modification according to the tasks and requirements of strain modification; proposes a search method for strain modification targets based on multi - objective optimization according to the mathematical model of strain modification; constructs an initial population according to the multi - objective intelligent optimization task described by the mathematical model of strain modification; obtains the Nth parental population; obtains the Nth mutant population by cross - mutation of the Nth parental population; calculates the updated performance index of the Nth mutant population, and determines the (N + 1)th parental population according to the updated performance index; if the (N + 1)th parental population meets the preset conditions, an optimal strain modification plan is generated according to the (N + 1)th parental population, which can utilize multi - objective programming to obtain a strain that can not only produce a large amount of target products but also achieve steady - state growth, thereby improving the yield of specific compounds and realizing the enhancement of production performance.

[0232] This application embodiment also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above - mentioned strain modification method based on multi - objective optimization is implemented.

[0233] Such as Figure 5 , Figure 5 is the schematic hardware structure diagram of the electronic device provided in this application embodiment. The electronic device includes:

[0234] at least one battery;

[0235] at least one memory;

[0236] At least one processor;

[0237] At least one program;

[0238] The program is stored in the memory, and the processor executes at least one program to implement a method for strain modification based on multi-objective optimization as described above in the present disclosure.

[0239] The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), an in-vehicle computer, etc.

[0240] The electronic device of the embodiment of the present application will be introduced in detail below.

[0241] The processor 1600 can be implemented by using a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present disclosure;

[0242] The memory 1700 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 1700 can store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1700, and the processor 1600 is called to execute a method for strain modification based on multi-objective optimization in the embodiments of the present disclosure.

[0243] The input / output interface 1800 is used to implement information input and output;

[0244] The communication interface 1900 is used to implement communication interaction between this device and other devices, and can implement communication through a wired method (such as USB, network cable, etc.) or through a wireless method (such as a mobile network, WIFI, Bluetooth, etc.);

[0245] The bus 2000 transmits information between the various components of the device (such as the processor 1600, the memory 1700, the input / output interface 1800, and the communication interface 1900);

[0246] Among them, the processor 1600, the memory 1700, the input / output interface 1800, and the communication interface 1900 are communicatively connected to each other inside the device through the bus 2000.

[0247] An embodiment of the present disclosure also provides a storage medium, which is a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the above-described method for strain modification based on multi-objective optimization.

[0248] As a non-transitory computer-readable storage medium, a memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0249] The embodiments described in the embodiments of the present disclosure are for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present disclosure are equally applicable to similar technical problems.

[0250] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present disclosure, and may include more or fewer steps than shown in the figures, or combine certain steps, or different steps.

[0251] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0252] Those of ordinary skill in the art can understand that all or some of the steps in the above-disclosed methods, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0253] In the description of the present application and the above-mentioned accompanying drawings, terms such as "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0254] It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or its similar expressions refer to any combination of these items, including any combination of single items (ones) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0255] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0256] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0257] In addition, in each embodiment of the present application, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0258] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present application. The foregoing storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0259] The above is a specific description of the preferred embodiments of the present application, but the embodiments of the present application are not limited to the above-mentioned implementation manners. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the embodiments of the present application. These equivalent deformations or substitutions are all included within the scope defined by the claims of the embodiments of the present application.

[0260] The above has described the embodiments of the present application in detail with reference to the drawings, but the present application is not limited to the above embodiments. Various changes can be made without departing from the gist of the present application within the knowledge scope of those of ordinary skill in the art.

