Method for regulating and controlling gene expression of lactic acid bacteria in shrimp acid fermentation based on systematic biology

Abstract

The invention relates to a method for regulating and controlling gene expression of lactic acid bacteria in shrimp acid fermentation based on systematic biology. The method comprises the following steps: constructing a gene regulation network of lactic acid bacteria by using whole genome data and a high-throughput sequencing technology comprising RNAseq; through clustering analysis and network reconstruction, transcription factors and enzymes of key metabolic pathways of lactic acid generation, acid resistance and glycometabolism are identified, and a dynamic regulation and control model of gene expression is established; a GeneNet or Cytoscape software tool is used, genomics, transcriptomics and metabonomics data of lactic acid bacteria are fused in a multi-layer network, a metabolism regulation and control system is constructed, and the metabolism regulation and control system comprises gene expression regulation and control and real-time change of metabolic flow; the method comprises the following steps: constructing a metabolic flux dynamic model of lactic acid bacteria based on a metabolic flux analysis MFA and a dynamic system modeling technology, and analyzing a mutual relation between a lactic acid generation path and a byproduct generation path; fBA is analyzed through flow equilibrium, and the metabolic network of lactic acid bacteria is optimized.

Description

Technical Field

[0001] The invention relates to a method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation, in particular to a method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology. Background Art

[0002] At present, the gene expression regulation methods of lactic acid bacteria in shrimp acid fermentation based on systems biology have shown significant advantages in theoretical research and application practice, especially in optimizing lactic acid production efficiency and enhancing lactic acid bacteria acid resistance. However, these methods still have some shortcomings and disadvantages in actual operation and application, which limit their wide application in large-scale industrialization. First, the lactic acid bacteria regulation methods based on systems biology rely on high-quality multi-omics data, such as whole genome data, RNA-seq data, and metabolomics data. The acquisition cost of these data is high, especially for large-scale industrial production, this cost may be difficult to sustain. In addition, technical deviations between different experimental conditions and equipment may lead to inconsistent data quality, thus affecting the accuracy and reliability of model construction. Secondly, the construction of dynamic regulatory network models requires complex computing tools and algorithm support. For example, dynamic models of gene expression usually require the use of complex algorithms such as nonlinear differential equations, metabolic flux analysis (MFA), and flux balance analysis (FBA), which require high-performance computing equipment and expertise to implement. For many small and medium-sized fermentation companies, this technical threshold is high, and the lack of a professional team may lead to poor model construction and application results. In addition, existing models rely on settings and simplifications, such as ignoring the multiple feedback effects between gene expression and metabolism or the random perturbations of environmental variables. These simplifications may be feasible in laboratory environments, but in industrial conditions, due to the more complex and changeable fermentation environment, such settings may not fully reflect the actual situation, resulting in deviations in model predictions and failure of regulation. Third, there are still bottlenecks in the combination of metabolic flux analysis and gene regulation. Although dynamic regulatory networks can describe the bidirectional effects of genes and metabolic fluxes, the current models are still insufficient in describing complex metabolic pathways. For example, in the metabolism of lactic acid bacteria, there is a competitive consumption relationship between the lactic acid production pathway and the byproduct production pathway (such as ethanol and pyruvate). This competitive consumption requires more precise quantitative descriptions and optimization strategies. However, existing models usually only focus on the main metabolites, while ignoring the contribution and interference of secondary metabolic pathways, which may lead to the inability of regulatory strategies to fully optimize the overall metabolic efficiency of lactic acid bacteria.

[0003] Fourth, the accuracy and controllability of regulatory means still need to be further improved. At present, most methods based on systems biology regulate the metabolism and gene expression of lactic acid bacteria by dynamically optimizing metabolite concentrations and gene expression rates. However, this regulatory strategy relies on high-precision environmental variable monitoring and real-time feedback systems, but it is usually difficult to achieve real-time and precise regulation of parameters such as pH, temperature, and dissolved oxygen in industrial fermentation environments. In addition, regulatory means mainly rely on gene editing technology (such as CRISPR / Cas9) or synthetic biology tools. These technologies show good feasibility in laboratory environments, but in industrialization, they may face problems such as low gene editing efficiency, poor stability of exogenous DNA insertion, and off-target effects of gene mutations, which in turn affect the stability of lactic acid bacteria performance.

[0004] In summary, although the gene expression regulation method of shrimp acid fermentation lactic acid bacteria based on systems biology has significant theoretical value and practical potential, it still has significant shortcomings in terms of data acquisition cost, model complexity, adaptability to industrial environments, regulation accuracy and time cost. Future research needs to make further breakthroughs in reducing data costs, improving model versatility, and enhancing the stability and accuracy of regulatory means, so as to realize the industrialization and large-scale application of lactic acid bacteria gene expression regulation methods and bring higher efficiency and economic benefits to the lactic acid production industry. Summary of the invention

[0005] The purpose of the present invention is to provide a method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology, thereby solving some of the drawbacks and shortcomings pointed out in the background technology.

[0006] The present invention solves the above-mentioned technical problems by adopting the following technical solution, comprising the following steps:

[0007] S1. Modeling of gene expression and metabolic network of lactic acid bacteria:

[0008] S1.1. Use whole genome data and high-throughput sequencing technologies including RNAseq to construct the gene regulatory network of lactic acid bacteria; identify transcription factors and enzymes related to key metabolic pathways of lactic acid production, acid resistance, and sugar metabolism through cluster analysis and network reconstruction, and establish a dynamic regulatory model of gene expression;

[0009] S1.2. Using GeneNet or Cytoscape software tools, the genomics, transcriptomics and metabolomics data of lactic acid bacteria are integrated into a multi-level network to construct a metabolic regulation system, which includes gene expression regulation and real-time changes in metabolic flux;

[0010] S2. Model construction for optimizing the metabolic pathway of lactic acid bacteria:

[0011] S2.1. Based on metabolic flux analysis (MFA) and dynamic system modeling technology, a dynamic metabolic flux model of lactic acid bacteria was constructed to analyze the relationship between the lactic acid production pathway and the byproduct production pathway.

