Gene switching expression process optimization regulation and control method and system for resisting ribosome quantity disturbance

Abstract

The invention relates to the technical field of gene switching expression process regulation and control, in particular to a ribosome quantity disturbance resistant gene switching expression process optimization regulation and control method and system, and the method comprises the following steps: constructing a biological regulation and control loop based on the interaction relationship between biological molecules in regulated gene switching expression; according to the biological regulation loop, constructing a relational mathematical equation for expressing concentration changes among mRNA, sRNA-A and GFP products; constructing a controller which enables the expression level of the regulated gene to resist ribosome disturbance in cytoplasm; performing iterative calculation based on the controller to obtain a regulation and control input value that the expression product concentration of the regulated and controlled gene meets a stable condition; the input quantity is regulated and controlled to control the switching expression process of the regulated and controlled gene, so that the concentration of an expression product of the regulated and controlled gene can be kept stable under the condition that the quantity of ribosome in a cell fluctuates. The stability and flexibility of the gene switching expression process can be effectively improved, and the regulation efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gene switching expression process regulation, and in particular to a gene switching expression process optimization regulation method and system for resisting ribosome quantity disturbance. Background Art

[0002] In the field of bioengineering, the gene expression process is affected by a variety of factors in the cell environment, which may cause the gene expression state to switch frequently during different reactions. For example, when the transcription promoter strength increases, the gene may switch from the current transcription state to a high transcription state; for another example, when cells are growing and proliferating actively, in order to maintain more functional proteins, the cell may reduce protein degradation and enter a low degradation state. Therefore, it is a very common phenomenon that genes undergo dynamic switching between multiple modes during expression.

[0003] The current regulatory technologies for gene switching expression processes have the following drawbacks:

[0004] First, the cost is high: relying on a large number of repetitive experiments to determine the control input greatly increases the cost of control, involving the consumption of human, material and other resources.

[0005] Second, the regulatory efficiency is low: every time the gene expression state switches mode, it is necessary to conduct experiments again to select new regulatory inputs. The whole process is cumbersome, resulting in low regulatory efficiency.

[0006] Third, lack of flexibility and universality: the designed regulatory input can only guarantee stability under fixed conditions, and it is difficult to adapt to system dynamics such as the intracellular environment. It has poor universality under different environmental conditions and lacks the ability to flexibly respond to changes. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a gene switching expression process optimization regulation method and system for resisting ribosome number disturbance, and the regulation method comprises the following steps:

[0008] S1: Based on the interaction between the mRNA, the product of the regulated gene switching expression process, and the biological molecules of ribosomes and sRNA-A, the biological regulatory circuit of the gene switching expression process is constructed;

[0009] S2: Based on the biological regulatory circuit, a mathematical equation is constructed to express the relationship between the concentration changes of mRNA, sRNA-A and GFP products;

[0010] S3: Based on the mathematical equation of the relationship, construct a controller that enables the expression level of the regulated gene to resist the disturbance caused by the fluctuation of the number of available ribosomes in the cytoplasm;

[0011] S4: Based on the controller, iterative calculation is performed to obtain a dynamically updated control input value in which the concentration of the expression product of the regulated gene meets a stable condition under an environment in which the number of ribosomes in the cytoplasm fluctuates;

[0012] S5: The switching expression process of the regulated gene is controlled by the regulatory input amount after each update, so that it can maintain the stable concentration of the expression product of the regulated gene when the number of ribosomes in the cell fluctuates.

[0013] In one embodiment of the present invention, in S1, the method for constructing a biological control circuit for gene switching expression process is as follows:

[0014] The coding gene of ECF32 protein is embedded in the expression sequence of the regulated gene. The expression product of the regulated gene includes GFP protein and ECF32 protein. The ECF32 protein combines with the promoter PECF32 to form a transcription complex, which is transcribed to obtain sRNA. The sRNA is complementary to the target mRNA transcribed from the regulated gene to obtain double-stranded RNA.

[0015] In one embodiment of the present invention, in S2, the relational mathematical equation is as follows:

[0016]

[0017] Among them, x(t)=[m; s; y] is used to describe the state of the gene switching expression process, m represents the concentration of mRNA, the final product of the transcription stage, s represents the concentration of sRNA-A, the intermediate product of the constructed biological regulatory circuit, and y represents the concentration of the final protein product of the switching expression process; u(t) is the regulatory input in the regulatory circuit; B(x(t), t, r(t)) is the control coefficient matrix in the gene switching expression process; f[x(t), t, r(t)] is a nonlinear function; d represents the degree of fluctuation of the number of ribosomes in the cytoplasm; r(t) represents the switching mode of the gene switching expression process.

