A method, system and computer storage medium for optimizing regulation of gene expression processes

Abstract

The present application relates to the technical field of gene expression process regulation, in particular to a gene expression process optimization regulation method, system and computer storage medium, the method comprising: constructing a biological regulation circuit of a regulated gene expression process to obtain a mathematical relationship of the gene expression process; designing a controller model for capturing dynamic changes in the gene expression process in a space with a curvature not constant as 0; based on the controller model, establishing a matrix equation for solving the regulation input, and according to the matrix equation, obtaining the regulation input that makes the expression product GFP concentration of the regulated gene stable; and controlling the expression process of the regulated gene through the regulation input, so that the gene expression product concentration can be kept stable under the condition of ribosome quantity fluctuation in the cell. According to the difference between the ribosome disturbance degree and the GFP product concentration under different situations, the required regulation input is calculated, and the efficiency, universality and stability of the gene expression process are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gene expression process regulation, in particular to a gene expression process optimization regulation method, system and computer storage medium. BACKGROUND

[0002] In the field of bioengineering technology, the design of regulation input quantity of gene expression regulation system has always been a key and complex link. This process usually relies on extensive experimental verification and data analysis, aiming to accurately select control parameters that can optimize the production performance of the system from a large amount of experimental data. The core goal of this strategy is to ensure that the expression level of the regulated gene expression product, green fluorescent protein (GFP), can remain stable when facing the disturbance of dynamic changes in the number of ribosomes in cells, thereby ensuring the consistency and reliability of the biological manufacturing process.

[0003] However, the traditional control quantity selection method relying on experimental experience has significant defects. This method not only requires a large number of repetitive experiments, resulting in low research and development efficiency and high cost, but also the designed gene expression regulation system often lacks sufficient flexibility and wide applicability. Specifically, when the cell culture environment changes, such as temperature fluctuations or medium component adjustments, the control parameters set based on historical experience may not be able to quickly adapt to these changes, leading to system response lag and thus adversely affecting the final yield and product quality.

[0004] Taking the dissolved oxygen (DO) control in the cell culture process as an example, dissolved oxygen is a key physiological parameter that affects cell growth rate and metabolic product yield. Traditionally, operators will set an appropriate dissolved oxygen concentration range based on past experimental data and experience to ensure the best proliferation and metabolic efficiency of cells. However, when the culture conditions change, this experience-based control strategy may not be able to adjust the dissolved oxygen concentration in a timely and accurate manner, causing the system to be unable to effectively respond to environmental changes, thereby affecting the growth and metabolism of cells and ultimately reducing yield and product quality. SUMMARY

[0005] To solve the above technical problems, the present application provides a gene expression process optimization regulation method, system and computer storage medium, the regulation method comprising the following steps:

[0006] S1: constructing a biological regulation circuit of the regulated gene expression process;

[0007] S2: obtaining a mathematical relationship of the gene expression process based on the biological regulation circuit;

[0008] S3: based on the mathematical relationship, designing a controller model for capturing dynamic changes in the gene expression process in a space where the curvature is not always 0.

[0009] S4: based on the controller model, a matrix equation for solving the regulatory input is established, and a regulatory value is obtained according to the matrix equation, so that the expression product GFP concentration of the regulated gene satisfies the stable condition, and the regulatory input is updated by using the regulatory value;

[0010] S5: the expression process of the regulated gene is controlled by the regulatory input updated each time, so that the concentration of the gene expression product can be kept stable in the case of ribosome quantity fluctuation in the cell.

[0011] In an embodiment of the present application, in S1, the method for constructing the biological regulatory circuit of the regulated gene expression process is as follows:

[0012] The ECF32 gene sequence is embedded upstream of the regulated gene, the regulated gene expresses the GFP protein, the ECF32 gene sequence expresses the ECF32 protein, the ECF32 protein binds with the promoter PECF32 to form a transcription complex, transcription is obtained, and the sRNA is complementary to the target mRNA obtained by transcription of the regulated gene to obtain double-stranded RNA.

