Gene expression process optimization regulation and control method and system and computer storage medium

Abstract

The invention relates to the technical field of gene expression process regulation and control, in particular to a gene expression process optimization regulation and control method and system and a computer storage medium, and the method comprises the following steps: constructing a biological regulation and control loop of a regulated gene expression process to obtain a mathematical relationship of the gene expression process; designing a controller model used for capturing dynamic changes in the gene expression process in a space with the curvature not constantly being 0; based on the controller model, establishing a matrix equation for solving the regulation input quantity, and according to the matrix equation, obtaining the regulation input quantity which enables the expression product GFP concentration of the regulated gene to be stable; the expression process of the regulated gene is controlled by regulating the input quantity, so that the concentration of a gene expression product can be kept stable under the condition that the quantity of ribosome in a cell fluctuates. According to the difference between ribosome disturbance degrees and GFP product concentrations under different situations, the required regulation and control input quantity is calculated, and the efficiency, universality and stability of the gene expression process are improved.

Description

Technical Field

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

[0002] In the field of bioengineering technology, the design of regulatory inputs for gene expression regulation systems has always been a critical and complex process. This process usually relies on extensive experimental verification and data analysis, aiming to accurately select control parameters that can optimize system production performance from massive amounts 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 in the face of disturbances caused by the dynamic changes in the number of ribosomes in cells, thereby ensuring the consistency and reliability of the biomanufacturing process.

[0003] However, the traditional method of selecting control quantities that relies on experimental experience has significant defects. This method not only requires a large number of repetitive experiments, resulting in low R&D efficiency and high costs, but also the designed gene expression regulation system often lacks sufficient flexibility and broad universality. Specifically, when the cell culture environment changes, such as temperature fluctuations or adjustments to the culture medium composition, the control parameters set based on historical experience may not be able to adapt to these changes quickly, resulting in a delayed system response, which in turn has an adverse effect on the final yield and product quality.

[0004] Take 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 metabolite production. Traditionally, operators set an appropriate range of dissolved oxygen concentrations based on previous experimental data and experience to ensure optimal cell proliferation and metabolic efficiency. However, when culture conditions change, this experience-based control strategy may not be able to adjust the dissolved oxygen content in a timely and accurate manner, resulting in the system being unable to effectively respond to environmental changes, thereby affecting cell growth and metabolic processes, and ultimately reducing production and product quality. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method, system and computer storage medium for optimizing and regulating gene expression process, wherein the regulation method comprises the following steps:

[0006] S1: Construction of biological regulatory circuits for regulated gene expression processes;

[0007] S2: Based on the biological regulatory circuit, a mathematical relationship of the gene expression process is obtained;

[0008] S3: Based on the mathematical relationship, a controller model is designed to capture the 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 control input is established, and according to the matrix equation, a control value that makes the GFP concentration of the expression product of the regulated gene meet the stability condition is obtained, and the control input is updated using the control value;

[0010] S5: The expression process of the regulated gene is controlled by the regulatory input after each update, so that it can maintain a stable concentration of gene expression products when the number of ribosomes in the cell fluctuates.

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

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

[0013] In one embodiment of the present invention, 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 change of system state in the process of gene expression, m represents the concentration of mRNA, the product of transcription in the process of gene expression, s represents the concentration of sRNA, the product of gene expression, and y represents the concentration of protein, the product of gene expression; u(t) is the regulatory input to be calculated; B(x(t), t) is the control coefficient matrix; f[x(t), t] is a nonlinear function.

[0016] In one embodiment of the present invention, in S3, in a space where the curvature is not always 0, the method for designing a controller model for capturing dynamic changes in a gene expression process includes:

[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 change rate of the distance, a judgment condition for reaching a stable state during the GFP gene expression process is constructed.