[0018] In one embodiment of the present invention, the conversion probability of the switching mode r(t) of the gene switching expression process is as follows:

[0019]

[0020] Among them, r(t)=i means that the switching mode of the gene switching expression process at time t is i, r(t+Δt)=j means that at the time (t+Δt), the mode is switched from mode i to mode j, Δt>0, for i,j∈N,i≠j,π ij >0, and is a finite integer set, n is the total number of switching modes in the gene switching expression process; for each mode i, the following conditions are met:

[0021]

[0022] In one embodiment of the present invention, in S3, the method for constructing a controller that enables the expression level of a regulated gene to resist the disturbance caused by the fluctuation of the number of available ribosomes in the cytoplasm comprises:

[0023] In a non-completely straight space, an imaginary spacing is used to represent the spacing between the GFP concentration change trajectories of the final product of any adjacent gene switching expression under different conditions of available ribosome numbers in the cytoplasm;

[0024] And according to the change rate of the spacing, a judgment condition for reaching a stable state during the switching expression of the regulated gene is constructed.

[0025] In one embodiment of the present invention, in a non-completely straight space, an imaginary spacing is used to represent the spacing between the GFP concentration change trajectories of the final product of any adjacent gene switching expression under an environment with different numbers of available ribosomes in the cytoplasm, including:

[0026] When r(t)=i, the differential form of the mathematical equation of the relationship in formula (1) is expressed as follows using the imaginary spacing:

[0027]

[0028] Among them, δ x represents the imaginary spacing, is δ x The derivative of indicates that in the process of switching expression of any adjacent gene, the hypothetical spacing δ x The rate of change that is updated continuously over time; A i (x(t),t,i,d) represents the imaginary spacing δ x A state change matrix that is continuously updated over time;

[0029] Let the control input u(t) = K i x(t), K i Represents the control gain to be designed, and the differential form of the control input u(t) is expressed as:

[0030] δ u =K i δ x (4)

[0031] Substituting (4) into (3) yields a closed-loop representation of regulated gene expression:

[0032]

[0033] Introducing a regression matrix M in a non-completely flat space i (t), and M i (t) is a positive and symmetric matrix. Therefore, when the number of available ribosomes in the cytoplasm is different, the spacing V (δ x ,t,i) is expressed as follows:

[0034] V(δ x ,t,i)=δ x T M i (t)δ x (6).

[0035] In one embodiment of the present invention, the spacing V(δ x ,t,i) is calculated by introducing a weak differential operator The spacing V(δ x ,t,i) is expressed as follows:

[0036]

[0037] in, Represents the regression matrix M i (t) Update rate over time.

[0038] In one embodiment of the present invention, according to the change rate of the spacing The judgment condition for reaching a stable state during the switching expression of the regulated gene is constructed as follows:

[0039]

[0040] In one embodiment of the present invention, in S4, the method for obtaining the dynamically updated control input value u(t) in which the concentration of the expression product of the regulated gene satisfies the stability condition under the environment where the number of ribosomes in the cytoplasm fluctuates is:

[0041] S41: Construct the first group of positive definite matrices and the second group of positive definite matrices, where the expression of the first group of positive definite matrices is as follows:

[0042] (F i +G i ) T (F i +G i )=F i T F i +G i T F i +Fi T G i +G i T G i (9)

[0043] And let the positive definite matrix Q i =T i T T i , the expression of the second group of positive definite matrices is as follows:

[0044] K t T Q i K i =K i T T i T T i K i (10)

[0045] S42: The judgment condition in formula (8) includes K i The items must meet the following conditions:

[0046] K i T B i T M i +M i B i K i <0 (11)

[0047] S43: Transform inequality (11), including:

[0048] Let the last term G in formula (9) T G is equal to the last term K in equation (10) i T T i T T i K i , solve for G i The expression is:

[0049] G i =T i K i (12)

[0050] From formula (12), we can get G i T F i =K i T T i T Ti K i , let K i T T i T T i K i =K i T B i T M i , solve for F i The expression is:

[0051] F i =(T i T ) -1 B i T M i (13)

[0052] From formula (13), we can get:

[0053] F i T F i =M i B i (T i ) -1 (T i T ) -1 B i T M i =M i B i Q i -1 B i T M i (14)

[0054] Let F i +G i = 0, then T i K i +(T i T ) -1 B i T M i = 0, so that Q i =T i T T i , get the control feedback gain K i The calculation expression is:

[0055] K i =-Q i-1 B i T M i (15)

[0056] Therefore, based on inequality (11), we can get:

[0057] K i T B i T M i +M i B i K i =(F i +G i ) T (F i +G i )-2M i B i Q i -1 B i T M i <0 (16)

[0058] Let P i =2Q i -1 , P i is a constant positive quantitative parameter. According to formula (15), the final control feedback gain K is obtained i The calculation expression is:

[0059] K i =-0.5P i B i T M i (17)

[0060] S44: Substituting equation (16) into equation (8) yields the following linear matrix inequality:

[0061]

[0062] Multiply both sides of each term in equation (18) by M i -1 We can get:

[0063]

[0064] Let W i =M i -1 , W i The derivative of , simplifies formula (18) to express:

[0065]

[0066] The final control feedback gain K i The calculation expression is:

[0067] K i =-0.5P i B i T W i (twenty one)

[0068] Iterate and solve equation (20) and equation (21) to obtain the control regression matrix M i and quantitative parameter P i , the control regression matrix M obtained in each iteration i and quantitative parameter P i Substituting into equation (17), we get the control feedback gain K i , using the control feedback gain K i Update the control input u(t).