[0012] S2.2. Optimize the metabolic network of lactic acid bacteria using flow balance analysis (FBA), solve the metabolic flow through the algorithm, and dynamically optimize the metabolic network in combination with external environmental conditions including pH, temperature, and dissolved oxygen;

[0013] S3. Control of gene expression regulation of lactic acid bacteria:

[0014] S3.1. Targeted gene silencing in the lactic acid bacteria genome using CRISPR interference technology; gene knock-in using CRISPR / Cas9 technology;

[0015] S3.2. Based on synthetic biology, design gene regulatory circuits including regulator-driven elements and enhancers to enable lactic acid bacteria to regulate the expression of key genes under different environmental conditions;

[0016] S4. Regulation of the expression of acid-resistant genes in lactic acid bacteria:

[0017] S4.1. Based on the gene expression data of lactic acid bacteria in acidic environment, select and analyze genes related to acid resistance, including Hsp genes, ATP synthase genes, and membrane proteins; improve the acid resistance of lactic acid bacteria through gene overexpression or knock-in strategy;

[0018] S4.2. Use single-cell RNA-seq technology to deeply analyze the single-cell gene expression differences of lactic acid bacteria under acid stress, identify the change pattern of acid-resistant genes, and combine gene expression analysis with system biology models to regulate the acid-resistant response of lactic acid bacteria;

[0019] S4.3. Based on genome-wide association analysis (GWAS), identify marker genes in the lactic acid bacteria genome that are strongly associated with acid resistance, and use CRISPR interference technology to modify genes in a targeted manner to enhance the adaptability of lactic acid bacteria to low pH environments.

[0020] S5. Dynamic optimization of lactic acid bacteria fermentation process based on algorithm:

[0021] S5.1. Use the deep reinforcement learning (DRL) algorithm to establish an intelligent optimization model for the lactic acid bacteria fermentation process. By collecting gene expression data, metabolic flow data, and fermentation parameter data of lactic acid bacteria, the model is trained to predict the growth of lactic acid bacteria and lactic acid production under different fermentation conditions.

[0022] Furthermore, the modeling method of the lactic acid bacteria gene expression and metabolic network includes:

[0023] A dynamic analysis method integrating whole genome data and time-series RNA-seq data is used to combine the static expression of genes with regulatory relationships, and a mathematical model is used to dynamically capture changes in gene expression. Gene expression E(t) is used as a variable that responds to changes in environmental factors, and the dynamic effects of environmental factors are introduced. The relationship between nonlinear exponential functions and environmental factor concentrations is used to describe the adaptability of gene expression to environmental changes:

[0024]

[0025] Where: E(t) represents the expression level of lactic acid bacteria gene at time point t; is the sum of all environmental factors, n is the number of environmental factors; f i (C i (t),θ) is the gene regulatory factor function of environmental factor i, and the concentration C of the environmental factor i (t) is associated with the control parameter vector θ; C i (t) is the concentration of the i-th environmental factor at time point t; is the concentration value of environmental factor i; α i is the nonlinear influence coefficient of environmental factor i on gene expression regulation; exp is an exponential function that describes the nonlinear response of gene expression to changes in environmental factors; ∈(t) is the error term, which represents the partial factor of random interference in the model.

[0026] Furthermore, the modeling method of the lactic acid bacteria gene expression and metabolic network includes: combining the time-series RNA-seq data with metabolic flux analysis (MFA), using dynamic clustering technology to reconstruct the time-series gene regulatory network; and identifying the expression patterns of gene groups under different environmental conditions.

[0027] Furthermore, the modeling method of the lactic acid bacteria gene expression and metabolic network includes: using metabolic flux analysis (MFA) and gene expression regulation through flow balance analysis (FBA) for coupling modeling to form a multi-level metabolic and gene regulation closed-loop system; metabolic flux analysis is performed by solving the flow balance in the metabolic network, and gene regulation analysis is performed by the regulatory effect of gene expression levels on enzymes.

[0028] Furthermore, the modeling method of the lactic acid bacteria gene expression and metabolic network includes:

[0029] By jointly analyzing time-resolved RNA-seq data and metabolomics data, the activity changes of transcription factors and metabolic enzymes under different environmental conditions are identified; and the relationship between the dynamic activities of transcription factors and metabolic enzymes is modeled by a nonlinear regression model:

[0030]

[0031] Where: R(t) is the combined activity of transcription factors and metabolic enzymes at time point t; T f (t) is the concentration of lactic acid bacteria metabolites including lactic acid and pyruvic acid at time point t; E f (t) is the concentration of metabolic enzymes, representing the activity of enzymes in metabolic pathways; α k is the regression coefficient, which indicates the influence weight of the k-th metabolite on transcription factors and metabolic enzymes; β k and γ k The metabolites T f (t) and metabolic enzyme E f (t) is the nonlinear exponential adjustment coefficient; ∈(t) is the error term, which represents the external factors not captured by the model.

[0032] Furthermore, the method for regulating the expression of the acid-resistant gene of lactic acid bacteria comprises:

[0033] Using single-cell RNA-seq technology, we revealed the differences in gene expression of lactic acid bacteria under acidic stress at the single-cell level and captured the characteristics of subpopulations within the cell population; we quantified the gene expression level of each cell through single-cell RNA-seq data:

[0034]

[0035] Among them, E i (t) represents the gene expression level of the ith cell at time t; f ij is the correlation coefficient between the ith cell and the jth gene, indicating the intensity of the cell's response to gene j; G j (t) is the expression level of gene j at time t.

[0036] Furthermore, the method for regulating the expression of the acid-resistant gene of lactic acid bacteria includes: combining metabolic flux analysis (MFA) with gene expression, using the flux balance analysis (FBA) model, and establishing a dynamic regulatory network including the interaction between gene regulation and metabolic flux; in the dynamic regulatory network, gene expression is affected by the feedback of metabolite concentration, and metabolic flux is also regulated by gene expression.

[0037] Furthermore, the method for regulating the expression of the acid-resistant gene of lactic acid bacteria comprises:

[0038] Based on the dynamic data of metabolic flux analysis and gene expression, a dynamic optimization model for gene regulation of lactic acid bacteria acid resistance was constructed; dynamic optimization was achieved by adjusting metabolite concentrations and gene expression changes:

[0039]

[0040] Where, ΔE i(t) represents the expression change of gene i at time t under regulation; λ is the regulation coefficient, which represents the regulation intensity of gene expression; S in and S out are the intracellular and extracellular concentrations of metabolites, respectively; is the expression rate of gene i, reflecting its dynamic changes under acidic stress conditions.

[0041] The present invention is a method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology. By combining metabolic flux analysis (MFA), flux balance analysis (FBA), single-cell RNA-seq data and dynamic regulation modeling technology, the dynamic change mechanism of gene expression and metabolic network of lactic acid bacteria in shrimp acid fermentation is revealed from multiple levels, providing a scientific and efficient method for optimizing lactic acid bacteria performance. Its beneficial effects mainly include the following aspects:

[0042] Through dynamic coupling of metabolic flux analysis and gene expression regulation, the expression levels of key genes in the lactic acid production pathway (such as ldh1) are precisely optimized, significantly improving the efficiency of lactic acid production. At the same time, the production of byproducts that compete with lactic acid production (such as pyruvate and ethanol) is reduced, optimizing the utilization of metabolic resources. Using single-cell RNA-seq technology and dynamic regulatory models, the expression of acid-resistant related genes (such as hsp1 and atp1) is identified and optimized, significantly improving the environmental adaptability of lactic acid bacteria under acidic stress conditions, enabling them to survive and metabolize more efficiently, and ensuring fermentation stability in a low pH environment.

