Gene expression stabilization method based on sRNA feedback and non-periodic sampling control

Abstract

The application discloses a gene expression stabilization method and system based on sRNA feedback and non-periodic sampling regulation, relates to the technical field of gene expression optimization and regulation, and comprises the following steps: firstly, an sRNA-mediated feedback control gene circuit is designed and constructed; secondly, a state space model describing system dynamics is established, and minimization of the steady-state expression deviation of a target protein before and after ribosome disturbance is taken as an optimization target; through real-time calculation of a stability evaluation index, a non-periodic sampling strategy is used to dynamically judge a regulation triggering time; finally, when the triggering condition is met, the ribosome disturbance level is estimated in real time, and the optimal ribosome binding site strength adjustment value is calculated through solving an optimization problem and is implemented. The application can adaptively maintain the stability of target protein expression in a culture environment with dynamic competition of ribosome resources at a low intervention frequency, and significantly improves the robustness and output consistency of a synthetic biology system.

Description

Technical Field

[0001] This invention relates to the field of gene expression optimization and regulation technology, and in particular to a gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation. Background Technology

[0002] In the fields of biomanufacturing and synthetic biology, achieving predictable and stable protein output is a core objective. However, engineered biological systems commonly face the challenge of "cellular background interference" in practical applications, manifested as inconsistent function of genetic circuits under different cellular environments or culture conditions. Among the many influencing factors, the dynamic fluctuations in the availability of ribosomes, as the core translational machine, have become a key factor leading to gene expression variations. As a limited and shared critical resource within the cell, the ribosome pool is simultaneously competed for by endogenous genes and introduced synthetic circuits. When multiple genes, especially high-yield target genes and competing genes, are co-expressed, it triggers fierce competition for limited ribosome resources. This unavoidable ribosome perturbation directly leads to significant fluctuations in the efficiency of target protein synthesis, severely impacting the yield stability, process reproducibility, and final product quality consistency of synthetic biological systems in applications such as biopharmaceuticals, metabolic engineering, and fine chemical production.

[0003] To address this challenge, feedback control strategies have been introduced into synthetic biology to enhance the robustness of systems to external perturbations. Among these, post-transcriptional regulatory mechanisms based on synthetic small RNAs (sRNAs) have shown significant potential. sRNAs can precisely downregulate gene expression by specifically binding to and promoting the degradation of target messenger RNAs. Theoretically, by linking sRNA expression to target protein concentration or its downstream effects, a negative feedback loop can be constructed to partially compensate for resource fluctuations. However, existing sRNA-based regulatory methods largely rely on static or pre-defined regulatory logic, typically optimized for specific, steady-state ribosome levels. When continuous, time-varying fluctuations in intracellular ribosome availability occur, such static regulatory systems cannot adaptively adjust, their compensatory capacity rapidly saturates, resulting in significant deviations in target product expression levels and difficulty in maintaining stable protein output in highly dynamic real fermentation or culture environments.

[0004] Therefore, there is an urgent need in this field for an innovative control framework that can go beyond traditional static sRNA regulation and combine real-time monitoring of ribosome availability with dynamic regulation of ribosome binding site (RBS) strength. Ideally, the method should automatically and precisely identify regulatory needs when ribosome perturbations occur and reengineer the RBS with minimal intervention frequency, thereby achieving higher levels of expression stability in dynamically changing cellular environments. This capability is of great significance for reducing batch-to-batch variability in biomanufacturing processes, improving yields, and achieving reliable coordinated expression of complex multi-gene circuits.

[0005] In summary, under the background of dynamic competition among ribosomes, constructing a novel feedback system that connects resource sensing with adaptive adjustment of translation efficiency holds promise for solving the problem of unstable target gene expression with minimal regulatory cost. This method can effectively suppress expression noise caused by intracellular resource fluctuations, providing a key technical path for the reliable and predictable operation of synthetic genetic circuits in dynamic culture environments. Summary of the Invention

[0006] To address this issue, this invention provides a gene expression stabilization method and system based on sRNA feedback and non-periodic sampling regulation, which solves the problem that static sRNA regulation systems in the prior art cannot adapt to dynamically changing ribosome availability, leading to significant fluctuations in target protein expression under resource competition.