[0069] Based on the same inventive concept, the present invention also provides a gene switching expression process optimization and regulation system for resisting ribosome number disturbance, characterized in that the steps for implementing the gene switching expression process optimization and regulation method for resisting ribosome number disturbance as claimed in any one of claims 1 to 9, the gene switching expression process optimization and regulation system for resisting ribosome number disturbance comprises the following modules:

[0070] A regulatory circuit construction module is used to construct a biological regulatory circuit of the gene switching expression process based on the interaction between the process product mRNA in the regulated gene switching expression and the biological molecules of ribosomes and sRNA-A;

[0071] A product concentration change association module is used to construct a mathematical equation expressing the relationship between the concentration changes of mRNA, sRNA-A and GFP products based on the biological regulation circuit;

[0072] A controller design module, for constructing a controller based on the mathematical equation of the relationship so that the expression level of the regulated gene can resist the disturbance caused by the fluctuation of the number of available ribosomes in the cytoplasm;

[0073] A control input value solving module is used to perform iterative calculation based on the controller to obtain a dynamically updated control input value in which the concentration of the expression product of the regulated gene meets the stability condition under an environment in which the number of ribosomes in the cytoplasm fluctuates;

[0074] The gene regulation optimization module is used to control the switching expression process of the regulated gene through the regulatory input after each update, so that it can maintain the stable concentration of the expression product of the regulated gene when the number of ribosomes in the cell fluctuates.

[0075] The above technical solution of the present invention has the following advantages compared with the prior art:

[0076] On the basis of being able to measure the concentrations of mRNA, sRNA-A and GFP, the present invention introduces the control theory in engineering into the regulation process of gene switching expression, and by monitoring the change of GFP concentration and the amplitude of ribosome number disturbance during gene switching expression, the biological regulation circuit model of gene switching expression is used to construct a mathematical equation to express the rate of change of intermediate product (mRNA, sRNA-A) and final product GFP concentration, and flexibly calculate the required regulation input value in nonlinear space with the help of imaginary spacing, thereby getting rid of the dependence on a large number of repeated experiments. The present invention significantly improves the efficiency, applicability and stability of the regulation process of gene switching expression, making the gene switching expression process more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0078] Figure 1 This is a schematic flow chart of a method for optimizing and regulating a gene switching expression process for resisting ribosome number disturbance provided in Example 1 of the present invention;

[0079] Figure 2 It is the trajectory diagram of the concentration changes of the expression products GFP protein, mRNA and sRNA-A when the number of ribosomes fluctuates;

[0080] Figure 3 It is a result of the robustness of the gene switching expression process;

[0081] Figure 4 It is a schematic diagram of the switching of different modes during the gene switching expression process;

[0082] Figure 5 This is a schematic diagram of the structure of a gene switching expression process optimization regulation system for resisting ribosome number disturbance provided in Example 2 of the present invention;

[0083] Explanation of the reference numerals in the specification: 100, control circuit construction module; 200, product concentration change association module; 300, controller design module; 400, control input value solution module; 500, gene regulation optimization module. DETAILED DESCRIPTION

[0084] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0085] Embodiment 1

[0086] like Figure 1 As shown, the present invention provides a method for optimizing and regulating the gene switching expression process to resist ribosome number disturbance, and the regulation method comprises the following steps:

[0087] S1: Based on the interaction between the mRNA, the product of the regulated gene switching expression process, and the biological molecules of ribosomes and sRNA-A, the biological regulatory circuit of the gene switching expression process is constructed;

[0088] S2: Based on the biological regulatory circuit, a mathematical equation is constructed to express the relationship between the concentration changes of mRNA, sRNA-A and GFP products;

[0089] S3: Based on the mathematical equation of the relationship, construct a controller that enables the expression level of the regulated gene to resist the disturbance caused by the fluctuation of the number of available ribosomes in the cytoplasm;

[0090] S4: Based on the controller, iterative calculation is performed to obtain a dynamically updated control input value in which the concentration of the expression product of the regulated gene meets a stable condition under an environment in which the number of ribosomes in the cytoplasm fluctuates;

[0091] S5: The switching expression process of the regulated gene is controlled by the regulatory input amount after each update, so that it can maintain the stable concentration of the expression product of the regulated gene when the number of ribosomes in the cell fluctuates.

