Optimization solving method and system for metabolic flux in biomanufacturing process

Abstract

The present invention relates to an optimization solving method and system for metabolic flux in a biomanufacturing process. The method comprises: constructing a flux balance equation of a microbial fermentation process; taking metabolic flux as a control variable to be optimized, on the basis of the flux balance equation, constructing a kinetic equation system, and taking into account a constraint condition and a target function in the fermentation process to construct a dynamic flux balance analysis model based on a dynamic optimization problem; on the basis of a control time domain of a production cycle in the fermentation process, constructing a new time domain, and reconstructing the metabolic flux in the new time domain; parameterizing the reconstructed metabolic flux by means of a piecewise constant approximation strategy; on the basis of the parameterized metabolic flux, converting the dynamic flux balance analysis model from the dynamic optimization problem into a static optimization problem; and solving the optimal metabolic flux of the dynamic flux balance analysis model based on the static optimization problem. The present invention effectively improves the product quality by solving the optimal metabolic flux in the microbial fermentation process.

Description

A metabolic flux optimization solution method and system for biomanufacturing processes Technical Field

[0001] The present invention relates to the field of industrial bioprocess control optimization and application technology, and in particular to a metabolic flux optimization solution method and system for a biomanufacturing process. Background Art

[0002] With global economic development and social progress, small-batch, high-profit, and high-variety production methods, such as industrial microbiology, are gaining popularity to meet people's growing needs. Biomanufacturing processes, typically conducted in bioreactors, can achieve cost-effective products within a defined timeframe through batch production. For example, in the production of exogenous secreted proteins from the commonly used Escherichia coli strain, fermentation is typically used to produce these proteins. However, the fermentation process involves microbial cell growth and metabolism, with complex biochemical reaction mechanisms. Product quality and yield are largely determined by regulatory control conditions. The goal of these controls is to reduce costs and increase returns.

[0003] Adjusting fundamental process variables during fermentation, such as temperature, pH, ventilation rate, agitation power, and feed rate, ultimately alters microbial growth and metabolic activity, thereby affecting final product quality. With the continuous advancement of biotechnology, an increasing number of cellular metabolic networks have been established. Using techniques such as dynamic flux balance analysis, it is now possible to establish metabolic models at the cellular level during fermentation.

[0004] The optimization of the metabolic activity of microbial cells aims to obtain the optimal trajectory of metabolic flux that changes dynamically over time. Mathematically, this can be expressed as an optimization problem. However, there are some problems with traditional solutions. Existing technologies can usually only ensure that the process state meets the constraints at each sampling time point, but cannot guarantee the continuity of the constraints between sampling points. This is particularly important in microbial fermentation processes, because the complexity of the reaction mechanism requires that the constraints on metabolic flux are met throughout the process. Since existing technologies only focus on the state and constraints at discrete time points, this may cause the process to violate the constraints or even malfunction between sampling points. This discontinuity may lead to instability of the metabolic flux, affecting the growth and metabolic activity of microbial cells.

[0005] Summary of the Invention

[0006] Therefore, the technical problem to be solved by the present invention is to overcome the problem of instability of the microbial fermentation process optimization method in the prior art.

[0007] To solve the above technical problems, the present invention provides a method for optimizing metabolic flux in a biomanufacturing process, comprising:

[0008] Step S1: constructing a flux balance equation for the microbial fermentation process, wherein the flux balance equation is used to describe the relationship between each metabolite and the metabolic flux;

[0009] Step S2: taking the metabolic flux as the control variable to be optimized, and constructing a kinetic equation system according to the flux balance equation, and then constructing a dynamic flux balance analysis model based on the dynamic optimization problem in combination with the constraints and objective function in the fermentation process;

[0010] Step S3: constructing a new time domain based on the control time domain of the fermentation process production cycle, and reconstructing the metabolic flux in the new time domain;

[0011] Step S4: parameterizing the reconstructed metabolic fluxes using a piecewise constant approximation strategy;

[0012] Step S5: converting the dynamic flux balance analysis model from a dynamic optimization problem to a static optimization problem according to the parameterized metabolic flux;

[0013] Step S6: solving the optimal metabolic flux of the dynamic flux balance analysis model based on the static optimization problem.