[0007] To address the aforementioned technical problems, this invention provides a gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation, comprising the following steps: Step S1: Design and construct an sRNA-mediated feedback control gene circuit: This circuit contains a co-transcribed RNA, which has a target sequence of sRNA and can be translated to generate a target protein and transcription factor ECF32; the transcription factor ECF32 can activate the transcription of sRNA, and the generated sRNA inhibits its translation by binding to the target sequence of the co-transcribed RNA. Step S2: Establish a state-space model of the gene expression system: Based on the gene circuit constructed in step S1, establish a state-space model describing the dynamic changes in co-transgenic RNA concentration, sRNA concentration, and target protein concentration. This model uses parameters related to ribosome binding site strength. It serves as a core regulatory variable and includes perturbation parameters that reflect changes in ribosome availability. ; Step S3: Set the optimization objective: Construct the objective function with the goal of minimizing the deviation of the target protein's steady-state expression level before and after the change in ribosome availability. ; Step S4: Assess gene expression stability: Based on the state-space model and real-time monitoring data, calculate the evaluation index used to quantify the expression stability of the target protein; Step S5: Non-periodic sampling to determine the timing of regulation: Based on the stability evaluation index obtained in step S4, dynamically determine whether the preset triggering conditions are met, and only start the regulation of the intensity of ribosome binding sites when the conditions are met. Step S6: Determine and implement the control amount: At the trigger point of the control, estimate the current ribosome perturbation level based on real-time monitored concentration data. And by solving the objective function The optimization problem was solved by calculating the optimal ribosome binding site strength adjustment value. And implement it.

[0008] Preferably, in step S1, the feedback control gene loop is implemented by constructing the target protein gene and the ECF32 protein-coding gene into a bicistronic structure and placing them under the control of the same promoter, so that the translation efficiency of the target protein and ECF32 is proportional, thereby converting the change in the concentration of the target protein into a regulatory signal for sRNA transcription.

[0009] Preferably, in step S2, the state-space model is described by the following set of equations: ; ; ; in, , and These represent the concentrations of co-transcribed RNA, sRNA, and target product protein, respectively. , and These represent the rates of change in the concentrations of co-transcribed RNA, sRNA, and target product protein over time, respectively. This is the transcription rate constant for co-transcribed RNA; This represents the plasmid copy number. Describe the regulatory role of transcription factors in the transcription process; and Let be the binding rate constant and dissociation rate constant of the co-transcribed RNA and sRNA, respectively; the degradation rate constants of both co-transcribed RNA and sRNA are denoted as . ; This is the transcription rate constant for sRNA; This represents the maximum translation rate when ribosomes are fully available. This indicates the degree of ribosome unavailability due to resource competition; a higher value indicates fewer usable ribosomes. ; This represents the dissociation constant of the ribosome binding site; The degradation rate constant of the target protein; parameter The ratio of the dissociation constants of the target protein to that of the ECF32 protein is defined as: ,in is the dissociation constant of the ECF32 protein.

[0010] Preferably, the objective function Defined as: ; in, This represents the steady-state concentration of the target protein in the absence of ribosome competition. The goal is to determine the steady-state concentration of the target protein in the presence of ribosome competitive perturbation; the optimization objective is to find a way to achieve this. Minimum value .

[0011] Preferably, in step S4, the stability evaluation index is selected from at least one of the following: (a) Normalized target protein index: ,in To normalize the target protein index, This represents the steady-state concentration of the target protein in the absence of ribosome competition. This represents the steady-state concentration of the target protein in the presence of ribosome competitive perturbation. (b) Target protein fluctuation assessment index based on root mean square error: ,in This represents a target protein fluctuation index based on root mean square error. When ribosome competitive perturbation exists, the first Each sampling time The actual measured concentration of the target protein This indicates the sequence from the 1st to the 2nd. Sum all the deviations from each sampling to calculate the cumulative deviation.

[0012] Preferably, in step S5, the condition for triggering the control timing is that the following conditions are met simultaneously: (1) The expression of the target protein fluctuates beyond the first threshold. ,Right now ,in To normalize the target protein index; (2) The concentration of the target protein tends to stabilize, that is, the concentration difference between adjacent sampling times is less than the second threshold. ; (3) The time interval since the last regulation is greater than the third threshold. .

[0013] Preferably, in step S6, the current ribosome perturbation level is estimated. The method is based on the normalized target protein index obtained through real-time calculation. Current regulatory variables and the functional relationships derived from the state-space model The numerical solution inversion yields the results. The value of .