[0092] It can be seen from the above technical scheme that the present invention constructs a targeted biological regulation circuit by deeply analyzing the biological molecular interaction relationship between the process product mRNA and ribosomes and sRNA-A in the expression of regulated gene switching, which enables the regulation of gene expression to be closely combined with the key biological molecules in the gene switching expression process, and achieves precise regulation from the root, avoiding the blindness and inaccuracy of traditional regulation methods. The mathematical equation for the relationship between the concentration changes of mRNA, sRNA-A and GFP products constructed based on the biological regulation circuit can quantitatively describe the dynamic changes of the concentration of each substance in the gene switching expression process. This quantitative method helps to deeply understand the inherent laws of gene expression. Through mathematical models, the situation of gene expression under different conditions can be predicted and simulated in advance, so as to formulate a more targeted regulation strategy. The dynamically updated regulation input value obtained by iteratively calculating the designed controller can be flexibly adjusted according to the real-time changes in the number of ribosomes in the cell. This dynamic optimization method enables the regulation process to adapt to the changes in the intracellular environment in real time and always maintain the best regulation state.

[0093] Furthermore, in S1, the method for constructing a biological regulatory circuit for the gene switching expression process is as follows:

[0094] The coding gene of ECF32 protein is embedded in the expression sequence of the regulated gene. The expression product of the regulated gene includes GFP protein and ECF32 protein. The ECF32 protein combines with the promoter PECF32 to form a transcription complex, which is transcribed to obtain sRNA. The sRNA is complementary to the target mRNA transcribed from the regulated gene to obtain double-stranded RNA.

[0095] In step S2, based on the biological control circuit, a mathematical equation describing the relationship between the concentration changes of mRNA, sRNA-A and GFP products is constructed as follows:

[0096]

[0097] Among them, x(t) = [m; s; y] is used to describe the state of the gene switching expression process, m represents the concentration of the final product mRNA in the transcription stage during the gene switching expression process, s represents the concentration of the intermediate product sRNA-A of the constructed biological regulatory circuit, and y represents the concentration of the final protein product of the switching expression process; u(t) is the regulatory input in the regulatory circuit; B(x(t), t, r(t)) is the control coefficient matrix in the gene switching expression process; f[x(t), t, r(t)] is a nonlinear function; d represents the degree of fluctuation of the number of ribosomes in the cytoplasm; r(t) represents the switching mode of the gene switching expression process. The conversion probability of the switching mode r(t) of the gene switching expression process is as follows:

[0098]

[0099] Among them, r(t)=i means that the switching mode of the gene switching expression process at time t is i, r(t+Δt)=j means that at the time (t+Δt), the mode is switched from mode i to mode j, Δt>0, for i,j∈N,i≠j,π ij >0, and is a finite integer set, n is the total number of switching modes in the gene switching expression process; for each mode i, the following conditions are met:

[0100]

[0101] In step S3, the method for constructing a controller that enables the expression level of the regulated gene to resist the disturbance caused by the fluctuation of the number of available ribosomes in the cytoplasm comprises:

[0102] In a non-completely straight space, an imaginary spacing is used to represent the spacing between the GFP concentration change trajectories of the final product of any adjacent gene switching expression under different conditions of available ribosome numbers in the cytoplasm;

[0103] And according to the change rate of the spacing, a judgment condition for reaching a stable state during the switching expression of the regulated gene is constructed.

[0104] When designing a controller in a non-completely straight space, when the number of free ribosomes available in the cytoplasm is different, the distance between any two adjacent trajectories of the GFP concentration change of the switching expression product is not necessarily a straight line, so it is necessary to consider the length of the arc. This will increase the complexity of the calculation, but the tiny local space in the non-completely straight space can be regarded as a completely straight space, so the distance between each trajectory describing the change of GFP concentration in the local completely straight space can be approximately calculated as a straight line.

[0105] In order to use the same measurement standard to calculate the length of the distance in the local micro-space on each track, the imaginary distance is used to represent the distance between the GFP concentration change tracks of the final product of any adjacent gene switching expression under the environment of different available ribosome numbers in the cytoplasm, including:

[0106] When r(t)=i, the differential form of the mathematical equation of the relationship in formula (1) is expressed as follows using the imaginary spacing:

[0107]

[0108] Among them, δ x represents the imaginary spacing, is δx The derivative of indicates that in the process of switching expression of any adjacent gene, the hypothetical spacing δ x The rate of change that is updated continuously over time; A i (x(t),t,i,d) represents the imaginary spacing δ x A state change matrix that is continuously updated over time;

[0109] Let the control input u(t) = K i x(t), K i Represents the control gain to be designed, and the differential form of the control input u(t) is expressed as:

[0110] δ u =K i δ x (4)

[0111] Substituting (4) into (3) yields a closed-loop representation of regulated gene expression:

[0112]

[0113] Introducing a regression matrix M in a non-completely flat space i (t), and M i (t) is a positive and symmetric matrix. Therefore, when the number of available ribosomes in the cytoplasm is different, the spacing V (δ x ,t,i) is expressed as follows:

[0114] V(δ x ,t,i)=δ x T M i (t)δ x (6).