[0013] In an embodiment of the present application, in S2, the method for obtaining the mathematical relationship of the gene expression process is as follows:

[0014]

[0015] Wherein, x(t) = [m; s; y] is the system state change in the gene expression process, m represents the concentration of the product mRNA in the transcription process in the gene expression process, s represents the concentration of the product sRNA in the gene expression process, and y represents the concentration of the gene expression product protein; u(t) is the regulatory input to be calculated; B(x(t), t) is the control coefficient matrix; and f[x(t), t] is a nonlinear function.

[0016] In an embodiment of the present application, in S3, in the space where the curvature is not always 0, the method for designing the controller model for capturing the dynamic change in the gene expression process comprises:

[0017] The virtual distance is used to solve the distance between the concentration change trajectories of the expression product GFP over time;

[0018] And according to the rate of change of the distance, a judgment condition for reaching a stable state in the GFP gene expression process is constructed.

[0019] In an embodiment of the present application, the method steps for solving the distance V(δ x ,t) between the concentration change trajectories of the expression product GFP over time by using the virtual distance are as follows:

[0020] The differential form of the mathematical equation is expressed as:

[0021]

[0022] wherein δ x represents a virtual distance, is a derivative of δ x , represents a change rate of the virtual distance δ x updated over time; A(x(t), t) represents a state change matrix of the virtual distance δ x updated over time;

[0023] Let the control input u(t) = K K x(t), wherein K K represents a control feedback gain to be designed, i.e., a ratio of the translation rate of the GFP protein to the ECF32 protein, and the differential form of the control input is expressed as:

[0024] δ u = K K δ x (3)

[0025] The closed-loop expression of the GFP gene expression can be obtained by bringing (3) into (2):

[0026]

[0027] The control aggregation matrix M(t) is introduced in the space where the curvature is not always 0, and M(t) is a uniformly positive definite symmetric matrix, so the distance V(δ x , t) between the expression product GFP concentration change trajectories under different available ribosome quantities is expressed as:

[0028] V(δ x , t) = δ x T M(t) δ x (5).

[0029] In an embodiment of the present application, the change rate of the distance V(δ x , t) is as follows:

[0030]

[0031] wherein, represents an update rate of the control aggregation matrix M(t) over time.

[0032] In an embodiment of the present application, according to the change rate of the distance V(δ x , t), the judgment condition for reaching a steady state in the GFP gene expression process is:

[0033]

[0034] In one embodiment of the present application, the method for obtaining the regulation value that makes the expression product GFP concentration of the regulated gene satisfy the stability condition is as follows:

[0035] S41: Constructing a first set of positive definite matrices and a second set of positive definite matrices, wherein the expression of the first set of positive definite matrices is as follows:

[0036] (F+G) T (F+G)=F T F+G T F+F T G+G T G (8)

[0037] and the expression of the second set of positive definite matrices is as follows:

[0038] K K T QK K =K K T T T TK K (9)

[0039] S42: The term containing K K in the judgment condition in formula (7) needs to satisfy the following condition:

[0040] K K T B T M+MBK K <0 (10)

[0041] S43: Transforming the inequality (10) to include:

[0042] Let the last term K K T T T TK K in formula (9) be equal to the last term G T G in formula (8), and solve the expression of G:

[0043] G=TK K (11)

[0044] From formula (11), G T F=K K T T T F, let K K T T T F=K KT B T M, the expression of F is solved:

[0045] F = (T T ) -1 B T M (12)

[0046] From equation (12), we can get:

[0047] F T F = MB(T) -1 (T T ) -1 B T M = MBQ -1 B T M (13)

[0048] Let F + G = 0, TK K +(T T ) -1 B T M = 0, the calculation expression of control feedback gain K K is obtained:

[0049] K K = -(T T T) -1 B T M = -Q -1 B T M (14)

[0050] Therefore, based on inequality (10), it is obtained after arrangement:

[0051] K K T B T M + MBK K = (F + G) T (F + G) - 2MBQ -1 B T M < 0 (15)

[0052] Let P = 2Q -1 , P is a positive constant parameter, according to equation (14), the calculation expression of control feedback gain K K is obtained:

[0053] K K = -0.5PB T M (16)

[0054] S44: Substitute equation (15) into equation (7), the following linear matrix inequality is obtained:

[0055]

[0056] Multiply M to each term on the left side of formula (17) -1 We can get:

[0057]

[0058] Let W=M -1 , Simplify formula (17) by taking the derivative of W, and we get:

[0059]

[0060] Then the final control feedback gain K K is:

[0061] K K =-0.5PB T W -1 (20)

[0062] By iteratively solving formula (19) and formula (20), the aggregation matrix M and the constant parameter P are obtained, and the control feedback gain K K , that is, the control value, is obtained by substituting the aggregation matrix M and the constant parameter P obtained in each iteration into formula (16).