[0014] Preferably, in step S6, the optimal control amount is calculated. The method is: after estimating the current After the value, As decision variables, the optimization problem is solved using gradient descent, quasi-Newton methods, or direct search algorithms. This is to obtain the ribosome binding site strength adjustment value that restores stable expression of the target protein.

[0015] This invention also provides a gene expression stabilization system based on sRNA feedback and non-periodic sampling regulation. This system is used to implement the gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation described above, specifically including: A feedback control gene circuit construction module is used to design and construct an sRNA-mediated feedback control gene circuit: the circuit contains a co-transcribed RNA, which has a target sequence of sRNA and is capable of translating into a target protein and a transcription factor ECF32; the transcription factor ECF32 can activate the transcription of sRNA, and the generated sRNA inhibits its translation by binding to the target sequence of the co-transcribed RNA. The state-space modeling module is used to establish a state-space model of the gene expression system: based on the gene circuit constructed in step S1, a state-space model describing the dynamic changes in co-transgenic RNA concentration, sRNA concentration, and target protein concentration is established. This model uses parameters related to ribosome binding site strength. It serves as a core regulatory variable and includes perturbation parameters that reflect changes in ribosome availability. ; The optimization objective setting module is used to set the optimization objective: to minimize the deviation of the target protein's steady-state expression level before and after changes in ribosome availability, and to construct the objective function. ; The stability assessment module is used to assess gene expression stability: based on the state-space model and real-time monitoring data, it calculates evaluation indicators to quantify the expression stability of the target protein. The non-periodic sampling regulation triggering module is used to determine the timing of regulation through non-periodic sampling: based on the stability evaluation index obtained by the stability evaluation module, it dynamically determines whether the preset triggering conditions are met, and only initiates regulation of the intensity of ribosome binding sites when the conditions are met. The regulation amount calculation and implementation module is used to determine and implement the regulation amount: at the time of triggering regulation, it estimates the current ribosome perturbation level based on real-time monitored concentration data. And by solving the objective function The optimization problem was solved by calculating the optimal ribosome binding site strength adjustment value. And implement it.

[0016] This invention also provides a computer storage medium storing a computer software product, the computer software product including several instructions to cause a computer device to execute the gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation described above.

[0017] As can be seen from the above technical solutions, this invention application has the following beneficial effects: (1) By constructing an sRNA-mediated closed-loop feedback gene circuit and combining real-time state space modeling and stability evaluation, the system can sense changes in ribosome availability in real time and dynamically adjust the strength of ribosome binding sites, thereby maintaining the steady state of target protein expression under resource fluctuations and overcoming the problem of insufficient compensation ability of traditional static regulation under dynamic perturbations.

[0018] (2) Based on stability indicators such as the normalized target protein index, the timing of regulation is dynamically judged. Regulation is only triggered when the expression fluctuation exceeds the set threshold and the system tends to be in a steady state. This avoids frequent or unnecessary genetic operations and achieves "on-demand regulation". While maintaining expression stability, it significantly reduces regulation costs and cell burden.