[0115] In one embodiment of the present invention, the spacing V(δ x ,t,i) is calculated by introducing a weak differential operator The spacing V(δ x ,t,i) is expressed as follows:

[0116]

[0117] in, Represents the regression matrix M i (t) Update rate over time.

[0118] Furthermore, if we want to ensure that the GFP gene can maintain the stability of switching expression when the number of available ribosomes in the cytoplasm fluctuates dynamically to varying degrees during the switching expression process, we need to ensure that the change rate of the spacing described in formula (7) is is uniformly negative, then the judgment condition for reaching a stable state during the switching expression of the regulated gene is constructed as:

[0119]

[0120] When the above judgment conditions are met, the distance between any two adjacent GFP concentration change trajectories will gradually decrease in an environment with different degrees of disturbance caused by the number of ribosomes. Therefore, even if the gene GFP is subjected to different disturbances during the switching expression process, it can return to the same stable expression concentration trajectory.

[0121] In step S4, the method for obtaining the dynamically updated control input value u(t) in which the concentration of the expression product of the regulated gene satisfies the stability condition under the environment where the number of ribosomes in the cytoplasm fluctuates is:

[0122] S41: Construct the first group of positive definite matrices and the second group of positive definite matrices, where the expression of the first group of positive definite matrices is as follows:

[0123] (F i +G i ) T (F i +G i )=F i T F i +G i T F i +F i T G i +G i T G i (9)

[0124] And let the positive definite matrix Q i =T i T T i , the expression of the second group of positive definite matrices is as follows:

[0125] K i T Q i K i =K i T T i T T i Ki (10)

[0126] S42: The judgment condition in formula (8) includes K i The items need to meet the following conditions:

[0127] K i T B i T M i +M i B i K i <0 (11)

[0128] S43: Transform inequality (11), including:

[0129] Let the last term G in formula (9) T G is equal to the last term K in equation (10) i T T i T T i K i , solve for G i The expression is:

[0130] G i =T i K i (12)

[0131] From formula (12), we can get G i T F i =K i T T i T T i K i , let K i T T i T T i K i =K i T B i T M i , solve for F i The expression is:

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

[0133] From formula (13), we can get:

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

[0135] Let F i +G i = 0, then T i K i +(T i T ) -1 B i T M i = 0, so that Q i =T i T T i , get the control feedback gain K i The calculation expression is:

[0136] K i =-Q i -1 B i T M i (15)

[0137] Therefore, based on inequality (11), we can get:

[0138] K i T B i T M i +M i B i K i =(F i +G i ) T (F i +G i )-F i T Fi -K i T Q i K i

[0139] =(F i +G i ) T (F i +G i )-2M i B i Q i -1 B i T M i <0 (16)

[0140] Let P i =2Q i -1 , P i is a constant positive quantitative parameter. According to formula (15), the final control feedback gain K is obtained i The calculation expression is:

[0141] K i =-0.5P i B i T M i (17)

[0142] S44: Substituting equation (16) into equation (8) yields the following linear matrix inequality:

[0143]

[0144] Multiply both sides of each term in equation (18) by M i -1 We can get:

[0145]

[0146] Let W i =M i -1 , W i The derivative of , simplifies formula (18) to express:

[0147]

[0148] The final control feedback gain K i The calculation expression is:

[0149] K i =-0.5P i Bi T W i (twenty one)

[0150] It can be found that inequality (20) can be continuously updated according to the change of time and GFP concentration, so the regression matrix M i and quantitative parameter P i It can also change continuously with time and GFP concentration. Accordingly, the control feedback gain K finally solved is i It also becomes a dynamic value that is continuously updated at every moment. With this feature, the controller can regulate the dynamic changes of the GFP gene during the switching expression process through continuous real-time adjustments, thereby effectively resisting the disturbance caused by fluctuations in the number of ribosomes and ensuring the stability and accuracy of the gene switching expression process.

[0151] Iterate and solve equation (20) and equation (21) to obtain the control regression matrix M i and quantitative parameter P i , the control regression matrix M obtained in each iteration i and quantitative parameter P i Substituting into equation (17), we get the control feedback gain K i , using the control feedback gain K i Update the control input u(t).

[0152] The method proposed in the present invention is applied to the switching expression process of the GFP gene. By analyzing the biological regulation circuit, its mathematical model can be expressed as:

[0153] dx1=(u-θ*x1*x2)-δ*x1

[0154] dx2=(TS*u_u*x3-θ*x1*x2)-δ*x2

[0155] dx3=R*(x1 / K p )*(1-d)-γ*x3

[0156] Among them, variables x1, x2 and x3 represent the GFP / ECF32 mRNA concentration m, sRNA concentration s and protein concentration y produced 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 RNA degradation rate is denoted as δ, TS represents the promoter strength of the sRNA; u represents the control input to be designed. R represents the translation rate of the regulated gene, K prepresents the dissociation constant of the ribosome binding site for the protein of the regulated gene expression; γ represents the degradation rate of the protein generated by the regulated gene; d represents the reduction ratio of the available ribosomes 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 available in the cell.