[0063] Based on the same inventive concept, the application also provides a gene expression process optimization control system for implementing the steps of the gene expression process optimization control method, and the gene expression process optimization control system comprises the following modules:

[0064] A control loop construction module is configured to construct a biological control loop of a controlled gene expression process.

[0065] A gene expression process modeling module is configured to obtain a mathematical relationship of the gene expression process based on the biological control loop.

[0066] A controller model design module is configured to design a controller model for capturing dynamic changes in the gene expression process in a space where the curvature is not always 0 based on the mathematical relationship.

[0067] A control input value calculation module is configured to establish a matrix equation for solving a control input based on the controller model, obtain a control value that makes the expression product GFP concentration of the controlled gene meet the stable condition according to the matrix equation, and update the control input using the control value.

[0068] A gene control module is configured to control the expression process of the controlled gene through the control input updated each time, so that the concentration of the gene expression product can be kept stable in the case of ribosome quantity fluctuation in the cell.

[0069] The application further provides a computer storage medium storing a computer software product, wherein the computer software product comprises a plurality of instructions for enabling a computer device to execute the gene expression process optimization regulation method.

[0070] Compared with the prior art, the above technical scheme of the application has the following advantages:

[0071] 1. The application deeply analyzes the dynamic characteristics of the regulated gene expression process, and ingeniously uses mathematical methods and theories to design an optimized feedback control gain for the GFP gene expression process. This innovative scheme breaks through the selection mode of control variables in traditional gene expression regulation, effectively eliminates the excessive dependence on a large number of time-consuming repeated experiments, greatly improves the gene expression efficiency, and effectively promotes the precise achievement of the expected level of gene expression in a shorter time.

[0072] 2. The application has the ability to accurately calculate and determine the required regulation input under different situations according to the differences in ribosome disturbance degree and the real-time changes in the expression product GFP concentration in the gene expression process. This not only significantly expands its application range, enabling it to be widely adapted to various gene expression systems and enhancing its universality, but also greatly enhances the stability of the gene expression process, effectively reduces the risk of expression abnormalities caused by external interference factors or internal system fluctuations, and provides reliable protection for the precise regulation of gene expression. BRIEF DESCRIPTION OF DRAWINGS

[0073] In order to make the content of the application more easily understood, the application will be further described in detail below according to specific embodiments of the application and in conjunction with the accompanying drawings, wherein,

[0074] Figure 1 is a gene expression process schematic diagram;

[0075] Figure 2 is a gene expression process optimization regulation method flowchart provided in a preferred embodiment of the application;

[0076] Figure 3 is a biological regulation circuit schematic diagram of a gene expression process resisting ribosome number disturbance;

[0077] Figure 4 is a protein product GFP, mRNA and sRNA-A concentration change trajectory diagram of a gene expression process under ribosome disturbance;

[0078] Figure 5 is a robustness result schematic diagram of a gene expression process;

[0079] Figure 6is a schematic diagram of a gene expression process optimization regulation system structure provided in a preferred embodiment of the present application;

[0080] Description of the figures: 100, regulation loop construction module; 200, gene expression process modeling module; 300, controller model design module; 400, regulation input value calculation module; 500, gene regulation module. DETAILED DESCRIPTION

[0081] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it.

[0082] Example 1

[0083] Gene expression is a complex and orderly process, mainly covering two key stages of transcription and translation, as shown in the following figure. Figure 1 In the transcription stage, the initial product generated is pre-mRNA, however, this precursor does not have the ability to directly participate in subsequent biological reactions, and must undergo intron splicing and a series of subsequent fine processing steps before it can be successfully converted into mature mRNA. When mature mRNA is produced, it enters the translation stage. At this time, free ribosomes in the cell play an important role, and mature mRNA molecules will combine with these ribosomes, and under the catalysis of ribosomes, the translation process is formally started, and then the nascent polypeptide chain is synthesized. But the nascent polypeptide chain is only a semi-finished product, which needs to be further subjected to post-translational modification, such as glycosylation, phosphorylation and other chemical modification processes, through these modifications, the polypeptide chain can be folded into a protein product with specific biological functions, thus completing the entire gene expression process and realizing the transformation of genetic information carried by genes from nucleic acid sequence to functional protein.