[0019] (3) By establishing a state-space model containing disturbance parameters, constructing an objective function, and using numerical optimization methods to solve for the optimal control quantity, a systematic closed-loop control from perception and judgment to execution was realized, which improved the accuracy and repeatability of control and provided a predictable and optimizable technical path for the reliable operation of complex synthetic biological systems in dynamic environments. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Referring to the drawings will make the features and advantages of the present invention clearer. The drawings are illustrative and should not be construed as limiting the present invention in any way. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart of a gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation provided by the present invention; Figure 2 This is a schematic diagram of the feedback regulation loop of gene expression mediated by sRNA; Figure 3 It is a graph showing the concentration trends of ctRNA, sRNA, and target protein; Figure 4 It is fixed Comparison of normalized target protein indices under optimized regulation methods for non-cyclic gene expression, with and without regulation; Figure 5 This is a diagram showing the interval between ribosome perturbation and regulation; Figure 6 It is fixed Comparison of target protein fluctuation assessment indices based on root mean square error under value and non-periodic gene expression optimization and regulation methods; Figure 7 This is a block diagram of a gene expression stabilization system based on sRNA feedback and non-periodic sampling regulation provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1: To address the problem in existing technologies where static sRNA regulatory systems cannot adapt to dynamically changing ribosome availability, leading to significant fluctuations in target protein expression under resource competition environments. For example... Figure 1 As shown, this invention proposes a gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation, which includes the following steps: Step S1: Design and construct an sRNA-mediated feedback control gene circuit: This circuit contains a co-transcribed RNA, which has a target sequence of sRNA and can be translated to generate a target protein and transcription factor ECF32; the transcription factor ECF32 can activate the transcription of sRNA, and the generated sRNA inhibits its translation by binding to the target sequence of the co-transcribed RNA. Step S2: Establish a state-space model of the gene expression system: Based on the gene circuit constructed in step S1, establish a state-space model describing the dynamic changes in co-transgenic RNA concentration, sRNA concentration, and target protein concentration. This model uses parameters related to ribosome binding site strength. It serves as a core regulatory variable and includes perturbation parameters that reflect changes in ribosome availability. ; Step S3: Set the optimization objective: Construct the objective function with the goal of minimizing the deviation of the target protein's steady-state expression level before and after the change in ribosome availability. ; Step S4: Assess gene expression stability: Based on the state-space model and real-time monitoring data, calculate the evaluation index used to quantify the expression stability of the target protein; Step S5: Non-periodic sampling to determine the timing of regulation: Based on the stability evaluation index obtained in step S4, dynamically determine whether the preset triggering conditions are met, and only start the regulation of the intensity of ribosome binding sites when the conditions are met. Step S6: Determine and implement the control amount: At the trigger point of the control, estimate the current ribosome perturbation level based on real-time monitored concentration data. And by solving the objective function The optimization problem was solved by calculating the optimal ribosome binding site strength adjustment value. And implement it.

[0023] As can be seen from the above technical solution, this invention proposes a gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation. This method forms a complete adaptive control closed loop through the following steps, thereby effectively suppressing the target protein expression fluctuations caused by ribosome resource competition: Step S1 constructs an sRNA-mediated feedback gene loop, converting the target protein concentration information into sRNA transcriptional regulatory signals through the transcription factor ECF32, realizing real-time sensing and primary compensation of expression perturbations; Step S2 establishes a mechanism including ribosome perturbation parameters. The state-space model provides a dynamic basis for the quantitative analysis and optimization of the system; step S3 sets the objective function as minimizing the expression bias. This clarified the direction for optimizing regulation; step S4 involved calculating, for example, the normalized protein index. The evaluation indicators enable real-time quantitative assessment of expression stability. Step S5, based on the assessment results and non-periodic triggering conditions, initiates regulation only when expression fluctuations are significant and tend to stabilize, achieving "intervention on demand" and significantly reducing unnecessary operation frequency. Step S6 then estimates the current perturbation level through inversion at the triggering time. The optimal ribosome binding site strength adjustment value was then calculated and optimized. Ultimately, this allows for precise adjustment of translation efficiency, enabling the system to maintain stable output of the target protein in a dynamic resource environment.

[0024] Further, in step S1, an sRNA-mediated feedback control gene loop is designed and constructed. A co-transcribed RNA (ctRNA) containing the sRNA target sequence is constructed. This ctRNA, after transcription, can be translated into the target protein and the transcription factor ECF32. The ECF32 protein further activates the transcription of the sRNA. The resulting sRNA and ctRNA form an inactive RNA complex through base complementarity, thereby inhibiting the translation process of ctRNA and reducing the amount of target protein synthesized. By introducing the ECF32 protein-encoding gene, the expression level of the target protein can be adaptively regulated to maintain its concentration stability when competitive gene expression causes changes in the number of ribosomes available for target protein gene expression.

[0025] Specifically, the feedback control gene loop is implemented by constructing the target protein gene and the ECF32 protein-coding gene into a bicistronic structure and placing them under the control of the same promoter, so that the translation efficiency of the target protein and ECF32 is proportional, thereby converting the change in the concentration of the target protein into a regulatory signal for sRNA transcription.

[0026] Furthermore, in step S2, a state-space model of the gene expression system is established.