[0157] The initial state of the selected system is [0; 0; 0] T , the strength u of the transcription promoter is 15, and the perturbation amount from the ribosome number is added at the middle time of the simulation, and the value is: d = 0.3. However, in the case of active cell growth and proliferation, in order to maintain more functional proteins, the cell may reduce the degradation of proteins, which will cause the gene switching expression process to enter the low degradation state. At this time, the degradation rate γ of the GFP protein switches from 0.02 to 0.01; the degradation rate δ of the unstable double-stranded RNA switches from 0.05 to 0.02. And the switching probability of the GFP gene between the two modes is:

[0158]

[0159] To make the evaluation of the robustness of gene expression in the regulated transcription device more intuitive, the following formula for calculating the system robustness is proposed:

[0160]

[0161] The precise differential form of the model of the system is obtained through Equation (5), and then the mathematical equation inequality (20) is solved to obtain the control regression matrix M i and the quantitative parameter P i , and then substitute them into Equation (21) to find the control feedback gain K at the next moment i .

[0162] Figure 2 Shows the change of the state trajectory of the gene switching expression process in the face of ribosome perturbation. With the control feedback gain K designed by the present invention i , the expression process can be effectively regulated. In Figure 2 , the three trajectories from top to bottom respectively represent the concentration change trends of the product protein GFP, the intermediate product mRNA-A, and sRNA-A. It can be clearly seen that even if the ribosome number changes dynamically at the middle time, the concentration of the product protein GFP does not show significant attenuation oscillation.

[0163] To further illustrate this phenomenon, it can be found from Figure 3 that during the switching expression process of the GFP gene, the robustness of its expression process can reach 91.478%. In addition, Figure 4Demonstrates the switching of the GFP gene between different modes during expression.

[0164] According to the above, this method is based on the ability to measure the concentrations of mRNA, sRNA-A and GFP. Through mathematical models and theoretical calculations, the feedback control gain applicable to the GFP gene switching expression process is determined, thereby getting rid of the dependence on a large number of repeated experiments. At the same time, the required regulatory input value is flexibly calculated according to the size of the ribosome perturbation and the concentration of GFP. This method significantly improves the efficiency, applicability and stability of the regulation process of gene switching expression, making the gene switching expression process more accurate and efficient. Compared with the traditional method that relies on a large number of repeated experiments to select control variables, an optimized gene switching expression regulation strategy is adopted, which can dynamically calculate the regulation parameters according to the amplitude of ribosome perturbation without a large amount of experimental data. This method can not only significantly reduce the experimental cost, but also effectively improve the regulation efficiency, providing a more efficient and flexible solution for gene expression regulation.

[0165] Embodiment 2

[0166] Based on the same inventive concept as in Example 1, the present invention also provides a gene switching expression process optimization control system for resisting ribosome number disturbance, which is used to implement the steps of the gene switching expression process optimization control method for resisting ribosome number disturbance described in Example 1. Figure 5 As shown, the gene switching expression process optimization regulation system for resisting ribosome number disturbance includes the following modules:

[0167] The regulatory circuit construction module 100 is used to construct a biological regulatory circuit of the gene switching expression process based on the interaction relationship between the process product mRNA in the regulated gene switching expression and the biological molecules of ribosomes and sRNA-A;

[0168] The product concentration change association module 200 is used to construct a mathematical equation expressing the relationship between the concentration changes of mRNA, sRNA-A and GFP products based on the biological regulation circuit;

[0169] A controller design module 300, for constructing a controller based on the mathematical equation of the relationship so that the expression level of the regulated gene can resist the disturbance caused by the fluctuation of the number of available ribosomes in the cytoplasm;

[0170] The control input value solving module 400 is used to perform iterative calculation based on the controller to obtain a dynamically updated control input value in which the concentration of the expression product of the regulated gene meets the stability condition under the environment where the number of ribosomes in the cytoplasm fluctuates;

[0171] The gene regulation optimization module 500 is used to control the switching expression process of the regulated gene through the regulation input amount after each update, so that it can maintain the stable concentration of the expression product of the regulated gene when the number of ribosomes in the cell fluctuates.

[0172] The present embodiment proposes a gene switching expression process optimization and regulation system for resisting ribosome number disturbance, which is used to implement the aforementioned gene switching expression process optimization and regulation method for resisting ribosome number disturbance. Therefore, the specific implementation method of the gene switching expression process optimization and regulation system for resisting ribosome number disturbance can be seen in the embodiment part of the aforementioned gene switching expression process optimization and regulation method for resisting ribosome number disturbance. For example, the control loop construction module 100, the product concentration change association module 200, the controller design module 300, the control input value solving module 400 and the gene regulation optimization module 500 are respectively used to implement the steps S1, S2, S3, S4 and S5 in the gene switching expression process optimization and regulation method for resisting ribosome number disturbance in the first embodiment. Therefore, its specific implementation method can refer to the description of the corresponding embodiments of each part. In order to avoid redundancy, it will not be repeated here.