[0014] In one embodiment of the present invention, the flux balance equation of the microbial fermentation process is constructed in step S1, and the formula is:

[0015] where Z(t), X(t), and v(t) are the metabolite concentration vector, biomass concentration, and metabolic flux per gram of biomass at time t, respectively; S is the stoichiometric matrix of the metabolic network; the growth rate μ(t) is the flux-weighted sum of all chemical reactions that synthesize the growth precursors; and ω i is the amount of growth precursor required per gram of biomass, v i (t) is the flux of the i-th chemical reaction at time t, n is the number of chemical reactions, is the differential with respect to time t.

[0016] In one embodiment of the present invention, in step S2, the metabolic flux is used as the control variable to be optimized, and a kinetic equation system is constructed according to the flux balance equation. Then, a dynamic flux balance analysis model based on a dynamic optimization problem is constructed in combination with the constraints and objective function in the fermentation process. The method includes:

[0017] The state vector of the fermentation process is represented as x(t) = [Z(t), X(t)] T , combined with the flux balance equation to construct a kinetic equation system, the formula is:

[0018] Among them, the metabolic flux v(t) is used as the control variable to be optimized, and the initial value of the state vector x(t0) = x0;

[0019] Taking the engineering and empirical requirements of fermentation production as constraints, the formula is: g(x(t),v(t),t)=0 h(x(t),v(t),t)≥0

[0020] Among them, g(·) is the equality constraint function, h(·) is the inequality constraint function, which gives the upper and lower limit constraints on the metabolic flux v(t). The formula is:

[0021] From the initial fermentation time t0=0 to the final fermentation time t f In the whole cycle, let ψ(x(t f ),v(t f ),t f ) is the terminal state term, φ(x(t), v(t), t) is the process state term, and the optimization objective function J is constructed, and the formula is:

[0022] Construct a dynamic flux balance analysis model for dynamic optimization problems: st g(x(t),v(t),t)=0 h(x(t),v(t),t)≥0 x(0)=x0 t0≤t≤t f .

[0023] In one embodiment of the present invention, in step S3, a new time domain is constructed based on the control time domain of the fermentation process production cycle, and the metabolic flux is reconstructed in the new time domain. The method includes:

[0024] The control time domain of the fermentation process production cycle [t0,t f ] is divided into N subintervals [t k-1 ,t k ], k=1,…,N, t0≤t1≤…≤t N-1 ≤t N =t f , then the i-th component v of the metabolic flux v(t) at time t is i (t) is represented by the combination of its values ​​in each subinterval:

[0025] in, Indicates v i (t) in the subinterval [t k-1 ,t k ], δ k (t) is the unit switching function.

[0026] In one embodiment of the present invention, the step S4 uses a piecewise constant approximation strategy to parameterize the reconstructed metabolic flux, and the method includes:

[0027] Will By controlling the parameters Approximate description, the metabolic flux v i (t) By controlling the parameters The linear combination of is expressed as:

[0028] in, For the subinterval [t k-1 ,t k ] is the control parameter to be optimized in the control time domain [t0,t f The trajectory on ] is determined by ψ and is denoted as v(t|ψ).

[0029] In one embodiment of the present invention, in step S5, the dynamic flux balance analysis model is converted from a dynamic optimization problem to a static optimization problem according to the parameterized metabolic flux, and the method includes:

[0030] The state variable x(t) is determined by the metabolic flux v(t) through Determined, the parameterized x(t) is determined by ψ and expressed as x(t|ψ), and the dynamic flux balance analysis model is transformed from a dynamic optimization problem to a static optimization problem for determining the control parameter ψ: st g(x(t|ψ),v(t|ψ),t)=0 h(x(t|ψ),v(t|ψ),t)≥0 x(0)=x0 t0≤t≤t f .

[0031] In one embodiment of the present invention, the method for solving the optimal metabolic flux of the dynamic flux balance analysis model based on the static optimization problem in step S6 includes: solving the control parameter ψ of the dynamic flux balance analysis model based on the static optimization problem, and substituting the solved control parameter ψ into the formula To find the optimal metabolic flux.