[0027] Based on the gene circuit constructed in step S1, a state-space model describing the dynamic changes in co-transcribed RNA concentration, sRNA concentration, and target protein concentration is established. This model is described by the following set of ordinary differential equations: (1) (2) (3) in, , and These represent the concentrations of co-transcribed RNA, sRNA, and target product protein, respectively. , and These represent the rate of change of their concentration over time; This is the transcription rate constant for co-transcribed RNA; This represents the plasmid copy number. Describe the regulatory role of transcription factors in the transcription process; and These are the binding rate constant and dissociation constant of co-transcribed RNA and sRNA, respectively; The degradation rate constants for co-transcribed RNA and sRNA; This is the transcription rate constant for sRNA; This represents the maximum translation rate when ribosomes are fully available. This indicates the degree of ribosome unavailability due to resource competition; the higher the value, the fewer ribosomes are available. This represents the dissociation constant of the ribosome binding site; The degradation rate constant of the target protein; parameter The ratio of the dissociation constants of the target protein to that of the ECF32 protein is defined as... (4), of which This is the dissociation constant of the ECF32 protein, and this ratio ultimately affects the concentration ratio of the target protein generated by translation to the ECF32 protein.

[0028] Further, in step S3, an optimization objective is set. The objective function is constructed with the goal of minimizing the deviation in the steady-state expression level of the target protein before and after changes in ribosome availability. : (5) in, This represents the steady-state concentration of the target protein in the absence of ribosome competition. The goal is to determine the steady-state concentration of the target protein in the presence of ribosome competitive perturbation; the optimization objective is to find a way to achieve this. Minimum value .

[0029] Further, in step S4, gene expression stability is evaluated. Based on the state-space model and real-time monitoring data, an evaluation index for quantifying the stability of target protein expression is calculated. The stability evaluation index is selected from at least one of the following: (a) Normalized Target Protein Index: This index is constructed based on the ratio of the steady-state concentration of the target protein after a change in ribosome number to the steady-state concentration before the change, and is expressed as: (6) in, The normalized target protein index is the ratio of the dissociation constants of the target protein and the ECF32 protein. and ribosome interference Related functions .when The closer the value is to 1, the less the target protein expression is affected by changes in ribosome availability, meaning the higher the stability of gene expression.

[0030] (b) Target protein fluctuation assessment index based on root mean square error: By continuously monitoring the change in target protein concentration over time, the cumulative deviation from the steady-state value without ribosome fluctuation is calculated: (7) in, This represents a target protein fluctuation index based on root mean square error. When ribosome competitive perturbation exists, the first Each sampling time The actual measured concentration of the target protein This indicates the sequence from the 1st to the 2nd. The cumulative bias is calculated by summing all the biases from each sampling. This comprehensive metric captures the cumulative time bias of target protein expression dynamics and provides a more robust quantification of how fluctuations in ribosome availability affect gene expression stability compared to single-endpoint measurements.

[0031] Furthermore, in step S5, non-periodic sampling is used to determine the timing of regulation. Based on the stability evaluation index obtained in step S4, a non-periodic sampling-based method for selecting the regulation time point is designed so that when the target protein fluctuates significantly and gradually tends to stabilize, the ribosome binding site is regulated to initiate the adjustment of the sRNA-mediated regulation of the target gene expression level. (8) in, This is the next adjustment time point. , , These represent the target protein fluctuation threshold, the target protein expression homeostasis threshold, and the regulation time interval threshold, respectively. and Indicates the target protein concentration at and The value at a given time point. Therefore, the next adjustment time point needs to meet the following three conditions: (1) The expression of the target protein fluctuates beyond the first threshold. ,Right now ; (2) The concentration of the target protein tends to stabilize, that is, the concentration difference between adjacent sampling times is less than the second threshold. ; (3) The time interval since the last regulation is greater than the third threshold. .

[0032] Further, in step S6, the regulatory amount is determined and implemented. At the trigger point of regulation, the current ribosome perturbation level is estimated based on real-time monitored concentration data. And by solving the objective function The optimization problem was solved by calculating the optimal ribosome binding site strength adjustment value. And implement it.

[0033] Specifically, it includes the following sub-steps:

[0034] 1. Estimate the level of ribosome perturbation : Normalized target protein index obtained based on real-time calculation Current regulatory variables and the functional relationships derived from the state-space model The solution is obtained through numerical inversion (such as Newton's iteration method). The value of is determined by the following system of equations: (9) (10) (11) (12) (13) (14) (15) in, In order to match system parameters and Related expressions.

[0035] 2. Calculate the optimal control amount : After estimating the current After the value, As decision variables, the optimization problem can be solved using gradient descent, quasi-Newton methods, or direct search algorithms. (16) To obtain the ribosome binding site strength adjustment value that restores stable expression of the target protein.