[0173] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0174] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented 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 processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0175] These computer program instructions may also be stored in a computer-readable memory capable of directing 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 an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0176] 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, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0177] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A method for optimizing and regulating the gene switching expression process to resist ribosome number disturbance, characterized in that: The following steps are involved: S1: Based on the interaction between the mRNA, the product of the regulated gene switching expression process, and the biological molecules of ribosomes and sRNA-A, the biological regulatory circuit of the gene switching expression process is constructed; S2: Based on the biological regulatory circuit, a mathematical equation is constructed to express the relationship between the concentration changes of mRNA, sRNA-A and GFP products; S3: Based on the mathematical equation of the relationship, construct a controller that enables the expression level of the regulated gene to resist the disturbance caused by the fluctuation of the number of available ribosomes in the cytoplasm; S4: Based on the controller, iterative calculation is performed to obtain a dynamically updated control input value in which the concentration of the expression product of the regulated gene meets a stable condition under an environment in which the number of ribosomes in the cytoplasm fluctuates; S5: The switching expression process of the regulated gene is controlled by the regulatory input amount after each update, so that it can maintain the stable concentration of the expression product of the regulated gene when the number of ribosomes in the cell fluctuates.

2. The method for optimizing and regulating the gene switching expression process to resist ribosome number disturbance according to claim 1, characterized in that: In S1, the method for constructing a biological regulatory circuit for the gene switching expression process is as follows: The coding gene of ECF32 protein is embedded in the expression sequence of the regulated gene. The expression product of the regulated gene includes GFP protein and ECF32 protein. The ECF32 protein combines with the promoter PECF32 to form a transcription complex, which is transcribed to obtain sRNA. The sRNA is complementary to the target mRNA transcribed from the regulated gene to obtain double-stranded RNA.

3. The method for optimizing and regulating the gene switching expression process to resist ribosome number disturbance according to claim 1, characterized in that: In S2, the mathematical equation of the relationship is as follows: Among them, x(t)=[m; s; y] is used to describe the state of the gene switching expression process, m represents the concentration of mRNA, the final product of the transcription stage, s represents the concentration of sRNA-A, the intermediate product of the constructed biological regulatory circuit, and y represents the concentration of the final protein product of the switching expression process; u(t) is the control quantity to be input in the regulatory circuit; B(x(t), t, r(t)) is the control coefficient matrix in the gene switching expression process; f[x(t), t, r(t)] is a nonlinear function; d represents the degree of fluctuation of the number of ribosomes in the cytoplasm; r(t) represents the switching mode of the gene switching expression process.

4. The method for optimizing and regulating the gene switching expression process to resist ribosome number disturbance according to claim 3, characterized in that: The conversion probability of the switching mode r(t) of the gene switching expression process is as follows: Among them, r(t)=i means that the switching mode of the gene switching expression process at time t is i, r(t+Δt)=j means that at time (t+Δt), mode i is switched to mode j, Δt>0, for i,j∈N,i≠j,π ij >0, and N = {1, 2, 3, ..... n} is a finite integer set, n is the total number of switching modes in the gene switching expression process; for each mode i, the following conditions are met:

5. The method for optimizing and regulating the gene switching expression process to resist ribosome number disturbance according to claim 3, characterized in that: In S3, the method for constructing a controller that enables the expression level of a regulated gene to resist perturbations caused by fluctuations in the number of available ribosomes in the cytoplasm comprises: In a non-completely straight space, an imaginary spacing is used to represent the spacing between the GFP concentration change trajectories of the final product of any adjacent gene switching expression under different conditions of available ribosome numbers in the cytoplasm; And according to the change rate of the spacing, a judgment condition for reaching a stable state during the switching expression of the regulated gene is constructed.