[0019] In one embodiment of the present invention, the distance V(δ x ,t) method steps are as follows:

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

[0021]

[0022] Among them, δ x Indicates the virtual distance, is δ x The derivative of , which represents the virtual distance δ x The rate of change that is continuously updated over time; A(x(t), t) represents the virtual distance δ x A state change matrix that is continuously updated over time;

[0023] 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 that of ECF32 protein, and the differential form of the control input is expressed as:

[0024] δ u =K K δ x (3)

[0025] Substituting (3) into (2), we can obtain the closed-loop representation of GFP gene expression:

[0026]

[0027] In the space where the curvature is not always 0, a control aggregation matrix M(t) is introduced, and M(t) is a uniformly positive definite symmetric matrix. Therefore, under different available ribosome numbers, the distance V(δ x ,t) is expressed as:

[0028] V(δ x ,t)=δ x T M(t)δ x (5).

[0029] In one embodiment of the present invention, the distance V(δ x ,t) is as follows:

[0030]

[0031] in, represents the update rate of the control clustering matrix M(t) over time.

[0032] In one embodiment of the present invention, according to the distance V(δ x ,t), the judgment condition for reaching a stable state during the GFP gene expression process is constructed as:

[0033]

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

[0035] 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:

[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 group of positive definite matrices is as follows:

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

[0039] S42: The judgment condition in formula (7) includes K K The items need to meet the following conditions:

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

[0041] S43: Transform inequality (10), including:

[0042] The last term K in formula (9) is K T T T TK K is equal to the last term G in equation (8) T G, solve the expression of G:

[0043] G=TK K (11)

[0044] From formula (11), we can get G T F=K K T T T F, let K K T T T F=K KT B T M, solve for the expression of F:

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

[0046] From formula (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, then we have TK K +(T T ) -1 B T M=0, and the control feedback gain K is obtained K The calculation expression is:

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

[0050] Therefore, based on inequality (10), we can get:

[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 formula (14), the control feedback gain K is obtained K The calculation expression is:

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

[0054] S44: Substituting equation (15) into equation (7), we obtain the following linear matrix inequality:

[0055]

[0056] Multiply both sides of each term in equation (17) by M -1 We can get:

[0057]

[0058] Let W = M -1 , is the derivative of W, and equation (17) is simplified to:

[0059]

[0060] The final control feedback gain K K for:

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

[0062] By iteratively solving equations (19) and (20), we get the aggregation matrix M and constant parameter P. Substitute the aggregation matrix M and constant parameter P obtained in each iteration into equation (16) to get the control feedback gain K K , that is, the control value.

[0063] Based on the same inventive concept, the present invention also provides a gene expression process optimization and regulation system, which is used to implement the steps of the gene expression process optimization and regulation method. The gene expression process optimization and regulation system includes the following modules:

[0064] Regulatory circuit building blocks, used to construct biological regulatory circuits for regulated gene expression processes;

[0065] A gene expression process modeling module, used to obtain a mathematical relationship of the gene expression process based on the biological regulatory circuit;

[0066] A controller model design module, for designing a controller model for capturing dynamic changes in a gene expression process in a space where the curvature is not always zero, based on the mathematical relationship;

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

[0068] The gene regulatory module is used to control the expression process of the regulated gene through the regulatory input after each update, so that it can maintain the concentration of the gene expression product stable when the number of ribosomes in the cell fluctuates.

[0069] The present invention also provides a computer storage medium, wherein the computer storage medium stores a computer software product, wherein the computer software product includes several instructions for enabling a computer device to execute the method for optimizing and regulating the gene expression process.

[0070] The above technical solution of the present invention has the following advantages compared with the prior art:

[0071] 1. The present invention has carried out an in-depth analysis of the dynamic characteristics of the regulated gene expression process, and cleverly used mathematical methods and theories to carefully design the optimized feedback control gain for the GFP gene expression process. This innovative solution has radically changed the selection mode of control variables in traditional gene expression regulation, effectively getting rid of the excessive reliance on large-scale, time-consuming repeated experiments, greatly improving the efficiency of gene expression, and effectively promoting gene expression to accurately reach the expected level in a shorter time.