[0084] In-depth analysis of the above gene expression process shows that the available concentration of ribosomes in the cell has a significant impact on the entire gene expression process. In the transcription process, although ribosomes do not directly participate in the chemical reactions of gene transcription, the available concentration of ribosomes will indirectly affect the efficiency of the subsequent translation link, and thus affect the rate and yield of the entire gene expression. And in the translation process, as the key participant, the available concentration of ribosomes directly determines the number of ribosomes that can bind to mature mRNA and start translation, thereby affecting the generation rate and quantity of nascent polypeptide chains. Based on this close internal relationship, by adjusting the available concentration of ribosomes in the cell, precise regulation of the level of gene expression can be achieved.

[0085] Referring to Figure 2As shown in the figure, in order to resist the disturbance from the number of ribosomes, the present application provides a method for optimizing and regulating the gene expression process, which comprises the following steps:

[0086] S1: constructing a biological regulation circuit of the regulated gene expression process;

[0087] S2: obtaining a mathematical relationship of the gene expression process based on the biological regulation circuit;

[0088] S3: based on the mathematical relationship, designing a controller model for capturing the dynamic changes in the gene expression process in a space where the curvature is not always 0;

[0089] S4: based on the controller model, establishing a matrix equation for solving the regulation input, obtaining a regulation value that makes the expression product GFP concentration of the regulated gene meet the stable condition according to the matrix equation, and updating the regulation input using the regulation value;

[0090] S5: controlling the expression process of the regulated gene through the regulation input updated each time, so that the concentration of the gene expression product can be kept stable in the case of ribosome number fluctuation in the cell.

[0091] Further, the process of constructing a biological regulation circuit of the regulated gene expression process comprises the following steps as shown in the figure: Figure 3

[0092] Embedding an ECF32 gene sequence upstream of the regulated gene, the regulated gene expressing a GFP protein, the ECF32 gene sequence expressing an ECF32 protein, the ECF32 protein combining with a promoter PECF32 to form a transcription complex, and transcribing to obtain sRNA, the sRNA being complementary to the target mRNA transcribed from the regulated gene to obtain double-stranded RNA; the double-stranded RNA is easily degraded in the cell. Therefore, the concentration of the transcription product mRNA of the regulated gene can be adjusted, and the method makes the expression level of the gene keep a certain degree of stability in the case of ribosome supply fluctuation in the cell.

[0093] In step S2, according to the above biological regulation circuit and product generation kinetics, the method for establishing the mathematical relationship of the gene expression process is as follows:

[0094]

[0095] ​Wherein, x(t) = [m; s; y] is the system state change in the gene expression process, m represents the concentration of product mRNA in the transcription process of the gene expression process, s represents the concentration of product sRNA in the gene expression process, and y represents the concentration of the gene expression product protein; u(t) is the to-be-calculated regulation input; B(x(t), t) is a control coefficient matrix; and f[x(t), t] is a nonlinear function.

[0096] In order to more accurately describe the close correlation between each intermediate product and the final product in the gene expression process, a controller is designed in a space with non-constant spatial curvature. Such a design can more effectively capture the dynamic change characteristics of the concentrations of mRNA, sRNA-A and product green fluorescent protein (GFP) during gene expression. In this way, the constructed controller can be closer to the real application scenario of the gene expression process, thereby improving its practicability and accuracy.

[0097] Specifically, in step S3, based on the mathematical relationship, in a space with non-constant curvature, a method for designing a controller model for capturing dynamic changes in the gene expression process includes:

[0098] Using a virtual distance δ x Solving the distance V(δ x ,t) between the trajectories of the expression product GFP concentration changing with time t;

[0099] And constructing a judgment condition for reaching a steady state in the GFP gene expression process according to the rate of change of the distance V(δ x ,t).