[0032] To solve the above technical problems, the present invention provides a metabolic flux optimization solution system for a biomanufacturing process, comprising:

[0033] The first building block is used to construct a flux balance equation for a microbial fermentation process, wherein the flux balance equation is used to describe the relationship between each metabolite and the metabolic flux;

[0034] The second construction module: taking the metabolic flux as the control variable to be optimized, and constructing a kinetic equation system based on the flux balance equation, and then constructing a dynamic flux balance analysis model based on the dynamic optimization problem in combination with the constraints and objective function in the fermentation process;

[0035] Reconstruction module: used for constructing a new time domain based on the control time domain of the fermentation process production cycle, and reconstructing the metabolic flux in the new time domain;

[0036] Parameterization module: used to parameterize the reconstructed metabolic flux through piecewise constant approximation strategy;

[0037] A conversion module is used to convert the dynamic flux balance analysis model from a dynamic optimization problem to a static optimization problem according to the parameterized metabolic flux;

[0038] Solving module: used to solve the optimal metabolic flux of the dynamic flux balance analysis model based on static optimization problems.

[0039] In order to solve the above technical problems, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the metabolic flux optimization solution method for the above-mentioned biomanufacturing process are implemented.

[0040] To solve the above technical problems, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the metabolic flux optimization solution method for the above-mentioned biomanufacturing process are implemented.

[0041] The above technical solution of the present invention has the following advantages over the prior art:

[0042] Dynamic optimization is a low-investment, high-return technology in industrial production. Fermentation process optimization has significant economic benefits in improving product yield and quality, reducing costs, and more. This paper, targeting fermentation processes with unknown mechanistic models, establishes a dynamic flux balance analysis model at the microbial cell level based on cellular metabolic network information and engineering experience. Using a method based on metabolic flux parameterization, it provides an optimization method for the metabolic flux regulation trajectory of biomanufacturing processes.

[0043] The present invention solves the optimal metabolic flux of the microbial fermentation process, which represents the optimal reaction rate of the fermentation process, and obtains the optimal fermentation process of the microorganism by adjusting the reaction rate;

[0044] The present invention verifies the effectiveness and superiority of the method of the present invention through optimization research on the Escherichia coli fermentation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0046] FIG1 is a flow chart of the method of the present invention;

[0047] FIG2 is a schematic diagram of a specific metabolic flux optimization process in an embodiment of the present invention;

[0048] 3 is a diagram of the optimal metabolic flux control trajectory when the number of time domain sub-interval segments N is 10, 20, and 50, respectively, during the fermentation process of E. coli according to an embodiment of the present invention;

[0049] 4 is a graph showing biomass concentration changes when the number of time domain sub-interval segments N is 10, 20, and 50, respectively, during the control of the E. coli fermentation process according to an embodiment of the present invention;

[0050] 5 is a graph showing changes in substrate glucose concentration when the number of segmentation N of the time domain subintervals in the control process of E. coli fermentation is 10, 20, and 50, respectively, according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention.

[0052] Example 1

[0053] 1 , the present invention relates to a method for optimizing metabolic flux in a biomanufacturing process, comprising:

[0054] Step S1: constructing a flux balance equation for the microbial fermentation process, wherein the flux balance equation is used to describe the relationship between each metabolite and the metabolic flux;

[0055] Step S2: taking the metabolic flux as the control variable to be optimized, and constructing a kinetic equation system according to the flux balance equation, and then constructing a dynamic flux balance analysis model based on the dynamic optimization problem in combination with the constraints and objective function in the fermentation process;

[0056] Step S3: constructing a new time domain based on the control time domain of the fermentation process production cycle, and reconstructing the metabolic flux in the new time domain;

[0057] Step S4: parameterizing the reconstructed metabolic fluxes using a piecewise constant approximation strategy;

[0058] Step S5: converting the dynamic flux balance analysis model from a dynamic optimization problem to a static optimization problem according to the parameterized metabolic flux;

[0059] Step S6: solving the optimal metabolic flux of the dynamic flux balance analysis model based on the static optimization problem.

[0060] For fermentation processes with unknown mechanism models, the present invention establishes a dynamic flux balance analysis model at the microbial cell level based on cellular metabolic network information and engineering experience knowledge. Using a method based on metabolic flux parameterization, an optimization method for the metabolic flux regulation trajectory of the biomanufacturing process is given.