[0072] 2. The present invention has the ability to accurately calculate and determine the required regulatory input amount in different situations based on the difference in the degree of ribosome perturbation during the gene expression process and the real-time change in the concentration of the expression product GFP. This not only significantly expands its scope of application, enabling it to be widely adapted to various gene expression systems and enhances universality; it also greatly enhances the stability of the gene expression process, effectively reduces the risk of abnormal expression caused by external interference factors or internal system fluctuations, and provides reliable protection for the precise regulation of gene expression. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] 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:

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

[0075] Figure 2 It is a schematic diagram of a method for optimizing and regulating gene expression process provided in a preferred embodiment of the present invention;

[0076] Figure 3 It is a schematic diagram of the biological regulatory circuit that resists the gene expression process from the perturbation of ribosome number;

[0077] Figure 4 It is the trajectory diagram of the concentration changes of protein products GFP, mRNA and sRNA-A when the gene expression process is disturbed by ribosomes;

[0078] Figure 5 It is a schematic diagram of the robustness results of the gene expression process;

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

[0080] Explanation of the accompanying drawings in the specification: 100, regulatory circuit construction module; 200, gene expression process modeling module; 300, controller model design module; 400, regulatory input value calculation module; 500, gene regulation module. DETAILED DESCRIPTION

[0081] 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.

[0082] Embodiment 1

[0083] Gene expression is a complex and orderly process, which mainly covers two key stages: transcription and translation. Figure 1 As shown. In the transcription stage, the initial product is pre-mRNA. However, this precursor does not have the ability to directly participate in subsequent biological reactions. It 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, the free ribosomes in the cell play an important role. The mature mRNA molecules will bind to these ribosomes. Under the catalysis of the ribosomes, the translation process is officially started, and then the nascent polypeptide chain is synthesized. However, the nascent polypeptide chain is only a semi-finished product. It needs to undergo further post-translational modifications, 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, thereby completing the entire gene expression process and realizing the transformation of the genetic information carried by the gene from the nucleic acid sequence to the functional protein.

[0084] In-depth analysis of the above gene expression process shows that the available concentration of ribosomes in cells has a significant impact on the entire gene expression process. During the transcription process, although ribosomes do not directly participate in the chemical reaction of gene transcription, the available concentration of ribosomes will indirectly affect the efficiency of subsequent translation links, thereby affecting the rate and yield of the entire gene expression. In the translation process, ribosomes are key participants, and their available concentration directly determines the number of ribosomes that can bind to mature mRNA and initiate translation, thereby affecting the rate and amount of new polypeptide chains. Based on this close intrinsic connection, precise regulation of gene expression levels can be achieved by adjusting the available concentration of ribosomes in cells.

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

[0086] S1: Construction of biological regulatory circuits for regulated gene expression processes;

[0087] S2: Based on the biological regulatory circuit, a mathematical relationship of the gene expression process is obtained;

[0088] S3: Based on the mathematical relationship, a controller model is designed to capture 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, a matrix equation for solving the control input is established, and according to the matrix equation, a control value that makes the GFP concentration of the expression product of the regulated gene meet the stability condition is obtained, and the control input is updated using the control value;

[0090] S5: The expression process of the regulated gene is controlled by the regulatory input after each update, so that it can maintain a stable concentration of gene expression products when the number of ribosomes in the cell fluctuates.

[0091] Furthermore, the biological regulatory circuit process of constructing the regulated gene expression process is as follows Figure 3 As shown, including:

[0092] The ECF32 gene sequence is embedded upstream of the regulated gene, the regulated gene expresses GFP protein, the ECF32 gene sequence expresses ECF32 protein, the ECF32 protein binds to the promoter PECF32 to form a transcription complex, transcribes to obtain sRNA, the sRNA is 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, by adjusting the concentration of the transcription product mRNA of the regulated gene, this method can enable the expression level of the gene to maintain a certain degree of stability when the ribosome supply in the cell fluctuates.