[0043] By combining system biology modeling, a multi-level regulatory system including gene regulatory network, metabolic network and environmental response is constructed to achieve dynamic optimization of two-way feedback between gene expression and metabolic flow of lactic acid bacteria, making the performance of lactic acid bacteria more stable and efficient under complex fermentation conditions. By quantitatively analyzing the gene expression dynamics and metabolic flow changes of lactic acid bacteria at different fermentation stages, the present invention provides an accurate basis for adjusting fermentation parameters (such as pH, temperature, dissolved oxygen, etc.), supporting the intelligent and refined regulation of lactic acid fermentation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention is a flow chart of the method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology.

[0045] Figure 2 The present invention is a flowchart of the method for modeling the gene expression and metabolic network of lactic acid bacteria.

[0046] Figure 3 The present invention is a flow chart of the method for regulating the expression of the acid-resistant gene of lactic acid bacteria. DETAILED DESCRIPTION

[0047] The specific implementation modes of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0048] Combined with Figure 1 The gene expression regulation method of lactic acid bacteria in shrimp acid fermentation based on systems biology is a method that comprehensively utilizes the whole genome data, transcriptome data and metabolome data of lactic acid bacteria, and constructs the dynamic regulatory network of lactic acid bacteria under specific fermentation conditions through high-throughput sequencing technology and systems biology modeling technology. Its core lies in achieving multi-level interactive optimization between lactic acid bacteria gene expression and metabolic flow through systematic and dynamic modeling. First, in the modeling stage of lactic acid bacteria gene expression and metabolic network, the gene expression pattern of lactic acid bacteria under shrimp acid fermentation conditions is comprehensively obtained by integrating whole genome data (including gene sequence, structural variation and functional annotation) and high-throughput sequencing technologies such as RNA-seq. This process uses data-driven methods to construct the gene regulatory network of lactic acid bacteria, focusing on analyzing gene groups related to lactic acid production, acid resistance and sugar metabolism through cluster analysis (such as time series K-means clustering) and network reconstruction technology, and identifying the core transcription factors and metabolic enzymes in these key metabolic pathways. Subsequently, by introducing the dynamic regulation modeling method, a dynamic regulation model of lactic acid bacteria gene expression is established to describe how gene expression levels change with time and environmental conditions. Using software tools such as GeneNet or Cytoscape, lactic acid bacteria genomics, transcriptomics and metabolomics data are integrated into multi-level dynamic networks to model and visualize real-time changes in gene expression regulation, transcription factor activity and metabolic flux.

[0049] Combining metabolic flux analysis (MFA) with dynamic system modeling technology, a dynamic model of lactic acid bacteria metabolic flux was established to comprehensively analyze the relationship between the lactic acid production pathway and the byproduct production pathway. First, the metabolic pathway of lactic acid bacteria was quantified through metabolic flux analysis (MFA), the key metabolic nodes and rate-limiting steps of lactic acid production during fermentation were identified, and the competitive consumption and regulatory effects of byproduct production pathways (such as ethanol, pyruvate, etc.) on lactic acid production were analyzed. On this basis, dynamic system modeling technology was introduced to associate metabolic flux with time variables and environmental conditions, and a dynamic model was established to describe the changing laws of lactic acid bacteria metabolic flux at different stages of shrimp acid fermentation. At the same time, in order to further optimize metabolic flux and increase lactic acid production, the flow balance analysis (FBA) technology was used to optimize the lactic acid bacteria metabolic network. FBA describes the material flow relationship between metabolic reactions through mathematical modeling, and uses algorithms to solve metabolic flux to achieve the maximum flow of the lactic acid production pathway. In addition, in order to enhance the environmental adaptability of the model, key external environmental variables including pH, temperature, and dissolved oxygen are integrated, and the metabolic network is adjusted dynamically in real time using dynamic system optimization technology, so that lactic acid bacteria can maintain efficient flow distribution in the lactic acid production pathway under complex fermentation conditions while minimizing the generation of by-products, thereby significantly increasing lactic acid production under shrimp acid fermentation conditions and enhancing the environmental tolerance of lactic acid bacteria.

[0050] Through modern gene editing technology and synthetic biology, precise regulation and functional optimization of lactic acid bacteria gene expression can be achieved. First, CRISPR interference (CRISPRi) technology is used to efficiently silence the targeted genes in the lactic acid bacteria genome. By designing specific gRNA to guide dCas9 (Cas9 protein without cutting activity) to bind to the promoter region of the target gene, the binding of RNA polymerase or the inhibition of transcriptional elongation can be inhibited, and the expression level of the target gene can be accurately reduced. This targeted gene silencing strategy can effectively reduce the expression of genes in lactic acid bacteria that are not related to lactic acid production or related to competitive metabolic pathways, reduce the amount of by-products generated, and improve the efficiency of lactic acid production. At the same time, CRISPR / Cas9 technology is used to accurately knock in the lactic acid bacteria genome. Through double-strand breaks induced by Cas9 protein and homologous recombination of exogenous DNA templates, key metabolic enzyme genes or function-enhancing genes are inserted into the lactic acid bacteria genome, thereby enhancing the lactic acid production ability of lactic acid bacteria or improving its adaptability to shrimp acid fermentation environments (such as low pH). In addition, based on synthetic biology technology, a gene regulatory circuit containing regulator-driven elements and enhancers is designed and constructed, and a modular regulatory system is used to achieve dynamic regulation of key gene expression by lactic acid bacteria under different environmental conditions. Specifically, the regulator-driven element can sense signals based on environmental variables (such as pH, temperature, and dissolved oxygen) and trigger the expression regulation of downstream genes, while the enhancer element further amplifies the expression signal of the target gene, ensuring that lactic acid bacteria achieve the best gene expression pattern under specific fermentation conditions.