[0061] The following is a detailed introduction to this embodiment:

[0062] Step S1: Establish the flux balance equation:

[0063] The microbial fermentation process is a metabolic network consisting of m metabolites and n chemical reactions. According to the mass conservation law of each metabolite, the flux balance equation is as follows:

[0064] where Z(t), X(t), and v(t) are the metabolite concentration vector, biomass concentration, and metabolic flux per gram of biomass at time t, respectively; S is the stoichiometric matrix of the metabolic network; the growth rate μ(t) is the flux-weighted sum of all chemical reactions that synthesize the growth precursors; and ω i is the amount of growth precursor required per gram of biomass, v i (t) is the flux of the i-th chemical reaction at time t, and n is the number of chemical reactions.

[0065] Step S2: Constructing a dynamic flux balance analysis model:

[0066] The state vector of the fermentation process is expressed as: x(t) = [Z(t), X(t)] T (This is equivalent to making the state vector x(t) continuous.) Combining equations (1) and (2), the dynamic equation system can be described by the following state equation:

[0067] The metabolic flux v(t) is the control variable to be optimized, and the initial value of the state vector x(t0) = x0. In addition to the kinetic equation system, some engineering and empirical requirements in actual fermentation production must also be taken as constraints: g(x(t),v(t),t) = 0 (4) h(x(t),v(t),t) ≥ 0 (5)

[0068] Among them, is the equality constraint function, is the inequality constraint function, and gives the upper and lower limit constraints on the metabolic flux:

[0069] In the entire fermentation cycle from the initial moment to the terminal moment, is set as the terminal state term and is the process state term, and the following optimization objective function J is constructed:

[0070] Depending on the fermentation products and production requirements, the optimization objective function can be selected to maximize the yield of metabolites at the end of fermentation, maximize the substrate conversion rate during the fermentation process, or optimize cell growth. Therefore, the dynamic flux balance analysis model can be expressed as a general dynamic optimization problem: st g(x(t),v(t),t)=0 h(x(t),v(t),t)≥0 x(0)=x0 t0≤t≤t f

[0071] The purpose of the dynamic flux balance analysis model based on dynamic optimization problem is to obtain the optimal metabolic flux v at each moment by optimizing * (t) Make the objective function J at the terminal time t f The following steps S3 to S6 specifically explain how to solve the optimal metabolic flux v * (t).

[0072] Step S3: Metabolic flux is reconstructed in the new time domain:

[0073] The control time domain of the fermentation process production cycle [t0,t f ] is divided into N subintervals [t k-1 ,t k ], k=1,…,N, that is, t0≤t1≤…≤t N-1 ≤t N =t f The i-th component v of the metabolic flux at time t is i (t) can be represented by the combination of its values ​​in each subinterval:

[0074] in, Indicates v i (t) in the subinterval [t k-1 ,t k ], δ k (t) is the unit switching function.

[0075] Step S4: Parameterize metabolic fluxes:

[0076] The metabolic fluxes were parameterized using a piecewise constant approximation strategy, Can be controlled by parameters Approximately described, the metabolic flux can be expressed by a linear combination of:

[0077] in, For the subinterval [t k-1 ,t k ] is the control parameter to be optimized in the control time domain [t0,t f The trajectory on ] is determined by ψ and is denoted as v(t|ψ).

[0078] The above steps S3 and S4 are equivalent to discretizing the metabolic flux.

[0079] Step S5: Original optimization problem transformation:

[0080] The state variable x(t) can be expressed by the metabolic flux v(t) through the formula If ψ is determined, the parameterized x(t) is determined by ψ and expressed as x(t|ψ). Therefore, the original dynamic optimization problem of solving the optimal control trajectory can be transformed into a finite-dimensional static optimization problem of determining the control parameters: st g(x(t|ψ),v(t|ψ),t)=0 h(x(t|ψ),v(t|ψ),t)≥0 x(0)=x0 t0≤t≤t f

[0081] Step S6: Solving the optimization objective:

[0082] (a) Set the iteration counter j = 0 and set the initial value of the control variable parameter ψ of the metabolic flux trajectory (0) , optimization target termination condition ρ;

[0083] (b) Change ψ (j) Substitute into formula (11) to determine the metabolic flux v(t);

[0084] (c) Substitute the metabolic flux v(t) into the state equation The equation is solved by computer numerical method to obtain the state variable x(t|ψ (0) ), the Runge-Kutta method is generally used, but other numerical solution methods can also be used;