[0093] In step S2, according to the above biological regulation circuit and product generation dynamics, 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 change of system state in the process of gene expression, m represents the concentration of mRNA, the product of transcription in the process of gene expression, s represents the concentration of sRNA, the product of gene expression, and y represents the concentration of protein, the product of gene expression; u(t) is the regulatory input to be calculated; B(x(t), t) is the control coefficient matrix; f[x(t), t] is a nonlinear function.

[0096] In order to more accurately describe the close correlation between the various intermediate products and the final products in the gene expression process, the controller was designed in an environment with non-constant spatial curvature. Such a design can more effectively capture the dynamic changes in the concentration of mRNA, sRNA-A and the 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 practicality and accuracy.

[0097] Specifically, in step S3, based on the mathematical relationship, in a space where the curvature is not always 0, a method for designing a controller model for capturing dynamic changes in the gene expression process includes:

[0098] Using virtual distance δ x Solve the distance V(δ x ,t);

[0099] And according to the distance V(δ x ,t) to construct the judgment condition for reaching a stable state during the expression of the GFP gene.

[0100] Compared with the traditional method of judging the stability of GFP only based on whether the GFP level is constant, the present invention measures the changing trend of the distance between the GFP concentration change tracks under different ribosome numbers to evaluate whether GFP can maintain a stable state in the face of ribosome number fluctuations. Specifically, if the distance between any adjacent GFP concentration tracks continues to decrease, this indicates that no matter what degree of perturbation occurs in the ribosome number, the gene expression level of GFP can gradually reach a stable state.

[0101] When designing a controller in a space where the curvature is not always 0, the distance between the expression product GFP concentration change trajectories may appear as a curve rather than a straight line, which does increase the complexity of the calculation. However, the present invention divides this complex space into multiple tiny local areas, and the curvature in each local area can be approximately regarded as 0, so that in these tiny areas, the distance between the GFP concentration trajectories can be approximated as a straight line distance.

[0102] In order to uniformly measure the length of the distance in these tiny spaces, the clustering matrix M is introduced. This is a positive definite symmetric matrix used to standardize and unify the distances in different local spaces during the calculation process. In this way, even in a space where the curvature is not always 0 and in an environment with different available ribosome numbers, a virtual distance can be used to describe the distance between the trajectories of the expression product GFP concentration changes.

[0103] Furthermore, the virtual distance is used to solve the distance V(δ x ,t) method steps are as follows:

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

[0105]

[0106] Among them, δ x Indicates the virtual distance, is δ x The derivative of , which represents the virtual distance δ x The rate of change that is continuously updated over time; A(x(t), t) represents the virtual distance δ x A state change matrix that is continuously updated 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 rates of GFP protein and ECF32 protein, and the exact differential form of the control input is expressed as:

[0108] δ u =K K δ x (3)

[0109] Substituting (3) into (2), we can obtain the closed-loop representation of GFP gene expression:

[0110]

[0111] In the space where the curvature is not always 0, a control aggregation matrix M(t) is introduced, and M(t) is a uniformly positive definite symmetric matrix. Therefore, under different available ribosome numbers, the distance V(δ x ,t) is expressed as:

[0112] V(δ x ,t)=δ x T M(t)δ x (5).

[0113] In addition, the distance V(δ x ,t) rate of change It is expressed as:

[0114]

[0115] in, represents the update rate of the control clustering matrix M(t) over time.

[0116] If we want to ensure that the GFP gene expression process can maintain stable expression under different degrees of ribosome numbers, we need to ensure that formula (6) is uniformly negative definite. In this way, we can ensure that under different degrees of disturbance caused by the number of ribosomes, the distance between any two adjacent GFP concentration change trajectories is gradually reduced. Therefore, even if the gene GFP expression is subject to different degrees of disturbance, it can be gathered on the same stable expression trajectory.