[0036] Example 2: Figure 7 As shown, this invention provides a gene expression stabilization system based on sRNA feedback and non-periodic sampling regulation. This system is used to implement the gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation described in Example 1 above, specifically including: Feedback control gene circuit construction module 100 is used to design and construct sRNA-mediated feedback control gene circuits: the circuit contains co-transcribed RNA, which has a target sequence of sRNA and is capable of translating into a target protein and transcription factor ECF32; the transcription factor ECF32 can activate the transcription of sRNA, and the generated sRNA inhibits its translation by binding to the target sequence of the co-transcribed RNA. State-space modeling module 200 is used to establish a state-space model of the gene expression system: based on the gene circuit constructed in step S1, a state-space model describing the dynamic changes in co-transfected RNA concentration, sRNA concentration, and target protein concentration is established. This model uses parameters related to ribosome binding site strength. It serves as a core regulatory variable and includes perturbation parameters that reflect changes in ribosome availability. ; The optimization target setting module 300 is used to set the optimization target: to minimize the deviation of the steady-state expression level of the target protein before and after the change in ribosome availability, and to construct the objective function. ; The stability assessment module 400 is used to assess gene expression stability: based on the state-space model and real-time monitoring data, it calculates evaluation indicators to quantify the expression stability of the target protein. The non-periodic sampling control trigger module 500 is used to determine the timing of control through non-periodic sampling: based on the stability evaluation index obtained by the stability evaluation module 400, it dynamically determines whether the preset trigger conditions are met, and only initiates the control of the intensity of the ribosome binding site when the conditions are met. The regulation amount calculation and implementation module 600 is used to determine and implement the regulation amount: at the time of triggering regulation, it estimates the current ribosome perturbation level based on real-time monitored concentration data. And by solving the objective function The optimization problem was solved by calculating the optimal ribosome binding site strength adjustment value. And implement it.

[0037] This embodiment presents a gene expression stabilization system based on sRNA feedback and non-periodic sampling regulation, used to implement the aforementioned gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation. Therefore, the specific implementation of the gene expression stabilization system based on sRNA feedback and non-periodic sampling regulation can be found in the previous section on the embodiments of the gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation. For example, the feedback control gene loop construction module 100, the state space modeling module 200, the optimization target setting module 300, the stability assessment module 400, the non-periodic sampling regulation triggering module 500, and the regulation amount calculation and implementation module 600 are respectively used to implement steps S1, S2, S3, S4, S5, and S6 in the aforementioned gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation. Therefore, its specific implementation can be referred to the descriptions of the corresponding embodiments. To avoid redundancy, further details are omitted here.

[0038] Example 3: This embodiment of the invention provides a computer storage medium storing a computer software product. The computer software product includes several instructions to cause a computer device to execute the above-described gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation.

[0039] Example 4: The following uses green fluorescent protein (GFP) as the target protein gene and red fluorescent protein (RFP) as the competing gene to illustrate the implementation of the present invention.

[0040] like Figure 2 As shown, the system design incorporates four key biological components: the sRNA gene, the sRNA target sequence, ECFsigma factor 32_1122 (ECF32), and its homologous promoter pECF32. The GFP gene and ECF32 gene are co-transcribed via the same promoter to form a co-transcribed RNA (ctRNA) containing the sRNA target sequence, GFP, and ECF32. Since ECF32 is downstream of GFP, forming a bicistronic operon structure, both genes share the same ribosome pool, and their translation rates are proportional. The translated ECF32 protein binds to the pECF32 promoter, initiating sRNA transcription. Subsequently, the sRNA specifically binds to the target sequence on the ctRNA, forming an inactive RNA complex (RC), ultimately leading to ctRNA degradation. The continuous kinetic model of this process is shown below: (17) (18) (19) The production process was set to 100 hours, and the continuous model was discretized using the Euler method with a sampling interval of 0.1 hours. The objective function was chosen to maintain the stability of the target protein concentration before and after ribosome perturbation, as shown in formula (5). Initially, the concentrations of ctRNA, sRNA, and target protein were all set to 0. Competitive gene expression was introduced at the 50-hour time point to simulate the ribosome resource competition effect. Based on the stability index of formula (6) as the trigger condition, the non-periodic regulation strategy shown in formula (8) was adopted, where the target protein fluctuation threshold... Target protein expression homeostasis threshold and control time interval threshold The values ​​were set to 0.01, 0.0001, and 7, respectively. The ribosome perturbation level was estimated in real time based on the normalized target protein index and the current RBS intensity using the equations constructed by formulas (9)-(15). Then, the optimal RBS adjustment amount is obtained by solving the optimization problem (16).