[0085] (d) According to x(t|ψ (0) ) Calculate the values ​​of the objective function and constraint function, and determine whether the termination condition is met. If so, the optimal metabolic flux control trajectory v is obtained. * (t); If the termination condition is not met, the next step is to use the nonlinear programming method to find the optimal solution;

[0086] (e) Solve the objective function (12) by using the penalty function class interior point method and exterior point method to transform the constrained problem into an unconstrained problem, as well as the quadratic programming (QP) method and the genetic algorithm class of intelligent optimization;

[0087] (f) Set the iteration counter j = j + 1 to obtain the new control variable parameter ψ (j) , go to step (b) of step S6.

[0088] Step S6 can not only find the optimal control parameters Based on the optimal Find

[0089] The following is a detailed introduction to this embodiment in conjunction with the fermentation process of an actual microorganism (Escherichia coli):

[0090] First, the stoichiometric matrix is ​​determined based on the microbial metabolic network, and the flux balance equation (1) is established according to the law of conservation of mass to describe the relationship between each metabolite and metabolic flux.

[0091] The metabolic flux v(t) is taken as the control variable to be optimized, and a kinetic equation system is constructed based on the above relationship. Taking some engineering and empirical requirements in actual fermentation production as constraints, a dynamic flux balance analysis model (8) is constructed, from the initial time t0=0 of fermentation to the end time t f Throughout the entire cycle, we search for the optimal metabolic flux regulation trajectory v * (t), maximize the objective function J.

[0092] The control time domain of the fermentation process production cycle [t0,t f ] is divided into subintervals [t k-1 ,t k ], k=1,…,N, the i-th component v of metabolic flux at time t i (t) can be represented by a combination of its values ​​in each subinterval.

[0093] The metabolic flux v is parameterized using a piecewise constant approximation strategy. i (t) can be controlled by the variable parameter As expressed in formula (11).

[0094] After parameterization, x(t) is determined by ψ and expressed as x(t|ψ), which can transform the original dynamic optimization problem of solving the optimal control trajectory into a finite-dimensional static optimization problem of determining the control parameter ψ (12).

[0095] Set the iteration counter j = 0 and set the initial value of the control variable parameter ψ of the metabolic flux trajectory 0, optimization target termination condition ρ.

[0096] ψ j Substitute into equation (11) to determine the metabolic flux v(t) and bring it into the state equation The Runge-Kutta method is used to solve the equation and obtain the state variable x(t|ψ 0 ).

[0097] According to x(t|ψ 0 ) Calculate the values ​​of the objective function and constraint function, and determine whether the termination condition is met. If so, the optimal metabolic flux control trajectory v is obtained. * (t).

[0098] If the termination condition is not met, the nonlinear programming method is used to find the optimal algorithm to solve the objective function (12), and the iteration counter j = j + 1 is set to obtain the new control variable parameter ψ j , iteratively solve the state equation And calculate the objective function (12).

[0099] Implementation effect analysis:

[0100] The method of the present invention was used to study the fermentation process of E. coli. The metabolic network of E. coli fermentation is known, including 54 metabolites and 85 metabolic reactions. Based on the knowledge related to biomass synthesis and E. coli physiology, four pathways were selected to construct a simplified E. coli metabolic network, including four fluxes v = (v1v2v3v4) T with three metabolite substrates glucose, acetate and oxygen, as well as biomass.

[0101] The control time domain [0,10] of the fermentation production cycle is divided into N = 10, 20, and 50 subintervals for research. The initial value of the state variable is set to x(0) = [10.8 0.4 0.21 0.001] T , given the boundary constraint v(t) v=[0 0 0 0] T and The nonlinear programming algorithm uses the interior point method to find the optimal solution, and the optimization target termination condition is ρ = 10 -8 , the Runge-Kutta method is used to solve the state equation.