[0051] Improve the adaptability and tolerance of lactic acid bacteria in acidic environments through multi-level gene expression analysis and precise genetic engineering technology. First, through the gene expression data of lactic acid bacteria in acidic environments, systematically screen and analyze key genes closely related to acid resistance, including heat shock protein (Hsp) genes, ATP synthase genes, and membrane protein genes. These genes play an important role in lactic acid bacteria's response to acidic stress. For example, Hsp genes enhance cell survival by protecting protein stability and folding function, ATP synthase genes improve energy supply in acidic environments by maintaining cell energy metabolism, and membrane protein genes improve cell acid tolerance by regulating cell membrane permeability. Targeting these key genes, lactic acid bacteria are precisely modified through gene overexpression or knock-in strategies, thereby improving the acid resistance level of lactic acid bacteria. On this basis, single-cell RNA-seq technology was used to deeply analyze the differences in single-cell gene expression of lactic acid bacteria under acidic stress, reveal the response patterns of different cells in the lactic acid bacteria population under acidic stress, identify the dynamic change patterns of acid-resistant genes, and combine the system biology model to quantitatively analyze the functions of these genes, further explore the dynamic relationship between gene expression, metabolic flow and environmental factors, and form a set of directional regulation methods for the acid-resistant response of lactic acid bacteria. In addition, based on genome-wide association analysis (GWAS), marker genes related to strong acid resistance were screened from the lactic acid bacteria genome, and potential functional genes that can significantly affect acid resistance were identified by integrating genomic variation and phenotypic data, and these genes were directed to be modified in combination with CRISPR interference technology. CRISPR interference inhibits the binding of RNA polymerase by designing specific gRNA to bind to the promoter region of the marker gene, reducing the expression of non-critical genes while enhancing the functional performance of critical genes.

[0052] An intelligent optimization model of the lactic acid bacteria fermentation process was established through the deep reinforcement learning (DRL) algorithm to achieve dynamic optimization of lactic acid bacteria fermentation conditions and maximize lactic acid production. First, a systematic data acquisition technology was used to comprehensively collect multi-dimensional data generated by lactic acid bacteria during the fermentation process, including gene expression data (such as the dynamic expression of key genes revealed by RNA-seq data), metabolic flux data (using metabolic flux analysis MFA to describe the dynamic characteristics of lactic acid production pathways and by-product generation) and key fermentation parameter data (such as environmental variables such as pH, temperature, and dissolved oxygen). These data were used as input to provide multi-dimensional behavioral information of lactic acid bacteria under different fermentation conditions. Subsequently, the deep reinforcement learning (DRL) algorithm was used to train the intelligent optimization model, in which the DRL model constructed a closed-loop architecture of "environment-decision-feedback" and used neural networks to capture nonlinear relationships and complex dynamic changes in the lactic acid bacteria fermentation process. In this model, the environment consists of the current fermentation state of lactic acid bacteria and key variables, including the gene expression level of lactic acid bacteria, metabolic flow characteristics and external environmental parameters. The decision is defined by the fermentation condition adjustment strategy generated by the model, such as the temperature adjustment range, pH control range and dissolved oxygen supply rate. The feedback is evaluated based on the real-time performance of lactic acid production and bacterial growth status. The reward function is designed with the goal of maximizing lactic acid production and minimizing by-product generation. Through large-scale data input and iterative training, the DRL model gradually learns the growth law and metabolic performance of lactic acid bacteria under different fermentation conditions. It can eventually predict the growth trend and lactic acid production of lactic acid bacteria at any fermentation stage, and generate targeted optimization strategies to achieve dynamic regulation of the lactic acid fermentation process.

[0053] Embodiment 1:

[0054] Combined with Figure 2 In the shrimp acid fermentation process, the key goal of lactic acid bacteria is to increase lactic acid production while minimizing byproduct generation. The gene expression of lactic acid bacteria is significantly affected by external environmental factors (such as pH, temperature, and dissolved oxygen concentration). The research team hopes to use modeling methods to dynamically analyze the effects of these environmental factors on the expression of key genes for acid resistance and lactic acid production in lactic acid bacteria, thereby optimizing fermentation conditions.

[0055] The dynamic formula for gene expression is:

[0056]

[0057] in:

[0058] E(t): The expression level of key genes of lactic acid bacteria at time t.

[0059] n: The number of types of environmental factors, n=3 (corresponding to pH value, temperature, and dissolved oxygen concentration).

[0060] fi (C i (t),θ): The control function describing the environmental factor i, defined as the linear relationship f i (C i (t),θ)=θ i ·C i (t).

[0061] C i (t): The concentration of environmental factor i at time t, set in the pH range of 3.5-5.5, the temperature range of 30°C-40°C, and the dissolved oxygen concentration range of 10%-40%.

[0062] C max,i : The maximum value of environmental factor i (used for normalization), take C max,pH =5.5, C max,温度 =40, C max,溶氧 =40. i : Nonlinear influence coefficient of environmental factor i, set α pH =0.8,α 温度 =0.5, α 溶氧 =0.6. ∈(t): random error, taken

[0063] Example calculation: Set at a certain time t = 10min, the environmental parameters are: pH value is 4.5, temperature is 35℃, and dissolved oxygen concentration is 25%.

[0064] 1. Calculate the normalized concentration of each environmental factor:

[0065]

[0066] 2. Calculate the control function of each environmental factor:

[0067] The control parameter vector θ is set to [0.9, 0.7, 0.8] (corresponding to pH value, temperature and dissolved oxygen concentration, respectively).

[0068] f pH (C pH (t),θ)=θ pH ·C pH (t) = 0.9 4.5 = 4.05

[0069] f 温度 (C 温度 (t),θ)=θ 温度 ·C 温度 (t) = 0.7 35 = 24.5

[0070] f 溶氧 (C 溶氧 (t),θ)=θ溶氧 ·C 溶氧 (t) = 0.8 25 = 20

[0071] 3. Substitute the formula to calculate the exponential function of each factor:

[0072]

[0073] 4. Sum and add the error term:

[0074] E(t)=f pH (C pH (t),θ)·1.924+f 温度 (C 温度 (t),θ)·1.548+f 溶氧 (C 溶氧 (t),θ)·1.455+∈(t)

[0075] Assume random error∈(t)=0.05:

[0076] E(t)=4.05·1.924+24.5·1.548+20·1.455+0.05

[0077] E(t)=7.79+37.92+29.1+0.05≈74.86

[0078] Results analysis: By substituting specific data, the gene expression pattern of lactic acid bacteria during the fermentation process can be dynamically captured, and the effects of environmental factors (such as pH, temperature, and dissolved oxygen concentration) on the expression of key genes of lactic acid bacteria can be quantified. This model provides accurate data support and theoretical basis for optimizing fermentation conditions. For example, by adjusting the pH value to the optimal range (such as 4.5 to 5.0), the lactic acid production pathway can be further optimized, thereby increasing lactic acid production and reducing by-product production.

[0079] In this case, the combination of time-series RNA-seq data and metabolic flux analysis (MFA) was introduced, and dynamic clustering technology was used to reconstruct the time-series gene regulatory network to optimize fermentation conditions and improve lactic acid production efficiency and acid resistance. During the shrimp acid fermentation process, the research team collected time-series RNA-seq data and metabolic flux data of lactic acid bacteria through multiple experiments. Under different environmental conditions (such as a combination of pH, temperature, and dissolved oxygen concentration), the expression levels of multiple key genes of lactic acid bacteria were measured. The purpose was to identify the dynamic expression patterns of genes related to lactic acid production, acid resistance, and sugar metabolism through cluster analysis, as well as the impact of different environmental conditions on the gene regulatory network.