[0041] The results show that, after adopting the regulation method of the present invention, the concentration trends of ctRNA, sRNA, and target protein in the culture medium are as follows: Figure 3 As shown, after 50 hours of introducing competing gene expression, the target gene expression level remained stable despite a significant decrease in ribosome availability. Figure 4 Demonstrates the use of fixed A comparison of the normalized target protein index under three conditions—no regulation, no regulation, and the method of the present invention—shows that the method of the present invention significantly improves the stability of target gene expression. Figure 5This reflects the dynamic characteristics of ribosome perturbations caused by competitive gene expression, showing that the perturbations exhibit alternating patterns of small oscillations and large fluctuations after 50 hours. Thanks to the non-periodic triggering mechanism, the system can accurately identify the timing of regulation and adjust the RBS parameter only when necessary, effectively avoiding over-intervention. Figure 6 The control effects of different methods were further quantified using a target protein fluctuation assessment index based on root mean square error. The results show that the method of the present invention, compared with the fixed method, is superior. and In these cases, expression stability was improved by 73.6% and 50.9%, respectively.

[0042] Based on the above, this invention establishes a rapidly responsive and precisely targeted gene expression regulation strategy by constructing an sRNA-mediated intelligent feedback system. This method not only effectively offsets the fluctuations in target protein expression caused by competition for ribosome resources, but also achieves an optimal balance between regulatory costs and expression stability through a non-periodic triggering mechanism. Experimental data fully validate the excellent performance of this invention in maintaining stable gene expression in complex cellular environments, providing reliable technical support for the robust operation of synthetic biology systems in dynamic environments. Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0043] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0044] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0045] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation, characterized in that, Includes the following steps: Step S1: Design and construct an sRNA-mediated feedback control gene circuit: This circuit contains a co-transcribed RNA, which has a target sequence of sRNA and can be translated to generate a target protein and transcription factor ECF32; the transcription factor ECF32 can activate the transcription of sRNA, and the generated sRNA inhibits its translation by binding to the target sequence of the co-transcribed RNA. Step S2: Establish a state-space model of the gene expression system: Based on the gene circuit constructed in step S1, establish a state-space model describing the dynamic changes in co-transgenic RNA concentration, sRNA concentration, and target protein concentration. This model uses parameters related to ribosome binding site strength. It serves as a core regulatory variable and includes perturbation parameters that reflect changes in ribosome availability. ; Step S3: Set the optimization objective: Construct the objective function with the goal of minimizing the deviation of the target protein's steady-state expression level before and after the change in ribosome availability. ; Step S4: Assess gene expression stability: Based on the state-space model and real-time monitoring data, calculate the evaluation index used to quantify the expression stability of the target protein; Step S5: Non-periodic sampling to determine the timing of regulation: Based on the stability evaluation index obtained in step S4, dynamically determine whether the preset triggering conditions are met, and only start the regulation of the intensity of ribosome binding sites when the conditions are met. Step S6: Determine and implement the control amount: At the trigger point of the control, estimate the current ribosome perturbation level based on real-time monitored concentration data. And by solving the objective function The optimization problem was solved by calculating the optimal ribosome binding site strength adjustment value. And implement it.

2. The gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation according to claim 1, characterized in that, In step S1, the feedback control gene loop is implemented by constructing the target protein gene and the ECF32 protein-coding gene into a bicistronic structure and placing them under the control of the same promoter, so that the translation efficiency of the target protein and ECF32 is proportional, thereby converting the change in the concentration of the target protein into a regulatory signal for sRNA transcription.

3. The gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation according to claim 1, characterized in that, In step S2, the state-space model is described by the following set of equations: ； ； ； in, , and These represent the concentrations of co-transcribed RNA, sRNA, and target product protein, respectively. , and These represent the rates of change in the concentrations of co-transcribed RNA, sRNA, and target product protein over time, respectively. This is the transcription rate constant for co-transcribed RNA; This represents the plasmid copy number. Describe the regulatory role of transcription factors in the transcription process; and Let be the binding rate constant and dissociation rate constant of the co-transcribed RNA and sRNA, respectively; the degradation rate constants of both co-transcribed RNA and sRNA are denoted as . ; This is the transcription rate constant for sRNA; This represents the maximum translation rate when ribosomes are fully available. This indicates the degree of ribosome unavailability due to resource competition; a higher value indicates fewer usable ribosomes. ; The dissociation constant representing the ribosome binding site; The degradation rate constant of the target protein; parameter The ratio of the dissociation constants of the target protein to that of the ECF32 protein is defined as: ,in is the dissociation constant of the ECF32 protein.