[0102] Figure 3 shows the optimal metabolic flux control trajectory diagram obtained under the method of the present invention; it is obvious from the biomass concentration change curve in Figure 4 and the substrate glucose concentration change curve in Figure 5 that the E. coli cells are in the growth adaptation period from 0 to 3 hours, when glucose is almost not consumed and the biomass concentration is almost 0; after 3 hours, the cells are in the growth period, the glucose concentration is rapidly consumed, and the biomass concentration increases rapidly. In order to achieve the production goal of maximizing the biomass concentration, it is necessary to extend the growth period of the cells and slow down their entry into the decline period. It can be seen from Figure 4 that after the metabolic flux trajectory is optimized, the target substance biomass concentration reaches more than 0.95, and as the number of segments N increases, the terminal biomass concentration increases. Compared with the simulation results in some existing literature, the biomass concentration is between 0.7 and 0.8. It is not difficult to find that the method of the present invention has better effects.

[0103] Example 2

[0104] This embodiment provides a metabolic flux optimization solution system for a biomanufacturing process, comprising:

[0105] The first building block is used to construct a flux balance equation for a microbial fermentation process, wherein the flux balance equation is used to describe the relationship between each metabolite and the metabolic flux;

[0106] The second construction module: taking the metabolic flux as the control variable to be optimized, and constructing a kinetic equation system based on the flux balance equation, and then constructing a dynamic flux balance analysis model based on the dynamic optimization problem in combination with the constraints and objective function in the fermentation process;

[0107] Reconstruction module: used for constructing a new time domain based on the control time domain of the fermentation process production cycle, and reconstructing the metabolic flux in the new time domain;

[0108] Parameterization module: used to parameterize the reconstructed metabolic flux through piecewise constant approximation strategy;

[0109] A conversion module is used to convert the dynamic flux balance analysis model from a dynamic optimization problem to a static optimization problem according to the parameterized metabolic flux;

[0110] Solving module: used to solve the optimal metabolic flux of the dynamic flux balance analysis model based on static optimization problems.

[0111] Example 3

[0112] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for optimizing the metabolic flux of the biomanufacturing process described in Example 1 are implemented.

[0113] Example 4

[0114] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for optimizing the metabolic flux of the biomanufacturing process described in the first embodiment are implemented.

[0115] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0116] The present application is described with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to the embodiment of the present application. It should be understood that each flow process and / or box in the flow chart and / or block diagram and the combination of the flow process and / or box in the flow chart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processing machine or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one flow chart flow or multiple flows and / or one box or multiple boxes of the block diagram.

[0117] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0119] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0120] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for optimizing metabolic flux in a biomanufacturing process, characterized in that: include: Step S1: constructing a flux balance equation for a microbial fermentation process, wherein the flux balance equation is used to describe the relationship between each metabolite and the metabolic flux; Step S2: taking the metabolic flux as the control variable to be optimized, and constructing a kinetic equation system according to the flux balance equation, and then constructing a dynamic flux balance analysis model based on a dynamic optimization problem in combination with the constraint conditions and the objective function in the fermentation process; Step S3: constructing a new time domain based on the control time domain of the fermentation process production cycle, and reconstructing the metabolic flux in the new time domain; Step S4: parameterizing the reconstructed metabolic fluxes through a piecewise constant approximation strategy; Step S5: according to the parameterized metabolic flux, converting the dynamic flux balance analysis model from a dynamic optimization problem to a static optimization problem; Step S6: solving the optimal metabolic flux of the dynamic flux balance analysis model based on the static optimization problem.

2. The method for optimizing metabolic flux in a biomanufacturing process according to claim 1, characterized in that: In step S1, a flux balance equation of the microbial fermentation process is constructed, and the formula is: where Z(t), X(t) and v(t) are the metabolite concentration vector, biomass concentration and metabolic flux vector per gram of biomass at time t, respectively; S is the stoichiometric matrix of the metabolic network; the growth rate μ(t) is the flux-weighted sum of all chemical reactions that synthesize the growth precursors; ω i is the amount of growth precursor required per gram of biomass, v i (t) is the flux of the ith chemical reaction at time t, and n is the number of chemical reactions.