[0117] Therefore, under any ribosome perturbation size, the judgment condition for reaching a stable state during GFP gene expression is deduced using the distance change rate between the expression product GFP concentration change trajectories:

[0118]

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

[0120] 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:

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

[0122] And in order to satisfy the constructed form, construct another term: K K T QK K , where the Q matrix is ​​a uniformly 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 TT T TK K (9)

[0124] S42: The judgment condition in formula (7) includes K K The items need to meet the following conditions:

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

[0126] S43: Transform inequality (10), including:

[0127] The last term K in formula (9) is K T T T TK K is equal to the last term G in equation (8) T G, 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, solve for 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, then we have TK K +(T T ) -1 B T M=0, and the control feedback gain K is obtained K The calculation expression is:

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

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

[0136] K K T B T M+MBK K =(F+G) T (F+G)-F T FK 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 control feedback gain K is obtained K The calculation expression is:

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

[0140] S44: Substituting equation (15) into equation (7), we obtain the following linear matrix inequality:

[0141]

[0142] Multiply both sides of each term in equation (17) by M -1 We can get:

[0143]

[0144] Let W = M -1 , is the derivative of W, and equation (17) is simplified to:

[0145]

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

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

[0148] It can be found that the inequality (19) changes continuously with time and the concentration of GFP. Therefore, the obtained aggregation matrix M and the constant parameter P are also updated continuously with time and the concentration of GFP, and the corresponding control feedback gain K is solved K is not a fixed value either, but can be adjusted continuously to adapt to the dynamic changes in the number of available ribosomes in cells 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. Substitute the aggregation matrix M and the constant parameter P obtained in each iteration into equation (16) to obtain the control feedback gain K K , that is, the regulation value. According to the regulation value K K update the regulation input u(t) until the expression process ends.

[0149] Applying the method proposed in the present invention to the expression process of the GFP gene, based on the biological regulation circuit of the GFP gene expression process, the constructed regulation mathematical model is as follows:

[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] where the variables x1, x2, and x3 represent the GFP / ECF32mRNA concentration m, the sRNA concentration s, and the protein concentration y generated by the regulated gene, respectively; u represents the transcriptional promoter strength of the regulated gene, and θ represents the degradation rate of the mRNA-sRNA complex; the degradation rate of RNA is denoted as 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, and K p represents the dissociation constant of the ribosome binding site for the protein expressed by the regulated gene; γ represents the degradation rate of the protein generated by the regulated gene; d represents the reduction ratio of available ribosomes in the cell, and 0 < d < 1. When d = 0, it means that the number of ribosomes has not fluctuated; the larger d is, the greater the fluctuation in the number of free ribosomes available in the cell.

[0154] Select the initial state of the system as [0; 0; 0] T , the transcriptional promoter strength u = 15, and add a perturbation amount from the number of ribosomes at the middle moment of the simulation, and the value is: d = 0.3. To make the evaluation of the robustness of gene expression in the regulated transcriptional device more intuitive, the following formula for calculating the system robustness is proposed:

[0155]

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

[0157] Specifically, refer to Figure 4 The state trajectory of the gene expression process under ribosome perturbation, which characterizes the changes in the concentration of the final product protein GFP, the changes in the concentration of the gene expression intermediate product mRNA, and the changes in the concentration of sRNA from top to bottom. Through observation, it was found that even when the number of ribosomes fluctuated at the intermediate moment, the concentration of protein GFP did not show obvious oscillation.

[0158] This phenomenon is Figure 5 It is further explained in Figure 5 It was demonstrated that under certain conditions, the robustness of the GFP gene during expression can reach 93.36%.

[0159] In summary, the present invention focuses on the key issue of the effect of changes in the number of ribosomes in cells on the expression level of the product protein GFP during gene expression, especially when the size of the ribosome number disturbance is unknown, the control input can be dynamically adjusted according to the actual disturbance change and the current GFP concentration level. Using the method of the present invention, the determination of the control input value no longer depends on a large amount of experimental data, which not only significantly reduces the experimental cost and effectively improves the control efficiency, but also greatly enhances the stability and flexibility of the gene expression process, providing an innovative and efficient solution for the precise regulation of gene expression.