[0100] Compared with the traditional method of judging the stability of GFP only according to whether the GFP level is constant, the present application evaluates whether GFP can maintain a stable state when facing ribosome quantity fluctuations by measuring the change trend of the distance between the GFP concentration trajectories under different ribosome quantities. Specifically, if the distance between any adjacent GFP concentration trajectories continues to decrease, it indicates that the gene expression level of GFP can gradually reach a stable state regardless of the degree of disturbance of the ribosome quantity.

[0101] When designing a controller in a space with non-constant curvature, the distance between the trajectories of the expression product GFP concentration changing with time t may present as a curve rather than a straight line, which indeed increases the complexity of calculation. However, the present application divides this complex space into multiple small local regions, and the curvature in each local region can be approximately regarded as 0, so that the distance between the GFP concentration trajectories in these small regions can be approximately regarded as a straight line distance.

[0102] To unify the measure of the length of these small spatial distances, the aggregation matrix M is introduced, which is a positive definite symmetric matrix, used to normalize and unify the distances in different local spaces in the calculation process. Through this method, even in the space where the curvature is not always 0, and in the environment where the number of available ribosomes is different, a virtual distance can be used to express the distance between the trajectories of the change of the expression product GFP concentration.

[0103] Further, the method steps for solving the distance V(δ x ,t) between the trajectories of the change of the expression product GFP concentration with time using the virtual distance are as follows:

[0104] The differential form of the mathematical equation is expressed as:

[0105]

[0106] Wherein, δ x represents the virtual distance, is the derivative of δ x , which represents the rate of change of the virtual distance δ x updating over time; A(x(t),t) represents the state change matrix of the virtual distance δ x updating over time;

[0107] Let the control input u(t) = K K x(t), where K K represents the control feedback gain to be designed, that is, the ratio of the translation rate of GFP protein to ECF32 protein, and the accurate differential form of the control input is expressed as:

[0108] δ u = K K δ x (3)

[0109] By substituting (3) into (2), the closed-loop expression of the GFP gene expression can be obtained:

[0110]

[0111] The control aggregation matrix M(t) is introduced in the space where the curvature is not always 0, and M(t) is a uniformly positive definite symmetric matrix, so the distance V(δ x ,t) between the trajectories of the change of the expression product GFP concentration under different available ribosome numbers is expressed as:

[0112] V(δ x ,t) = δ x T M(t) δ x (5).

[0113] In addition, the rate of change of the distance V(δ x ,t) of the expression product GFP is represented as:

[0114]

[0115] wherein, represents the rate of updating the aggregation matrix M(t) over time.

[0116] If it is desired to ensure that the expression process of the GFP gene can maintain stable expression under different degrees of ribosome quantity, that is, it is necessary to ensure that formula (6) is consistently negative, so that under different degrees of disturbance caused by the ribosome quantity, the distance between any two adjacent GFP concentration change trajectories is gradually reduced, and thus even if the gene GFP expression is disturbed to different degrees, it can be aggregated to the same stable expression trajectory.

[0117] Therefore, under any ribosome disturbance size, the rate of change of the distance between the GFP concentration change trajectories of the expression product is used to derive the judgment condition for reaching the stable state in the GFP gene expression process as:

[0118]

[0119] According to the above condition for judging whether the concentration of the expression product GFP of the regulated gene is stable, a matrix equation for solving the regulation input quantity is established, and according to the matrix equation, the regulation value that makes the concentration of the expression product GFP of the regulated gene satisfy the stable condition is obtained, and the method steps are as follows:

[0120] S41: Construct a first group of positive definite matrices and a second group of positive definite matrices, wherein the expression of the first group of positive definite matrices is as follows:

[0121] (F+G) T (F+G)=F T F+G T F+F T G+G T G (8)

[0122] And in order to meet the form constructed, a term K is constructed again: K T QK K wherein the Q matrix is a consistently positive definite matrix; therefore, it can be expressed as: Q=T T T, therefore, the expression of the second group of positive definite matrices is as follows:

[0123] K K T QK K =K K T ​T T TK K (9)

[0124] S42: The term containing K K in the condition of judging in formula (7) needs to satisfy the following condition:

[0125] K K T B T M+MBK K <0 (10)