Claims

1. A method for strain modification based on multi-objective optimization, characterized in that, The method includes: Establishing a mathematical model for the strain modification according to the tasks and requirements of the strain modification; Proposing a search method for strain modification targets based on multi-objective optimization according to the mathematical model for the strain modification, so as to reduce the number of strain reactions as candidate variables in the multi-objective intelligent optimization task described by the mathematical model for the strain modification according to the search method; Constructing an initial population according to the multi-objective intelligent optimization task described by the mathematical model for the strain modification; Obtaining the Nth parental population; if N is 1, the Nth parental population is selected from the initial population, and if N is a positive integer greater than 1, the Nth parental population is generated based on the (N-1)th parental population; Performing crossover and mutation on the Nth parental population to obtain the Nth mutant population; Calculating the updated performance index of the Nth mutant population, and determining the (N + 1)th parental population according to the updated performance index; if the (N + 1)th parental population meets the preset conditions, generating an optimal strain modification plan according to the (N + 1)th parental population; Among them, the process of selecting the Nth parental population from the initial population includes: Randomly selecting two individuals of the initial population from the initial population; respectively calculating the fitness of the individuals of the initial population, and taking the individual with the larger fitness as the individual of the Nth frontier parental population; wherein, if the fitnesses of the two individuals of the initial population are the same, respectively calculating the crowding distance of the individuals of the initial population, and taking the individual with the larger crowding distance as the individual of the Nth frontier parental population; Respectively calculating the fitnesses of all individuals in the initial population that are not individuals of the Nth frontier parental population; calculating the selection probabilities of all individuals in the initial population that are not individuals of the Nth frontier parental population according to the fitnesses of the individuals in the initial population that are not individuals of the Nth frontier parental population; using the roulette wheel method to determine the individuals of the Nth subsequent parental population according to the selection probabilities of the individuals in the initial population that are not individuals of the Nth frontier parental population; Taking the Nth frontier parental population and the Nth subsequent parental population as the Nth parental population; The mathematical model for the strain modification includes: Setting a metabolic network in which a strain contains m metabolites and n reactions, and the metabolic space of the metabolic network is defined as: FS(v) = {v ∈ R n | Sv = 0, lb ≤ v ≤ ub}; Wherein, FS(v) represents the metabolic space of v, S is the stoichiometric coefficient matrix of m rows and n columns of reactions, v is the reaction rate vector on the metabolic network, and lb and ub are the range values of v; Construct two optimization objectives that satisfy the following conditions: maximize the yield of a given product P in the worst case, max min w P , w P is the yield of product P after strain modification and the coupling strength between the maximum yield and strain growth, max min Cs: u ∈ FS(u), w ∈ FS(w); Among them, u and w represent the rate vectors of the wild-type and perturbed steady-state reactions respectively, F > 1 is the change multiple defined by the user between u and w, J is the set of reactions that can be modified, is a binary 0-1 variable, representing the up-regulation, down-regulation, and knockout operations of j ∈ J respectively, K is the maximum value of the preset modification quantity, and the constraint ensures that only one of the up-regulation, down-regulation, and knockout operations can be performed on the candidate reaction. max or max(A(B = C)) means calculating the maximum or minimum value of A when B = C, w g and w p are the growth rate and production rate of the perturbed network under the considered design strategy respectively, is the maximum growth rate.

2. The method for strain modification based on multi-objective optimization according to claim 1, wherein The reducing the number of strain reactions as candidate variables in the multi-objective intelligent optimization task described by the mathematical model for the strain modification according to the search method includes: Eliminating the strain reactions that do not meet the preset conditions in the metabolic network according to the search method; Compressing the strain reactions to obtain candidate variables in the multi-objective intelligent optimization task described by the mathematical model for the strain modification; After proposing the search method for strain modification targets based on multi-objective optimization, it further includes: Chromosomally encode the strain reactions according to the metabolic network to obtain a plurality of strain chromosomes, where each strain chromosome contains at least one of the strain reactions.

3. The method for strain modification based on multi-objective optimization according to claim 1, characterized in that The obtaining of the Nth mutant population by performing crossover and mutation on the Nth parental population includes: Randomly select a chromosomal position from the individuals of the Nth parental population as the crossover point; Exchange the chromosomal segments of the individuals of the Nth parental population at the crossover point to obtain the processed individuals of the Nth parental population; Randomly select at least one chromosomal position from the processed individuals of the Nth parental population as the mutation point; Mutate the corresponding chromosomes of the processed individuals of the Nth parental population at all the mutation points to obtain the individuals of the Nth offspring population; Combine the Nth parental population and the Nth offspring population to obtain the Nth mutant population.

4. The method for strain modification based on multi-objective optimization according to claim 3, wherein The process of mutating the corresponding chromosomes of the processed individuals of the Nth parental population includes: Randomly generate a first mutation value; In the case where the first mutation value is less than or equal to a preset mutation probability, replace the corresponding chromosome of the processed individual of the Nth parental population with a second mutation value to obtain the mutated individual of the Nth parental population; in the case where the first mutation value is greater than the preset mutation probability, use the processed individual of the Nth parental population as the mutated individual of the Nth parental population, where the second mutation value is a randomly generated value within a preset range; Determine the non-zero elements of the individuals in the mutated Nth parental population according to the individuals in the mutated Nth parental population; In the case where the number of non-zero elements is greater than a preset number, randomly replace at least one non-zero element in the individuals of the mutated Nth parental population with a zero element until the number of non-zero elements is less than or equal to the preset number to obtain the individuals of the Nth offspring population; In the case where the number of non-zero elements is less than or equal to the preset number, use the individuals of the mutated Nth parental population as the individuals of the Nth offspring population.

