Prediction method and system for testicular sertoli cells damaged by vomitoxin

Abstract

The invention provides a method and a system for predicting vomitoxin damaged testicular sertoli cells, and relates to the technical field of testicular sertoli cell damage prediction.The method comprises the following steps: obtaining transcriptome sequencing, immunofluorescence quantification, electron microscope ultrastructure and flow detection data; extracting topological features of a gene expression space based on a continuous homology theory and encoding the topological features into continuous bar codes for expression; performing multi-modal fusion and dynamic evolution modeling on the bar code, the protein and the structural data through a neural differential equation to obtain topological dynamic characteristics representing the barrier function; constructing a cell state phase change model by combining flow data and applying a cellular automaton and a seepage theory, and simulating to obtain a critical damage threshold of the integrity of the blood testis barrier; constructing a potential well model based on the threshold value and the streaming data; and finally, calculating the damage probability of the cells under different toxin concentrations by adopting path integration and variational methods. According to the method, early warning and dynamic risk prediction of the testicular sertoli cells damaged by the vomitoxin are realized.

Description

Technical Field

[0001] This invention relates to the field of testicular Sertoli cell damage prediction technology, and more specifically, to a method and system for predicting testicular Sertoli cell damage caused by vomitoxin. Background Technology

[0002] Vomitoxin is one of the most prevalent mycotoxins globally, with persistently high detection rates in feed and raw materials, posing a serious threat to the reproductive performance of breeding livestock and poultry. Studies have shown that vomitoxin can penetrate the blood-testis barrier, directly affecting testicular Sertoli cells. By disrupting the tight junctions between cells, it impairs barrier function, leading to spermatogenesis disorders and male infertility. Currently, the assessment of this toxic effect mainly relies on traditional in vitro cytotoxicity experiments and in vivo histopathological analysis. These methods have significant limitations: firstly, they can only perform endpoint detection after exposure, failing to provide early warning and dynamic risk prediction; secondly, traditional methods only provide static, isolated toxicological phenotypic data, making it difficult to reveal the continuous dynamic evolution from gene regulatory network disorder to cell population dysfunction, and lacking the ability to quantify and model the critical threshold of toxic damage, resulting in insufficient accuracy and timeliness in risk assessment.

[0003] Therefore, there is a need for a method and system for predicting testicular Sertoli cell damage caused by vomitoxin, in order to solve the above-mentioned technical problems. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for predicting testicular Sertoli cell damage caused by vomitoxin, thereby improving the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows: In a first aspect, this application provides a method for predicting testicular Sertoli cell damage caused by vomitoxin, including: Transcriptome sequencing data of testicular tissue from male animals under vomitoxin exposure conditions, immunofluorescence quantitative data of tight junction proteins of supporting cells, electron microscopy images of the blood-testis barrier, and flow cytometry data of apoptosis and cell cycle of supporting cells at different toxin concentrations were obtained. Feature extraction was performed on the transcriptome sequencing data based on the persistent cohomology theory, and the extracted topological features were encoded to obtain a persistent barcode representation of the gene regulatory network. The persistent barcode representation, immunofluorescence quantitative data, and electron microscopy image data are fused and processed, and the fused data are then modeled using a preset neural differential equation to obtain a topological dynamic characterization that supports cell barrier function. Based on the aforementioned topological dynamics characterization and the aforementioned flow cytometry data, a cell state phase transition model is constructed using cellular automata and percolation theory. Based on the cell state phase transition model, cell state simulation is performed to obtain the critical damage threshold for the integrity of the blood-testis barrier. A potential well model is constructed based on the critical damage threshold and the flow cytometry data to obtain a potential well model of toxin dose-barrier damage. The potential well model was processed using path integral and variational methods to generate the probability value of testicular Sertoli cell damage for each toxin concentration.

[0005] Secondly, this application also provides a predictive system for vomiting toxin-induced damage to testicular Sertoli cells, comprising: The acquisition unit is used to acquire transcriptome sequencing data of testicular tissue from male animals under vomitoxin exposure conditions, immunofluorescence quantitative data of tight junction proteins of supporting cells, electron microscopy image data of the blood-testis barrier, and flow cytometry data of apoptosis and cell cycle of supporting cells at different toxin concentrations. The extraction unit is used to extract features from the transcriptome sequencing data based on the persistent cohomology theory, and to encode the extracted topological features to obtain a persistent barcode representation of the gene regulatory network. The modeling unit is used to fuse the persistent barcode representation, immunofluorescence quantitative data and electron microscopy image data, and to perform feature evolution modeling on the fused data driven by a preset neural differential equation to obtain a topological dynamic characterization that supports cell barrier function. The simulation unit is used to construct a cell state phase transition model based on the topological dynamics characterization and the flow cytometry detection data, using cellular automata and percolation theory, and to perform cell state simulation based on the cell state phase transition model to obtain the critical damage threshold for the integrity of the blood-testis barrier. The construction unit is used to construct a potential well model based on the critical damage threshold and the flow cytometry detection data to obtain a potential well model of toxin dose-barrier damage. The processing unit is used to process the potential well model based on path integral and variational method to generate the damage probability value of testicular supporting cells at each toxin concentration.