[0126] S43: Transform the inequality (10) to include:

[0127] Let the last term K K T T T TK K in formula (9) be equal to the last term G T G in formula (8), and solve the expression of G:

[0128] G=TK K (11)

[0129] From formula (11), we can get G T F=K K T T T F, let K K T T T F=K K T B T M, and solve the expression of F:

[0130] F=(T T ) -1 B T M (12)

[0131] From formula (12), we can get:

[0132] F T F=MB(T) -1 (T T ) -1 B T M=MBQ -1 B T M (13)

[0133] Let F+G=0, TK K +(T T ) -1 B T M=0, and get the calculation expression of control feedback gain K K :

[0134] K K =-(T T T) -1 B T M=-Q -1 B T M (14)

[0135] Therefore, based on inequality (10), we have:

[0136] K K T B T M+MBK K =(F+G) T (F+G)-F T F-K K T QK K

[0137] =(F+G) T (F+G)-2MBQ -1 B T M<0 (15)

[0138] Let P = 2Q -1 , P is a positive constant parameter, according to formula (14), the calculation expression of control feedback gain K K is obtained:

[0139] K K =-0.5PB T M (16)

[0140] S44: Substitute formula (15) into formula (7) to obtain the following linear matrix inequality:

[0141]

[0142] Multiply each term in formula (17) by M -1 on both sides to obtain:

[0143]

[0144] Let W = M -1 , the derivative of W, formula (17) is simplified as:

[0145]

[0146] The final control feedback gain K K is:

[0147] K K =-0.5PB T W -1(20)

[0148] It can be found that inequality (19) is constantly changing with time and the concentration of GFP, so the obtained aggregation matrix M and constant parameter P are also constantly updated with time and the concentration of GFP, corresponding to the obtained control feedback gain K K It is also not a fixed value, but can be adjusted to adapt to the dynamic changes of the available ribosome number in the cell during the expression of the GFP gene. Finally, by iteratively solving equations (19) and (20), the aggregation matrix M and the constant parameter P are obtained, and the aggregation matrix M and the constant parameter P obtained in each iteration are substituted into equation (16) to obtain the control feedback gain K K , that is, the control value, according to which the control input u(t) is updated K until the end of the expression process.

[0149] The method proposed in the present application is applied to the expression process of the GFP gene. According to the biological control loop of the GFP gene expression process, the constructed control mathematical model is:

[0150] dx1=(u-θ*x1*x2)-δ*x1

[0151] dx2=(TS*U-u*x3-θ*x1*x2)-δ*x2

[0152] dx3=R*(x1 / K p )*(1-d)-γ*x3

[0153] Wherein, the variables x1, x2 and x3 represent the concentrations of GFP / ECF32 mRNA m, sRNA s and protein y generated by the regulated gene respectively; u represents the transcription promoter strength of the regulated gene, θ represents the degradation rate of the mRNA-sRNA complex; the degradation rate of RNA is δ, TS represents the promoter strength of sRNA; u represents the control input to be designed. R represents the translation rate of the regulated gene, K p represents the dissociation constant of the ribosome binding site to the protein expressed by the regulated gene; γ represents the degradation rate of the protein generated by the regulated gene; d represents the reduction rate of the available ribosome in the cell, and 0 < d < 1, when d = 0, it means that the number of ribosomes does not fluctuate; the larger d is, the greater the fluctuation of the number of free ribosomes in the cell.

[0154] The initial state of the system is selected as [0; 0; 0] T , the strength of the transcription promoter u = 15, the ribosome number disturbance is added at the middle time of the simulation, and the value is: d = 0.3. In order to make the evaluation of the robustness of the regulated transcription device more intuitive, the following formula for calculating the robustness of the system is proposed:

[0155]

[0156] The accurate differential form of the model of the system is obtained through (4), and the mathematical equation inequality (19) is solved, and then the aggregation matrix M and the constant parameter P are brought into (16) to obtain the control feedback gain K of the next moment K .

[0157] Specifically, referring to Figure 4 the state trajectory of the gene expression process under ribosome disturbance in FIG. 6, the change of the final product protein GFP concentration, the change of the mRNA concentration in the gene expression intermediate product and the change of the sRNA concentration are sequentially represented from top to bottom. It is found through observation that even when the number of ribosomes fluctuates at the intermediate moment, the concentration of the protein GFP does not present obvious oscillation phenomenon.