[0080] Dynamic clustering optimization formula:

[0081]

[0082] in:

[0083] x i (t): The expression level of gene i at time point t (taken from RNA-seq data).

[0084] C j : The jth cluster center, representing the average expression pattern of all genes in the cluster.

[0085] |x i (t)C j | 2 : The square error between gene expression and cluster center, which measures the similarity between gene and cluster.

[0086] λ: Regularization parameter, which controls the smoothness of cluster center update. Its value range is [0.1, 10]. In this example, λ is set to 1. θ j : The regulatory factor vector of the jth class, representing the common regulatory features of genes in the cluster, with an initial value of θ0 = [1,1,1].

[0087] k: number of clusters, set to k=3 (lactic acid production, acid resistance, and sugar metabolism are set to form three main clusters respectively).

[0088] Specific data: The experimental team collected time-series RNA-seq data under three environmental conditions (pH: 4.5, temperature: 35°C, dissolved oxygen concentration: 25%), and recorded the changes in the expression levels of lactate production genes (such as ldh1), acid resistance genes (such as hsp1), and sugar metabolism genes (such as pfk) over time.

[0089] x ldh1 (t) = [5.2, 5.8, 6.0, 6.5] (corresponding to t = [0, 10, 20, 30] minutes)

[0090] x hsp1 (t) = [3.5, 3.8, 4.2, 4.0]

[0091] x pfk (t) = [4.8, 5.0, 5.1, 5.4]

[0092] Calculation steps:

[0093] 1. Initialize the cluster center: Set the initial cluster center to C = [4.0, 5.0, 4.5] corresponding to three clusters (lactic acid production, acid resistance, and sugar metabolism).

[0094] 2. Calculate the squared error term:

[0095]

[0096] Similarly:

[0097] |x hsp1 (t)C2| 2 =1.74,|x pfk (t)C3| 2 =2.61

[0098] 3. Regularization term: Assume the initial control vector θ0 = [1, 1, 1], set the updated control vector θ = [1.1, 0.9, 1.2], and calculate the regularization term:

[0099] λ·|θ j θ0| 2 =1·[(1.11) 2 +(0.91) 2 +(1.21) 2 ]=0.14

[0100] 4. Calculate the optimization objective function:

[0101]

[0102] 5. Update cluster center and control vector: Based on the principle of minimizing the objective function, adjust the cluster center C j and the control vector θ j , calculate the iterative cluster assignments and optimize the results.

[0103] Results and analysis: Through dynamic clustering, the expression patterns of lactate production genes (such as ldh1), acid resistance genes (such as hsp1), and sugar metabolism genes (such as pfk) were identified as three main categories. Adjusting environmental conditions (such as increasing dissolved oxygen concentration to 30%), the model predicted that the expression of lactate production-related genes would increase by x ldh1 (t) increased by about 15%, and the final lactic acid production increased by about 12%.

[0104] In this case, a coupled modeling method of metabolic flux analysis (MFA) and flux balance analysis (FBA) of gene expression regulation was introduced to achieve dynamic optimization of the lactic acid bacteria fermentation process by constructing a multi-level metabolic and gene regulation closed-loop system.

[0105] In the shrimp acid fermentation experiment, the goal is to improve the efficiency of lactic acid production and reduce the production of by-products (such as ethanol and pyruvate) by optimizing the metabolic flow and gene expression network of lactic acid bacteria. The experimental team obtained the metabolic pathway flow rate of lactic acid bacteria through metabolic flux analysis (MFA), and used RNA-seq data to evaluate gene expression levels. In order to optimize the lactic acid production pathway, the researchers constructed a coupling model of metabolic flow and gene regulation, and dynamically adjusted the fermentation conditions by optimizing the objective function.

[0106] Coupling model optimization formula:

[0107]

[0108] in:

[0109] The overall control effect at time t.

[0110] Flux j : The flow rate of the jth metabolic pathway, indicating the conversion rate of metabolites (unit: mmol / gDW / h).

[0111] α j : The weight of metabolic pathway j, ranging from [0.5, 1.5]. i (t): The actual expression level of the ith gene at time t. i : The expected expression level of the ith gene (in FPKM), which is determined by the metabolic demand of lactic acid bacteria or the concentration of the target product. λ: Regularization factor, used to balance the contribution of metabolic flux and gene expression, with a value range of [0.1, 1]. n, m: The number of genes and metabolic pathways, respectively, with n = 3 and m = 2 (considering the two main metabolic pathways of lactic acid production and ethanol production).

[0112] Specific data:

[0113] Set metabolic pathways including lactate production pathway (flux L ) and ethanol production pathway (Flux E ), gene expression level E i (t) includes lactate dehydrogenase gene (ldh1), sugar metabolism gene (pfk) and energy metabolism gene (atp1). The data of the experiment at time t = 30 minutes are as follows:

[0114] Lactate production pathway Flux L =8.5mmol / gDW / h, weight α L =1.2.

[0115] Ethanol production pathway Flux E =2.0mmol / gDW / h, weight α E =0.8.

[0116] Gene expression level E ldh1 (t)=5.2,E pfk (t)=4.8,E atp1 (t) = 3.5.

[0117] Expected expression level C ldh1 =6.0,C pfk =5.0,C atp1 =4.0.

[0118] The regularization parameter λ=0.5.

[0119] Calculation steps:

[0120] 1. Metabolic pathway weighting and calculation:

[0121]

[0122] 2. Calculation of the sum of squares of deviations between gene expression and expected expression:

[0123]

[0124] 3. Regularization adjustment calculation:

[0125]

[0126] 4. Optimize the objective function calculation:

[0127]

[0128] Results and Analysis: Optimization Objective Function The value of is 12.265, indicating that the overall regulation efficiency is high under the current conditions (time t = 30 minutes, metabolic pathway and gene expression level). However, there is still a certain deviation in the metabolic flux (Flux_L) and expression of key genes (ldh1) in the lactic acid production pathway. By adjusting the fermentation conditions, such as increasing the dissolved oxygen concentration or temperature, the expression of the ldh1 gene can be promoted to reach the expected value C ldh1 =6.0, which can further optimize lactic acid production.

[0129] This case jointly analyzes time-resolved RNA-seq data and metabolomics data to establish a nonlinear regression model of the dynamic activity of transcription factors and metabolic enzymes. Through practical demonstrations, it shows how to combine metabolite concentrations and enzyme activity to optimize the lactic acid production and acid resistance of lactic acid bacteria.