4. The gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation according to claim 1, characterized in that, The objective function Defined as: ； in, This represents the steady-state concentration of the target protein in the absence of ribosome competition. The goal is to determine the steady-state concentration of the target protein in the presence of ribosome competitive perturbation; the optimization objective is to find a way to achieve this. Minimum value .

5. The gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation according to claim 1, characterized in that, In step S4, the stability evaluation index is selected from at least one of the following: (a) Normalized target protein index: ,in To normalize the target protein index, This represents the steady-state concentration of the target protein in the absence of ribosome competition. This represents the steady-state concentration of the target protein in the presence of ribosome competitive perturbation. (b) Target protein fluctuation assessment index based on root mean square error: ,in This represents a target protein fluctuation index based on root mean square error. When ribosome competitive perturbation exists, the first Each sampling time The actual measured concentration of the target protein This indicates the sequence from the 1st to the 2nd. Sum all the deviations from each sampling to calculate the cumulative deviation.

6. The gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation according to claim 1, characterized in that, In step S5, the condition for triggering the control timing is that the following conditions are met simultaneously: (1) The expression of the target protein fluctuates beyond the first threshold. ,Right now ,in To normalize the target protein index; (2) The concentration of the target protein tends to stabilize, that is, the concentration difference between adjacent sampling times is less than the second threshold. ; (3) The time interval since the last regulation is greater than the third threshold. .

7. The gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation according to claim 1, characterized in that, In step S6, the current ribosome perturbation level is estimated. The method is based on the normalized target protein index obtained through real-time calculation. Current regulatory variables and the functional relationships derived from the state-space model The numerical solution inversion yields the results. The value of .

8. The gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation according to claim 1, characterized in that, In step S6, the optimal control amount is calculated. The method is: after estimating the current After the value, As decision variables, the optimization problem is solved using gradient descent, quasi-Newton methods, or direct search algorithms. This is to obtain the ribosome binding site strength adjustment value that restores stable expression of the target protein.

9. A gene expression stabilization system based on sRNA feedback and non-periodic sampling regulation, characterized in that, The system is used to implement the gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation as described in any one of claims 1 to 8, specifically including: A feedback control gene circuit construction module is used to design and construct an sRNA-mediated feedback control gene circuit: the circuit contains a co-transcribed RNA, which has a target sequence of sRNA and is capable of translating into a target protein and a transcription factor ECF32; the transcription factor ECF32 can activate the transcription of sRNA, and the generated sRNA inhibits its translation by binding to the target sequence of the co-transcribed RNA. The state-space modeling module is used to establish a state-space model of the gene expression system: based on the gene circuit constructed in step S1, a state-space model describing the dynamic changes in co-transgenic RNA concentration, sRNA concentration, and target protein concentration is established. This model uses parameters related to ribosome binding site strength. It serves as a core regulatory variable and includes perturbation parameters that reflect changes in ribosome availability. ; The optimization objective setting module is used to set the optimization objective: to minimize the deviation of the target protein's steady-state expression level before and after changes in ribosome availability, and to construct the objective function. ; The stability assessment module is used to assess gene expression stability: based on the state-space model and real-time monitoring data, it calculates evaluation indicators to quantify the expression stability of the target protein. The non-periodic sampling regulation triggering module is used to determine the timing of regulation through non-periodic sampling: based on the stability evaluation index obtained by the stability evaluation module, it dynamically determines whether the preset triggering conditions are met, and only initiates regulation of the intensity of ribosome binding sites when the conditions are met. The regulation amount calculation and implementation module is used to determine and implement the regulation amount: at the time of triggering regulation, it estimates the current ribosome perturbation level based on real-time monitored concentration data. And by solving the objective function The optimization problem was solved by calculating the optimal ribosome binding site strength adjustment value. And implement it.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer software product, which includes several instructions for causing a computer device to execute the gene expression stabilization method based on sRNA feedback and non-periodic sampling regulation as described in any one of claims 1 to 8.

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