[0158] This phenomenon is further explained in Figure 5 , which shows that the robustness of the GFP gene in the expression process can reach 93.36% under certain conditions. Figure 5

[0159] In summary, the present application focuses on the key problem of the influence of the change of the number of ribosomes in the cell on the expression level of the product protein GFP in the gene expression process, especially in the case where the size of the ribosome number disturbance is unknown, the control input can be dynamically adjusted according to the actual disturbance change condition and the current concentration level of GFP. The determination of the control input value by using the method of the present application no longer depends on a large amount of experimental data, which not only significantly reduces the experimental cost, effectively improves the control efficiency, but also greatly enhances the stability and flexibility of the gene expression process, and provides an innovative and efficient solution for the precise regulation of gene expression.

[0160] Embodiment Two

[0161] Based on the same inventive concept as embodiment one, the present application also provides a gene expression process optimization regulation system for realizing the steps of the gene expression process optimization regulation method described in embodiment one. As shown in Figure 6 , the gene expression process optimization regulation system comprises the following modules:

[0162] The regulation loop construction module 100 is used for constructing a biological regulation loop of a regulated gene expression process.

[0163] The gene expression process modeling module 200 is used for obtaining a mathematical relationship of the gene expression process based on the biological regulation loop.

[0164] ​The controller model design module 300 is configured to design a controller model for capturing dynamic changes in the gene expression process in a space where the curvature is not always 0 based on the mathematical relationship.

[0165] The regulation input value calculation module 400 is configured to establish a matrix equation for solving the regulation input based on the controller model, and obtain a regulation value that makes the expression product GFP concentration of the regulated gene meet the stable condition according to the matrix equation, and update the regulation input using the regulation value.

[0166] The gene regulation module 500 is configured to control the expression process of the regulated gene through the regulation input updated each time, so that the concentration of the gene expression product can be kept stable in the case of fluctuation of the number of ribosomes in the cell.

[0167] The gene expression process optimization regulation system proposed in the embodiment is used to implement the gene expression process optimization regulation method described above, and the specific embodiments of the synchronous response type single-point displacement monitoring system can be seen from the embodiment part of the gene expression process optimization regulation method described above. For example, the regulation loop construction module 100, the gene expression process modeling module 200, the controller model design module 300, the regulation input value calculation module 400, and the gene regulation module 500 are respectively used to correspond to the steps S1, S2, S3, S4, and S5 in the gene expression process optimization regulation method in the embodiment one. Therefore, the specific embodiments can be referred to the description of the corresponding respective embodiment part, and in order to avoid redundancy, it will not be repeated here.

[0168] Embodiment three

[0169] The application further provides a computer storage medium storing a computer software product, and the computer software product includes a plurality of instructions to make a computer device execute the gene expression process optimization regulation method described in the embodiment one.

[0170] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in 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.) containing computer usable program code.