[0130] In the shrimp acid fermentation experiment, the research team collected time-resolved RNA-seq data and metabolomics data of lactic acid bacteria under different environmental conditions (such as pH, temperature, and dissolved oxygen concentration). The goal is to reveal the key regulatory mechanisms of the lactic acid production pathway by modeling the dynamic activities of transcription factors and metabolic enzymes, and optimize fermentation conditions to increase lactic acid production.

[0131] Nonlinear regression model formula:

[0132]

[0133] in:

[0134] R(t): The combined activity of transcription factors and metabolic enzymes at time t.

[0135] T f (t): The concentration of lactic acid bacteria metabolites (such as lactic acid and pyruvic acid) at time t (unit: mmol / L).

[0136] E f (t): Metabolic enzyme concentration, representing the activity of key enzymes of lactic acid bacteria (unit: U / mg).

[0137] α k : Regression coefficient, which represents the weight of the influence of metabolites on activity, and its value range is [0.1,2].

[0138] β k , γ k : Nonlinear exponential adjustment coefficient of metabolites and metabolic enzymes, with value ranges of [0.5, 2] and [0.5, 1.5] respectively. ∈(t): Error term, set

[0139] Specific data:

[0140] Metabolites include lactate (T 乳酸 (t)) and pyruvate T 丙酮酸 (t), its concentration and the activity of related enzymes (such as lactate dehydrogenase and pyruvate dehydrogenase) change with time t as follows:

[0141] T 乳酸 (t)=[2.0,3.5,4.8,5.6]mmol / L, T 丙酮酸 (t)=[0.8,1.2,1.5,1.8]mmol / L.

[0142] E 乳酸脱氢酶 (t)=[10.5,12.0,13.5,14.0]U / mg,E 丙酮酸脱氢酶 (t)=[5.5,6.0,6.3,6.5]U / mg.

[0143] Regression coefficient α 乳酸 =1.2,α 丙酮酸 =0.8.

[0144] Exponential adjustment coefficient β 乳酸 =1.5,β 丙酮酸 =0.9,γ 乳酸脱氢酶 =1.2,γ 丙酮酸脱氢酶 =0.7.

[0145] Calculation steps:

[0146] 1. Calculate the contribution of lactate to activity:

[0147]

[0148] Take t=2 as an example:

[0149] contribute 乳酸 =1.2·3.5 1.5 12.0 1.2

[0150] =1.2·6.59·15.36≈121.34

[0151] 2. Calculate the contribution of pyruvate to activity:

[0152]

[0153] Take t=2 as an example:

[0154] contribute 丙酮酸 =0.8·1.2 0.9 6.0 0.7

[0155] =0.8·1.10·4.48≈3.94

[0156] 3. Calculate the comprehensive activity:

[0157] R(t) = Contribution 乳酸 +Contribute 丙酮酸 +∈(t)

[0158] Assume random error∈(t)=0.02:

[0159] R(2)=121.34+3.94+0.02=125.30

[0160] 4. Analyze dynamic changes:

[0161] Repeat the above calculation steps, calculate R(t) for each moment t = [1, 2, 3, 4] minutes, and obtain the dynamic change of comprehensive activity over time.

[0162] At t = 2, the contribution of lactate was significantly higher than that of pyruvate, indicating that the lactate metabolic pathway was the main direction of metabolic regulation at this moment. The efficiency of lactate production can be further improved by increasing the activity of lactate dehydrogenase or increasing the flow rate of the lactate metabolic pathway. In addition, the model revealed a nonlinear relationship between metabolite concentration and enzyme activity, indicating that regulating the activity of metabolic enzymes has the potential to amplify metabolic effects.

[0163] Embodiment 2:

[0164] Combined with Figure 3, through single-cell RNA-seq technology, it is demonstrated how to reveal the differences in gene expression of lactic acid bacteria under acidic stress at the single-cell level, and verify the feasibility and practical value of the method through formula calculation. In the shrimp acid fermentation experiment, the research team collected single-cell RNA-seq data of lactic acid bacteria under acidic stress (pH = 4.5). The goal is to reveal the heterogeneity within the lactic acid bacteria cell population, especially the characteristics of subpopulations with strong acid resistance, and the dynamic expression patterns of key genes (such as Hsp1, ldh1, pfk1). The gene expression level of each cell is quantified through single-cell data, and the cell subpopulations whose gene expression is related to acid resistance are determined.

[0165] Gene expression formula:

[0166]

[0167] in:

[0168] E i (t): Gene expression level of the ith cell at time t.

[0169] f ij : The response intensity of the ith cell to the jth gene (value range: 0.1≤f ij ≤1.0).

[0170] G j (t): The expression level of the jth gene at time t (unit: FPKM, value range: 1≤G j (t)≤10).

[0171] n: total number of genes, set to n=3 (Hsp1, ldh1, pfk1).

[0172] Specific data: Under an acidic stress environment, the gene expression and cell response intensity shown by RNA-seq data are as follows:

[0173] G Hsp1 (t) = [5.0, 5.5, 6.0, 6.2] (unit: FPKM, changes with time t = [10, 20, 30, 40] minutes).

[0174] G ldh1 (t) = [7.0, 7.2, 7.8, 8.0].

[0175] G pfk1 (t) = [3.5, 3.8, 4.0, 4.5].

[0176] The response intensity of the i=1th cell:

[0177] Calculation steps:

[0178] 1. Calculate the gene expression level of the i=1th cell at t=20 minutes:

[0179]

[0180] Substitute the data:

[0181] E1(20)=0.8·5.5+0.9·7.2+0.6·3.8

[0182] E1(20)=4.4+6.48+2.28=13.16

[0183] 2. Calculate the gene expression level of the i=1th cell at other time points:

[0184] Repeat the above calculation method to obtain:

[0185] E1(10)=0.8·5.0+0.9·7.0+0.6·3.5=4.0+6.3+2.1=12.4

[0186] E1(30)=0.8·6.0+0.9·7.8+0.6·4.0=4.8+7.02+2.4=14.22

[0187] E1(40)=0.8·6.2+0.9·8.0+0.6·4.5=4.96+7.2+2.7=14.86

[0188] 3. Analyze gene expression trends:

[0189] As time t increases, the gene expression level of the i=1th cell increases from E1(10)=12.4 to E1(40)=14.86, indicating that the adaptability of lactic acid bacteria under acidic stress conditions is enhanced. Hsp1 and G ldh1 The high expression of β-lactate significantly improved the acid resistance and lactic acid production ability of the cells.