[0160] Embodiment 2

[0161] Based on the same inventive concept as that of Embodiment 1, the present invention also provides a gene expression process optimization and regulation system for implementing the steps of the gene expression process optimization and regulation method described in Embodiment 1. Figure 6 As shown, the gene expression process optimization and regulation system includes the following modules:

[0162] A regulatory circuit construction module 100, for constructing a biological regulatory circuit of a regulated gene expression process;

[0163] A gene expression process modeling module 200, for obtaining a mathematical relationship of the gene expression process based on the biological regulation circuit;

[0164] A controller model design module 300, for designing a controller model for capturing dynamic changes in a gene expression process in a space where the curvature is not always 0, based on the mathematical relationship;

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

[0166] The gene regulation module 500 is used to control the expression process of the regulated gene through the regulation input amount after each update, so that it can maintain the concentration of the gene expression product stable when the number of ribosomes in the cell fluctuates.

[0167] A gene expression process optimization and regulation system proposed in this embodiment is used to implement the aforementioned gene expression process optimization and regulation method. Therefore, the specific implementation method of the synchronous responsive single-point displacement monitoring system can be seen in the embodiment part of the aforementioned gene expression process optimization and regulation method. For example, the control loop construction module 100, the gene expression process modeling module 200, the controller model design module 300, the control input value calculation module 400 and the gene regulation module 500 are respectively used to correspond to the implementation of steps S1, S2, S3, S4 and S5 in the gene expression process optimization and regulation method in Example 1. Therefore, its specific implementation method can refer to the description of the corresponding various parts of the embodiment. In order to avoid redundancy, it will not be repeated here.

[0168] Embodiment 3

[0169] The present invention also provides a computer storage medium, which stores a computer software product. The computer software product includes several instructions for enabling a computer device to execute the gene expression process optimization and regulation method described in Example 1.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0174] 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 a gene expression process, characterized in that: The following steps are involved: S1: Construction of biological regulatory circuits for regulated gene expression processes; S2: Based on the biological regulatory circuit, a mathematical relationship of the gene expression process is obtained; S3: Based on the mathematical relationship, a controller model is designed to capture the dynamic changes in the gene expression process in a space where the curvature is not always 0; S4: Based on the controller model, a matrix equation for solving the control input is established, and according to the matrix equation, a control value that makes the GFP concentration of the expression product of the regulated gene meet the stability condition is obtained, and the control input is updated using the control value; S5: The expression process of the regulated gene is controlled by the regulatory input after each update, so that it can maintain a stable concentration of gene expression products when the number of ribosomes in the cell fluctuates.

2. The method for optimizing and regulating gene expression process according to claim 1, characterized in that: In S1, the method for constructing a biological regulatory circuit for a regulated gene expression process is as follows: The ECF32 gene sequence is embedded upstream of the regulated gene, the regulated gene expresses GFP protein, the ECF32 gene sequence expresses ECF32 protein, the ECF32 protein binds to the promoter PECF32 to form a transcription complex, and sRNA is transcribed, and 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 gene expression process according to claim 2, characterized in that: In S2, the method for obtaining the mathematical relationship of the gene expression process is as follows: Wherein, x(t)=[m; s; y] is the change of system state in the process of gene expression, m represents the concentration of mRNA, the product of transcription in the process of gene expression, s represents the concentration of sRNA, the product of gene expression, and y represents the concentration of protein, the product of gene expression; u(t) is the regulatory input to be calculated; B(x(t), t) is the control coefficient matrix; f[x(t), t] is a nonlinear function.

4. The method for optimizing and regulating gene expression process according to claim 3, characterized in that: In S3, methods for designing controller models for capturing dynamic changes in gene expression in a space where curvature is not always zero include: The virtual distance is used to solve the distance between the trajectories of expression product GFP concentration changing over time; And according to the change rate of the distance, a judgment condition for reaching a stable state during the GFP gene expression process is constructed.