5. The method for strain modification based on multi-objective optimization according to claim 1, wherein The calculating of the updated performance index of the Nth mutant population and determining the (N + 1)th parental population according to the updated performance index includes: Calculate the objective values of the individuals in all the Nth mutant populations according to the mathematical model of strain modification; Perform non-dominated sorting on the individuals of the Nth mutant population according to the objective values of the individuals in the Nth mutant population to determine the non-dominated levels of the individuals in each of the Nth mutant populations; Calculate the crowding distance of the individuals in each non-dominated level; Determine the individuals of the (N + 1)th parental population according to the non-dominated levels of the individuals in the Nth mutant population. In the case where the number of individuals in the non-dominated level is greater than the size of the (N + 1)th parental population, use the individuals with the highest crowding distance in the non-dominated level as the individuals of the (N + 1)th parental population.

6. The method for strain modification based on multi-objective optimization according to claim 5, wherein The formula for calculating the crowding distance includes: CD first,n = CD end,n = 1; Among them, y i-1,n represents the nth objective value of the (i - 1)-th individual, y i+1,n represents the nth objective value of the (i + 1)-th individual, y first,n represents the nth objective value of the first individual in the current parental population, y end,n represents the nth objective value of the last individual in the current parental population, CD i,n represents the crowding distance CD of the nth objective value y of the i-th individual i,n of the i-th individual, CD i represents the total value of the crowding distance in the i-th individual.

7. A strain modification system based on multi-objective optimization, characterized in that The system includes: A building module for building the mathematical model of strain modification according to the tasks and requirements of strain modification; An optimization module, configured to propose a search method for strain modification targets based on multi-objective optimization according to the mathematical model of the strain modification, so as to reduce the number of strain reactions as candidate variables in the multi-objective intelligent optimization task described by the mathematical model of the strain modification according to the search method; A construction module, configured to construct an initial population according to the multi-objective intelligent optimization task described by the mathematical model of the strain modification; An acquisition module, configured to acquire the Nth parental population; if N is 1, the Nth parental population is selected from the initial population, and if N is a positive integer greater than 1, the Nth parental population is generated based on the (N - 1)th parental population; An iteration module, configured to perform crossover and mutation on the Nth parental population to obtain the Nth mutant population; An update module, configured to calculate the update performance index of the Nth mutant population, and determine the (N + 1)th parental population according to the update performance index; if the (N + 1)th parental population meets the preset conditions, generate an optimal strain modification plan according to the (N + 1)th parental population; Wherein, the process of selecting the Nth parental population from the initial population includes: Randomly select two individuals of the initial population from the initial population; calculate the fitness of the individuals of the initial population respectively, and use the individual with the larger fitness as the individual of the Nth front parental population; wherein, if the fitnesses of the two individuals of the initial population are the same, calculate the crowding distance of the individuals of the initial population respectively, and use the individual with the larger crowding distance as the individual of the Nth front parental population; Calculate the fitness of all individuals in the initial population that are not individuals of the Nth front parental population respectively; calculate the selection probability of all individuals in the initial population that are not individuals of the Nth front parental population according to the fitness of the individuals in the initial population that are not individuals of the Nth front parental population; use the roulette wheel method to determine the individuals of the Nth subsequent parental population according to the selection probability of the individuals in the initial population that are not individuals of the Nth front parental population; Use the Nth front parental population and the Nth subsequent parental population as the Nth parental population; The mathematical model of the strain modification includes: Assume that a strain contains a metabolic network with m metabolites and n reactions, and the metabolic space of the metabolic network is defined as: FS(v) = {v ∈ R n | Sv = 0, lb ≤ v ≤ ub}; Wherein, FS(v) represents the metabolic space of v, S is the stoichiometric coefficient matrix of m rows and n columns of reactions, v is the reaction rate vector on the metabolic network, and lb and ub are the range values of v; Construct two optimization objectives that satisfy the following conditions: maximize the yield of a given product P under the worst - case scenario, max min w P , w P where w is the yield of product P after strain modification and Cs is the coupling strength between the maximum yield and strain growth: max min Cs u ∈ FS(u), w ∈ FS(w); Among them, u and w represent the rate vectors of the wild-type and perturbed steady-state reactions respectively, f > 1 is the change multiple defined by the user between u and w, j is the set of reactions that can be altered, are binary 0-1 variables, representing up-regulation, down-regulation, and knockout operations for j ∈ J respectively, K is the maximum value of the preset number of modifications, and the constraint ensures that only one of the up-regulation, down-regulation, and knockout operations can be performed on the candidate reactions. max or max(A(B = C)) means calculating the maximum or minimum value of A when B = C, w g and w p are the growth rate and production rate of the perturbed network under the considered design strategy respectively, is the maximum growth rate.

8. An electronic device, characterized in that: Comprising at least one control processor and a memory communicatively connected to the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute a strain modification method based on multi-objective optimization according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions for causing a computer to execute a strain modification method based on multi-objective optimization according to any one of claims 1 to 6.

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