3. The method for optimizing metabolic flux in a biomanufacturing process according to claim 2, characterized in that: In step S2, the metabolic flux is used as the control variable to be optimized, and a kinetic equation system is constructed according to the flux balance equation, and then a dynamic flux balance analysis model based on a dynamic optimization problem is constructed in combination with the constraints and the objective function in the fermentation process. The method includes: The state vector of the fermentation process is represented as x(t) = [Z(t), X(t)] T , combined with the flux balance equation to construct a kinetic equation system, the formula is: Among them, the metabolic flux v(t) is used as the control variable to be optimized, and the initial value of the state vector x(t0) = x0; Taking the engineering and empirical requirements in actual fermentation production as constraints, the formula is: g(x(t),v(t),t)=0 h(x(t),v(t),t)≥0 Among them, g(·) is the equality constraint function, h(·) is the inequality constraint function, and the upper and lower limit constraints are given to the metabolic flux v(t). The formula is: From the initial fermentation time t0 = 0 to the final fermentation time t f In the whole cycle, let ψ(x(t f ),v(t f ),t f ) is the terminal state term, φ(x(t), v(t), t) is the process state term, and the optimization objective function J is constructed, the formula is: Construct a dynamic flux balance analysis model for dynamic optimization problems: s.t. g(x(t),v(t),t)=0 h(x(t),v(t),t)≥0 x(0)=x0 t0≤t≤t f 。 4. The method for optimizing metabolic flux in a biomanufacturing process according to claim 3, characterized in that: In step S3, a new time domain is constructed based on the control time domain of the fermentation process production cycle, and the metabolic flux is reconstructed in the new time domain. The method includes: The control time domain of the fermentation process production cycle [t0,t f ] is divided into N subintervals [t k-1 ,t k ], k=1,…,N, t0≤t1≤…≤t N-1 ≤t N =t f , then the metabolic flux v(t) at time t is the i-th component v i (t) is represented by the combination of its values ​​in each subinterval: in, Indicates v i (t) in the subinterval [t k-1 ,t k ], δ k (t) is the unit switching function.

5. The method for optimizing metabolic flux in a biomanufacturing process according to claim 4, characterized in that: In step S4, the reconstructed metabolic flux is parameterized by a piecewise constant approximation strategy, and the method includes: Will By controlling the parameters Approximately described, the metabolic flux v i (t) By controlling the parameters The linear combination of is expressed as: in, For the subinterval [t k-1 ,t k ] is the control parameter to be optimized in the control time domain [t0,t f The trajectory on ] is determined by ψ and is denoted as v(t|ψ).

6. The method for optimizing metabolic flux in a biomanufacturing process according to claim 5, characterized in that: In step S5, according to the parameterized metabolic flux, the dynamic flux balance analysis model is converted from a dynamic optimization problem to a static optimization problem, and the method includes: The state variable x(t) is determined by the metabolic flux v(t) through Determined, the parameterized x(t) is determined by ψ and expressed as x(t|ψ), and the dynamic flux balance analysis model is transformed from a dynamic optimization problem to a static optimization problem for determining the control parameter ψ: s.t. g(x(t|ψ),v(t|ψ),t)=0 h(x(t|ψ),v(t|ψ),t)≥0 x(0)=x0 t0≤t≤t f 。 7. The method for optimizing metabolic flux in a biomanufacturing process according to claim 6, characterized in that: In step S6, the optimal metabolic flux of the dynamic flux balance analysis model based on the static optimization problem is solved, and the method includes: solving the control parameter ψ of the dynamic flux balance analysis model based on the static optimization problem, and substituting the solved control parameter ψ into the formula To find the optimal metabolic flux.

8. A metabolic flux optimization solution system for a biomanufacturing process, characterized by: include: The first building block is used to construct a flux balance equation for a microbial fermentation process, wherein the flux balance equation is used to describe the relationship between each metabolite and the metabolic flux; The second construction module: taking the metabolic flux as the control variable to be optimized, and constructing a kinetic equation system according to the flux balance equation, and then constructing a dynamic flux balance analysis model based on a dynamic optimization problem in combination with the constraints and objective function in the fermentation process; Reconstruction module: used to construct a new time domain based on the control time domain of the fermentation process production cycle, and reconstruct the metabolic flux in the new time domain; Parameterization module: used to parameterize the reconstructed metabolic fluxes through piecewise constant approximation strategy; A conversion module: used for converting the dynamic flux balance analysis model from a dynamic optimization problem to a static optimization problem according to the parameterized metabolic flux; Solution module: used to solve the optimal metabolic flux of the dynamic flux balance analysis model based on static optimization problems.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for optimizing the metabolic flux of a biomanufacturing process according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the metabolic flux of a biomanufacturing process according to any one of claims 1 to 7 are implemented.

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