5. The method for optimizing and regulating gene expression process according to claim 4, characterized in that: The virtual distance is used to solve the distance V(δ x ,t) method steps are as follows: The differential form of the mathematical equation is expressed using virtual distance as: Among them, δ x Indicates the virtual distance, is δ x The derivative of , which represents the virtual distance δ x The rate of change that is continuously updated over time; A(x(t), t) represents the virtual distance δ x A state change matrix that is continuously updated over time; 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 that of ECF32 protein, and the differential form of the control input is expressed as: d u =K K d x (3) Substituting (3) into (2), we can obtain the closed-loop representation of GFP gene expression: In the space where the curvature is not always 0, a control aggregation matrix M(t) is introduced, and M(t) is a uniformly positive definite symmetric matrix. Therefore, under different available ribosome numbers, the distance V(δ x ,t) is expressed as: V(d x ,t)=δ x T M(t)δ x (5)。 6. The method for optimizing and regulating gene expression process according to claim 5, characterized in that: The distance V(δ x ,t) is as follows: in, represents the update rate of the control clustering matrix M(t) over time.

7. The method for optimizing and regulating gene expression process according to claim 6, characterized in that: According to the distance V(δ x ,t), the judgment condition for reaching a stable state during the GFP gene expression process is constructed as:

8. The method for optimizing and regulating gene expression process according to claim 7, characterized in that: In S4, the method for obtaining the regulation value that makes the GFP concentration of the expression product of the regulated gene meet the stability condition is as follows: 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+G) T (F+G)=F T F+G T F+F T G+G T G (8) And the expression of the second group of positive definite matrices is as follows: K K T QK K =K K T T T TK K (9) S42: The judgment condition in formula (7) includes K K The items must meet the following conditions: K K T B T M+MBK K <0 (10) S43: Transform inequality (10), including: The last term K in formula (9) is K T T T TK K is equal to the last term G in equation (8) T G, solve the expression of G: G=TK K (11) 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, solve for the expression of F: F=(T T ) -1 B T M (12) From formula (12), we can get: F T F=MB(T) -1 (T T ) -1 B T M=MBQ -1 B T M (13) Let F+G=0, then we have TK K +(T T ) -1 B T M=0, and the control feedback gain K is obtained K The calculation expression is: K K =-(T T T) -1 B T M=-Q -1 B T M (14) Based on inequality (10), we can get: K K T B T M+MBK K =(F+G) T (F+G)-2MBQ -1 B T M<0 (15) Let P = 2Q -1 , P is a positive constant parameter. According to formula (14), the control feedback gain K is obtained K The calculation expression is: K K =-0.5PB T M (16) S44: Substituting equation (15) into equation (7), we obtain the following linear matrix inequality: Multiply both sides of each term in equation (17) by M -1 We can get: Let W = M -1 , is the derivative of W, and equation (17) is simplified to: The final control feedback gain K K for: K K =-0.5PB T W -1 (20) By iteratively solving equations (19) and (20), we get the aggregation matrix M and constant parameter P. Substitute the aggregation matrix M and constant parameter P obtained in each iteration into equation (16) to get the control feedback gain K K , that is, the control value.

9. A gene expression process optimization and regulation system, characterized in that: The steps for implementing the method for optimizing and regulating the gene expression process according to any one of claims 1 to 8, wherein the gene expression process optimizing and regulating system comprises the following modules: Regulatory circuit building blocks, used to construct biological regulatory circuits for regulated gene expression processes; A gene expression process modeling module, used to obtain a mathematical relationship of the gene expression process based on the biological regulatory circuit; A controller model design module, for designing a controller model for capturing dynamic changes in a gene expression process in a space where the curvature is not always zero, based on the mathematical relationship; A control input value calculation module is used to establish a matrix equation for solving the control input amount based on the controller model, obtain a control value that makes the GFP concentration of the expression product of the regulated gene meet the stability condition according to the matrix equation, and update the control input amount using the control value; The gene regulatory module is used to control the expression process of the regulated gene through the regulatory input after each update, so that it can maintain the concentration of the gene expression product stable when the number of ribosomes in the cell fluctuates.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer software product, and the computer software product includes a number of instructions for enabling a computer device to execute the method for optimizing and regulating the gene expression process according to any one of claims 1 to 8.

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