[0190] Results and analysis: Through single-cell RNA-seq data and the above formula calculation, the gene expression pattern of lactic acid bacteria in acidic environment can be clearly quantified and the heterogeneity within the cell population can be revealed. The experimental results show that the i=1 cell exhibits strong acid resistance characteristics, and its E1(t) value continues to increase, indicating that the high expression of key genes is closely related to acid resistance.

[0191] In this case, the flow balance analysis (FBA) model was used to establish a dynamic regulatory network that includes the interaction of gene regulation and metabolic flux. The practical application and feasibility of the formula were demonstrated through example calculations. During the shrimp acid fermentation process, the research team hopes to optimize the acid resistance and lactic acid production ability of lactic acid bacteria. To achieve this goal, the team used metabolic flux analysis (MFA) to measure the concentration changes of metabolites (lactic acid and pyruvate) during fermentation, and used RNA-seq data to dynamically monitor the expression levels of key genes for lactic acid production (such as ldh1) and acid-resistant genes (such as hsp1) and their regulatory relationships by metabolites. This dynamic regulatory network aims to reveal the interaction between metabolic flux and gene expression and optimize the metabolic efficiency of lactic acid bacteria in an acidic environment.

[0192] Dynamic formula of metabolic flux and gene expression:

[0193]

[0194] in:

[0195] The expression rate change of the ith gene at time t (unit: FPKM / min).

[0196] α ik : The regulatory coefficient of transcription factor k on gene i, ranging from [0.1, 1.0].

[0197] E k (t): The expression level of transcription factor k at time t (unit: FPKM, value range: [1,10]).

[0198] S k : Concentration of metabolite k (unit: mmol / L, value range: 0.1, 10).

[0199] K k : The half-saturation constant of the metabolite, ranging from [0.5, 2.0].

[0200] Specific data:

[0201] Gene G ldh1 and G hsp1 Expression rate changes To optimize the goal.

[0202] Transcription factor: E tf1 (t) = [2.0, 3.0, 4.0], E tf2 (t) = [1.5, 2.5, 3.5].

[0203] Metabolite concentration: S 乳酸 (t) = [5.0, 6.0, 7.0] mmol / L, S 丙酮酸(t) = [2.0, 2.5, 3.0] mmol / L.

[0204] Half-saturation constant: K 乳酸 =1.0, K 丙酮酸 =0.8.

[0205] Control coefficient: α ldh1,tf1 =0.8,α ldh1,tf2 =0.6,α hsp1,tf1 =0.7,α hsp1,tf2 =0.5.

[0206] Calculation steps:

[0207] 1. Calculate lactate production gene G ldh1 Rate change at t = 2:

[0208]

[0209] Substitute the data:

[0210]

[0211] 2. Calculation of acid resistance gene G hsp1 Rate change at t = 2:

[0212]

[0213] Substitute the data:

[0214]

[0215] 3. Analysis results:

[0216] At t = 2, the lactate production gene G ldh1 The rate change was 3.193 FPKM / min, indicating that the regulation efficiency of the lactate production pathway was high. hsp1 The rate change was 2.748 FPKM / min, indicating that lactic acid bacteria have strong environmental adaptability under acidic stress conditions.

[0217] Results and analysis: Through the above calculations, the dynamic regulatory network can effectively quantify the bidirectional regulatory relationship between metabolite concentration and gene expression during the fermentation process of lactic acid bacteria. The efficiency of the lactic acid production pathway is directly dependent on the lactate dehydrogenase gene G ldh1 The expression of the acid resistance gene G hsp1 The increased expression of enhances the environmental adaptability of lactic acid bacteria.

[0218] In this case, a dynamic optimization model for the regulation of acid resistance genes in lactic acid bacteria was constructed. The practical application and feasibility of this method were verified through example calculations, showing how to regulate gene expression and metabolic flow through dynamic optimization during the fermentation process.

[0219] In the shrimp acid fermentation experiment, the research team measured the metabolite concentration inside and outside the cell of lactic acid bacteria under acidic stress (pH = 4.5) and the dynamic change data of gene expression, with the goal of optimizing the expression level of key acid-resistant genes (such as hsp1 and atp1), thereby enhancing the adaptability of lactic acid bacteria to acidic environments and improving the efficiency of lactic acid production. The team used a formula model to describe the dynamic relationship between metabolite concentration and gene expression rate, and adjusted gene expression to achieve acid resistance optimization.

[0220] Dynamic optimization formula:

[0221]

[0222] in:

[0223] ΔE i (t): Expression change of gene i at time t under regulation (unit: FPKM).

[0224] λ: Regulation coefficient, indicating the intensity of regulation on gene expression, with a value range of [0.1, 1.0].

[0225] S in , S out : The intracellular and extracellular concentrations of metabolites (unit: mmol / L).

[0226] The expression rate of gene i (unit: FPKM / min), indicating its dynamic changes.

[0227] Specific data:

[0228] Gene G hsp1 : Acid resistance key gene, its initial expression rate is

[0229] Gene G atp1 : Energy metabolism gene, with an initial expression rate of

[0230] Metabolite lactate: intracellular concentration S in =5.0mmol / L, extracellular concentration S out =2.0mmol / L.

[0231] Control coefficient: λ=0.8.

[0232] Calculation steps:

[0233] 1. Calculate gene G hsp1 Dynamic optimization expression changes:

[0234]

[0235] Substitute the data:

[0236]

[0237] 2. Calculate gene G atp1 Dynamic optimization expression changes:

[0238]

[0239] Substitute the data:

[0240]

[0241] 3. Changes in gene expression after optimization:

[0242] At time t = 10 minutes, gene G hsp1 The expression level of gene G increased by 1.0 FPKM, indicating that the acid resistance gene response was significantly enhanced. atp1 The expression level increased by 0.6 FPKM, indicating that the energy metabolism capacity was also improved to a certain extent.

[0243] By adjusting the intracellular and extracellular concentration difference of the metabolite lactate and increasing the regulation coefficient λ, the key gene G for acid resistance was significantly optimized. hsp1 and energy metabolism gene G atp1 The optimized model showed that lactic acid bacteria could adapt to acidic environment more effectively while maintaining efficient metabolic activity. The dynamic response of acid-resistant gene expression ensured the stable operation of lactic acid bacteria under acidic fermentation conditions.

[0244] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology, characterized in that The following steps are involved: S1. Modeling of gene expression and metabolic network of lactic acid bacteria: S1.

1. Use whole genome data and high-throughput sequencing technologies including RNAseq to construct the gene regulatory network of lactic acid bacteria; identify transcription factors and enzymes related to key metabolic pathways of lactic acid production, acid resistance, and sugar metabolism through cluster analysis and network reconstruction, and establish a dynamic regulatory model of gene expression; S1.