6. The method for optimizing and regulating the gene switching expression process to resist ribosome number disturbance according to claim 5, characterized in that: In a non-completely flat space, an imaginary spacing is used to represent the spacing between the GFP concentration change tracks of the final products of any adjacent gene switching expression under different conditions of available ribosome numbers in the cytoplasm, including: When r(t)=i, the differential form of the mathematical equation of the relationship in formula (1) is expressed as follows using the imaginary spacing: Among them, δ x represents the imaginary spacing, is δ x The derivative of indicates that in the process of switching expression of any adjacent gene, the hypothetical spacing δ x The rate of change that is updated continuously over time; A i (x(t),t,i,d) represents the imaginary spacing δ x A state change matrix that is continuously updated over time; Let the control input u(t) = K i x(t), K i Represents the control gain to be designed, and the differential form of the control input u(t) is expressed as: d u =K i d x (4) Substituting (4) into (3) yields a closed-loop representation of regulated gene expression: Introducing a regression matrix M in a non-completely flat space i (t), and M i (t) is a positive and symmetric matrix. Therefore, when the number of available ribosomes in the cytoplasm is different, the spacing V (δ x ,t,i) is expressed as follows: V(δ x ,t,i)=δ x T M i (t)δ x (6)。 7. The method for optimizing and regulating the gene switching expression process to resist ribosome number disturbance according to claim 6, characterized in that: The spacing V(δ x ,t,i) is calculated by introducing a weak differential operator The spacing V(δ x ,t,i) is expressed as follows: in, Represents the regression matrix M i (t) Update rate over time.

8. The method for optimizing and regulating the gene switching expression process to resist ribosome number disturbance according to claim 7, characterized in that: According to the rate of change of the spacing The judgment condition for reaching a stable state during the switching expression of the regulated gene is constructed as follows:

9. The method for optimizing and regulating the gene switching expression process to resist ribosome number disturbance according to claim 8, characterized in that: In S4, the method for obtaining the dynamically updated control input value u(t) in which the concentration of the expression product of the regulated gene satisfies the stability condition under the environment where the number of ribosomes in the cytoplasm fluctuates is: S41: Construct the first group of positive definite matrices and the second group of positive definite matrices, where the expression of the first group of positive definite matrices is as follows: (F i +G i ) T (F i +G i )=F i T F i +G i T F i +F i T G i +G i T G i (9) And let the positive definite matrix Q i =T i T T i , the expression of the second group of positive definite matrices is as follows: K i T Q i K i =K i T T i T T i K i (10) S42: The judgment condition in formula (8) includes K i The items must meet the following conditions: K i T B i T M i +M i B i K i <0 (11) S43: Transform inequality (11), including: Let the last term G in formula (9) T G is equal to the last term K in equation (10) i T T i T T i K i , solve for G i The expression is: G i =T i K i (12) From formula (12), we can get G i T F i =K i T T i T T i K i , let K i T T i T T i K i =K i T B i T M i , solve for F i The expression is: F i =(T i T ) -1 B i T M i (13) From formula (13), we can get: F i T F i =M i B i (T i ) -1 (T i T ) -1 B i T M i =M i B i Q i -1 B i T M i (14) Let F i +G i = 0, then T i K i +(T i T ) -1 B i T M i = 0, so that Q i =T i T Y i , get the control feedback gain K i The calculation expression is: K i =-Q i -1 B i T M i (15) Therefore, based on inequality (11), we can get: K i T B i T M i +M i B i K i =(F i +G i ) T (F i +G i )-2M i B i Q i -1 B i T M i <0(16) Let P i =2Q i -1 , P i is a constant positive quantitative parameter. According to formula (15), the control feedback gain K is obtained i The calculation expression is: K i =-0.5P i B i T M i (17) S44: Substituting equation (16) into equation (8) yields the following linear matrix inequality: Multiply both sides of each term in equation (18) by M i -1 We can get: Let W i =M i -1 , W i The derivative of , simplifies formula (18) to express: The final control feedback gain K i The calculation expression is: K i =-0.5P i B i T W i (21) Iterate and solve equation (20) and equation (21) to obtain the control regression matrix M i and quantitative parameter P i , the control regression matrix M obtained in each iteration i and quantitative parameter P i Substituting into equation (17), we get the control feedback gain K i , using the control feedback gain K i Update the control input u(t).

10. A gene switching expression process optimization control system that resists ribosome number disturbance, characterized in that: The steps for implementing the method for optimizing and regulating the gene switching expression process for resisting ribosome number disturbance as claimed in any one of claims 1 to 9, wherein the system for optimizing and regulating the gene switching expression process for resisting ribosome number disturbance comprises the following modules: A regulatory circuit construction module is used to construct a biological regulatory circuit of the gene switching expression process based on the interaction between the process product mRNA in the regulated gene switching expression and the biological molecules of ribosomes and sRNA-A; A product concentration change association module is used to construct a mathematical equation expressing the relationship between the concentration changes of mRNA, sRNA-A and GFP products based on the biological regulation circuit; A controller design module, for constructing a controller based on the mathematical equation of the relationship so that the expression level of the regulated gene can resist the disturbance caused by the fluctuation of the number of available ribosomes in the cytoplasm; A control input value solving module is used to perform iterative calculation based on the controller to obtain a dynamically updated control input value in which the concentration of the expression product of the regulated gene meets the stability condition under an environment in which the number of ribosomes in the cytoplasm fluctuates; The gene regulation optimization module is used to control the switching expression process of the regulated gene through the regulatory input after each update, so that it can maintain the stable concentration of the expression product of the regulated gene when the number of ribosomes in the cell fluctuates.

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