[0171] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0172] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0173] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0174] Obviously, the above-described embodiments are only examples for clarity and are not intended to limit the implementation. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, it is not necessary and impossible to enumerate all the implementations. The obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A method for optimizing the regulation of gene expression processes, characterized in that, The method comprises the following steps: S1: constructing a biological regulation circuit of a regulated gene expression process, in the following manner: Embedding an ECF32 gene sequence upstream of a regulated gene, the regulated gene expressing a GFP protein, the ECF32 gene sequence expressing an ECF32 protein, the ECF32 protein combining with a promoter PECF32 to form a transcription complex, transcription obtaining an sRNA, the sRNA being complementary to a target mRNA transcribed from the regulated gene to obtain double-stranded RNA; S2: obtaining a mathematical relationship of a gene expression process based on the biological regulation circuit, as follows: (1) wherein is a system state change in the gene expression process, denotes the concentration of the product mRNA of the transcription process in the gene expression process, denotes the concentration of the product sRNA in the gene expression process, denotes the concentration of the gene expression product protein; is a control input quantity to be calculated; is a control coefficient matrix; is a nonlinear function; S3: based on the mathematical relationship, designing a controller model for capturing dynamic changes in the gene expression process in a space with a curvature that is not always 0; S4: based on the controller model, establishing a matrix equation for solving a regulation input, and obtaining a regulation value that makes the expression product GFP concentration of the regulated gene satisfy a stable condition according to the matrix equation, and updating the regulation input using the regulation value; S5: controlling the expression process of the regulated gene through the regulation input updated each time, so that the gene expression product concentration can be kept stable in the case of ribosome quantity fluctuation in the cell; In S3, the method for designing a controller model for capturing dynamic changes in the gene expression process in a space with a curvature that is not always 0 based on the mathematical relationship is as follows: The virtual distance is used to solve the distance between the trajectories of the expression product GFP concentration changing over time, in the following steps: The differential form of the mathematical equation is expressed using the virtual distance as follows: =A + (2) wherein, represents a virtual distance, is derivative of a virtual distance a rate of change that is constantly updated over time; A represents a virtual distance a state change matrix that is constantly updated over time; Let the control input where represents the control feedback gain to be designed, i.e. the ratio of the translation rates of the GFP protein and the ECF32 protein, and the differential form of the control input is represented as: (3) The closed-loop expression of the GFP gene expression can be obtained by substituting (3) into (2): [ A + (4) Introducing a control aggregation matrix in a space where the curvature is not constant 0 , and is a positive definite symmetric matrix, so the distance between the trajectories of the expression product GFP concentration under different available ribosome numbers is expressed as: (5)； calculating the rate of change of the distance as follows: (6) wherein, denotes the control aggregation matrix update rate over time; and a rate of change of the distance The judgment condition for reaching the steady state in the GFP gene expression process is constructed according to the distance (7)。 2. The method of claim 1, wherein the process of gene expression is selected from the group consisting of transcription, translation, and post-translational modification. In S4, the method for obtaining a regulation value that makes the expression product GFP concentration of the regulated gene satisfy a stable condition is as follows: S41: constructing a first set of positive definite matrices and a second set of positive definite matrices, wherein the expression of the first set of positive definite matrices is as follows: (8) and the expression of the second set of positive definite matrices is as follows: (9) S42: the inclusion of the judgment condition in formula (7) The term "of the formula (7) requires that the following conditions are met: (10) S43: transforming the inequality (10), including: Let the last term in equation (9) be equal to the last term in equation (8) and solve for G: (11) From equation (11) we have = F Let F= Solving for F we have (12) From equation (12), we can obtain: = = (13) Let =0, we have + =0, to get the calculation expression of control feedback gain ​ =- =- (14) Therefore, based on inequality (10), we can obtain: (15) Let , P is a positive constant parameter, according to formula (14), the calculation expression of control feedback gain is obtained. =- (16) S44: substituting equation (15) into equation (7) to obtain the following linear matrix inequality: - (17) Multiply each term on the right side of equation (17) by This gives: (18) Let , be the derivative of , then equation (17) simplifies to: (19) then the final control feedback gain is: (20) By iteratively solving equation (19) and equation (20), the aggregated matrix M and the constant parameter P are obtained, and the aggregated matrix M and the constant parameter P obtained in each iteration are substituted into equation (16) to obtain the control feedback gain i.e. the control value.

3. A system for optimizing regulation of gene expression processes, characterized in that, The method comprises the following steps: A regulation circuit construction module is configured to construct a biological regulation circuit of a regulated gene expression process; A gene expression process modeling module is configured to obtain a mathematical relationship of a gene expression process based on the biological regulation circuit; A controller model design module is configured to design a controller model for capturing dynamic changes in the gene expression process in a space with a curvature that is not always 0 based on the mathematical relationship; A regulation input value calculation module is configured to obtain a regulation value that makes the expression product GFP concentration of the regulated gene satisfy a stable condition according to the matrix equation based on the controller model, and update the regulation input using the regulation value. A gene regulation module is used to control the expression process of a regulated gene through the regulation input quantity after each update, so that the concentration of the gene expression product can be kept stable in the case of ribosome quantity fluctuation in the cell.

4. A computer storage medium, characterized in that, The computer storage medium stores a computer software product, and the computer software product includes a plurality of instructions to make a computer device execute the gene expression process optimization regulation method in claim 1 or claim 2.

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