2. Using GeneNet or Cytoscape software tools, the genomics, transcriptomics and metabolomics data of lactic acid bacteria are integrated into a multi-level network to construct a metabolic regulation system, which includes gene expression regulation and real-time changes in metabolic flux; S2. Model construction for optimizing the metabolic pathway of lactic acid bacteria: S2.

1. Based on metabolic flux analysis (MFA) and dynamic system modeling technology, a dynamic metabolic flux model of lactic acid bacteria was constructed to analyze the relationship between the lactic acid production pathway and the byproduct production pathway. S2.

2. Optimize the metabolic network of lactic acid bacteria using flow balance analysis (FBA), solve the metabolic flow through the algorithm, and dynamically optimize the metabolic network in combination with external environmental conditions including pH, temperature, and dissolved oxygen; S3. Control of gene expression regulation of lactic acid bacteria: S3.

1. Targeted gene silencing in the lactic acid bacteria genome using CRISPR interference technology; gene knock-in using CRISPR / Cas9 technology; S3.

2. Based on synthetic biology, design gene regulatory circuits including regulator-driven elements and enhancers to enable lactic acid bacteria to regulate the expression of key genes under different environmental conditions; S4. Regulation of the expression of acid-resistant genes in lactic acid bacteria: S4.

1. Based on the gene expression data of lactic acid bacteria in acidic environment, select and analyze genes related to acid resistance, including Hsp genes, ATP synthase genes, and membrane proteins; improve the acid resistance of lactic acid bacteria through gene overexpression or knock-in strategy; S4.

2. Use single-cell RNA-seq technology to deeply analyze the single-cell gene expression differences of lactic acid bacteria under acid stress, identify the change pattern of acid-resistant genes, and combine gene expression analysis with system biology models to regulate the acid-resistant response of lactic acid bacteria; S4.

3. Based on genome-wide association analysis (GWAS), identify marker genes in the lactic acid bacteria genome that are strongly associated with acid resistance, and use CRISPR interference technology to modify genes in a targeted manner to enhance the adaptability of lactic acid bacteria to low pH environments. S5. Dynamic optimization of lactic acid bacteria fermentation process based on algorithm: S5.

1. Use the deep reinforcement learning (DRL) algorithm to establish an intelligent optimization model for the lactic acid bacteria fermentation process. By collecting gene expression data, metabolic flow data, and fermentation parameter data of lactic acid bacteria, the model is trained to predict the growth of lactic acid bacteria and lactic acid production under different fermentation conditions.

2. The method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology according to claim 1, characterized in that The modeling method of the lactic acid bacteria gene expression and metabolic network includes: A dynamic analysis method integrating whole genome data and time-series RNA-seq data is used to combine the static expression of genes with regulatory relationships, and a mathematical model is used to dynamically capture changes in gene expression. Gene expression E(t) is used as a variable that responds to changes in environmental factors, and the dynamic effects of environmental factors are introduced. The relationship between nonlinear exponential functions and environmental factor concentrations is used to describe the adaptability of gene expression to environmental changes: Where: E(t) represents the expression level of lactic acid bacteria gene at time point t; is the sum of all environmental factors, n is the number of environmental factors; f i (C i (t),θ) is the gene regulatory factor function of environmental factor i, and the concentration C of the environmental factor i (t) is associated with the control parameter vector θ; C i (t) is the concentration of the i-th environmental factor at time point t; is the concentration value of environmental factor i; α i is the nonlinear influence coefficient of environmental factor i on gene expression regulation; exp is an exponential function that describes the nonlinear response of gene expression to changes in environmental factors; ∈(t) is the error term, which represents the partial factor of random interference in the model.

3. The method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology according to claim 2, characterized in that The modeling method of the lactic acid bacteria gene expression and metabolic network includes: combining time-series RNA-seq data with metabolic flux analysis (MFA), using dynamic clustering technology to reconstruct the time-series gene regulatory network; and identifying the expression patterns of gene groups under different environmental conditions.

4. The method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology according to claim 3, characterized in that The modeling method of the gene expression and metabolic network of lactic acid bacteria includes: coupling modeling using metabolic flux analysis (MFA) and gene expression regulation through flow balance analysis (FBA) to form a multi-level metabolic and gene regulation closed-loop system; metabolic flux analysis is performed by solving the flow balance in the metabolic network, while gene regulation analysis is performed by regulating the enzyme through gene expression levels.

5. The method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology according to claim 4, characterized in that The modeling method of the lactic acid bacteria gene expression and metabolic network includes: By jointly analyzing time-resolved RNA-seq data and metabolomics data, the activity changes of transcription factors and metabolic enzymes under different environmental conditions are identified; and the relationship between the dynamic activities of transcription factors and metabolic enzymes is modeled by a nonlinear regression model: Where: R(t) is the combined activity of transcription factors and metabolic enzymes at time point t; T f (t) is the concentration of lactic acid bacteria metabolites including lactic acid and pyruvic acid at time point t; F f (t) is the concentration of metabolic enzymes, representing the activity of enzymes in metabolic pathways; α k is the regression coefficient, which indicates the influence weight of the k-th metabolite on transcription factors and metabolic enzymes; β k and γ k The metabolites T f (t) and metabolic enzyme E f (t) is the nonlinear exponential adjustment coefficient; ∈(t) is the error term, which represents the external factors not captured by the model.

6. The method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology according to claim 1, characterized in that The method for regulating the expression of the lactic acid bacteria acid-resistant gene comprises: Using single-cell RNA-seq technology, we revealed the differences in gene expression of lactic acid bacteria under acidic stress at the single-cell level and captured the characteristics of subpopulations within the cell population; we quantified the gene expression level of each cell through single-cell RNA-seq data: Among them, E i (t) represents the gene expression level of the ith cell at time t; f ij is the correlation coefficient between the ith cell and the jth gene, indicating the intensity of the cell's response to gene j; G j (t) is the expression level of gene j at time t.

7. The method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology according to claim 6, characterized in that The method for regulating the expression of the acid-resistant gene of lactic acid bacteria comprises: combining metabolic flux analysis (MFA) with gene expression, adopting the flux balance analysis (FBA) model, and establishing a dynamic regulatory network including the interaction between gene regulation and metabolic flux; in the dynamic regulatory network, gene expression is affected by the feedback of metabolite concentration, and metabolic flux is also regulated by gene expression.

8. The method for regulating gene expression of lactic acid bacteria in shrimp acid fermentation based on systems biology according to claim 7, characterized in that The method for regulating the expression of the lactic acid bacteria acid-resistant gene comprises: Based on the dynamic data of metabolic flux analysis and gene expression, a dynamic optimization model for gene regulation of acid resistance in lactic acid bacteria was constructed; dynamic optimization was achieved by adjusting metabolite concentrations and gene expression changes.

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