[0006] The beneficial effects of this invention are as follows: This invention integrates multi-dimensional data from transcriptomics, proteomics, and cell morphology, and utilizes neural differential equations to construct a dynamic evolutionary model. This model can simulate and predict the continuous dynamic process from gene expression perturbation to barrier function decline during toxin exposure. This enables risk warnings to be issued before damage is fully apparent, significantly improving the timeliness of assessment.

[0007] This invention innovatively employs the continuous cohomology theory to analyze transcriptome data and extract the topological stability characteristics of gene regulatory networks, thereby revealing the systemic disorder of gene network structure under the influence of toxins.

[0008] This invention constructs a stochastic dynamic potential well model and uses path integrals and variational methods to calculate the probability of damage to the system when crossing the potential barrier at different toxin concentrations. The result is no longer "whether damage occurs," but rather "with what probability of damage." This probabilistic prediction can more scientifically and comprehensively reflect the individual differences and uncertainties in the organism's response, greatly improving the accuracy and reliability of risk assessment.

[0009] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the method for predicting testicular Sertoli cell damage caused by vomiting toxins, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the predictive system for testicular Sertoli cell damage caused by vomiting toxins as described in an embodiment of the present invention. Figure 3 This is a KEGG enrichment analysis diagram of differentially expressed genes downregulated after DON exposure in the method for predicting testicular Sertoli cell damage by vomitoxin described in this embodiment of the invention. Figure 4 This is a transmission electron microscope image of DON-induced tight junction damage in the method for predicting testicular Sertoli cell damage by vomitoxin described in this embodiment of the invention. Figure 5 This is a dot plot showing the effect of different concentrations of DON treatment on apoptosis rate in the method for predicting testicular Sertoli cell damage caused by vomitoxin as described in this embodiment of the invention. Figure 6 This is a bar chart showing the effect of different concentrations of DON treatment on apoptosis rate in the method for predicting testicular Sertoli cell damage caused by vomitoxin as described in this embodiment of the invention.

[0012] In the diagram: 701, Acquisition Unit; 702, Extraction Unit; 703, Modeling Unit; 704, Simulation Unit; 705, Construction Unit; 706, Processing Unit. Detailed Implementation

[0013] 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, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0014] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0015] Example 1

[0016] This embodiment provides a method for predicting testicular Sertoli cell damage caused by vomitoxin.

[0017] See Figure 1 The figure shows that the method includes steps S1, S2, S3, S4, S5 and S6.

[0018] Step S1: Obtain transcriptome sequencing data, immunofluorescence quantitative data of tight junction proteins of male animal testis tissue under vomitoxin exposure conditions, electron microscopy image data of blood-testis barrier, and flow cytometry data of apoptosis and cell cycle of supporting cells at different toxin concentrations. Understandably, the transcriptome sequencing data (RNA-seq) in this step provides a genome-wide expression profile of testicular tissue under DON exposure, enabling unbiased identification of key differentially expressed genes related to cell connectivity, inflammatory responses, and apoptosis pathways (such as...). Figure 3 As shown), where, Figure 3The horizontal axis represents the pathway enrichment factor, i.e., the proportion of differentially expressed genes in a particular pathway out of the total number of genes in that pathway. The vertical axis represents the specific KEGG pathway name. This reveals the initial target and signaling pathway perturbation of toxin action at the molecular network level. Immunofluorescence quantitative data supporting tight junction proteins (such as ZO-1, CX43) directly correlate these molecular-level changes with protein localization and expression levels. High-resolution imaging and fluorescence intensity quantification precisely quantify the loss of key structural proteins in the blood-testis barrier. Electron microscopy images of the blood-testis barrier provide standard validation at the ultrastructural level, allowing direct observation of the breakage and loosening of tight junctions (TJs) (e.g., Figure 4 Morphological damage (as shown) including, Figure 4 The scale bar for the first image (top left) is 5 µm, and for the second image (top right) it is 2 µm. Yellow arrows indicate intact tight junction desmosomes. The scale bar for the third image (bottom left) is 5 µm, and for the fourth image (bottom right) it is 2 µm. Red arrows indicate damaged or broken tight junction desmosomes. This provides structural confirmation and deeper supplementation to the immunofluorescence data. Flow cytometry data supporting apoptosis and cell cycle at different toxin concentrations quantitatively reveal the toxic effects of vomiting toxin (DON) at the cellular function level, such as increased apoptosis rate (e.g., ...). Figure 5 and Figure 6 (as shown) Figure 5 The colors represent the cell density at each location (combination of fluorescence signal intensities) (blue → green → yellow → red areas: cell number gradually increases). Figure 6 The different colored bars represent different concentrations of vomitoxin treatment. Gray indicates cells treated with 0 µM vomitoxin (control group), pink indicates cells treated with 0.4 µM vomitoxin, green indicates cells treated with 1.6 µM vomitoxin, and purple indicates cells treated with 6.4 µM vomitoxin (treatment group). Compared with the control group (0 µM), the apoptosis rate in the treatment group (6.4 µM) was significantly increased. These data directly reflect the survival status and proliferative capacity of the supporting cell population, serving as a crucial bridge connecting molecular damage and organ dysfunction (such as blood-testis barrier collapse).

[0019] Step S2: Based on the persistent cohomology theory, feature extraction is performed on the transcriptome sequencing data, and the extracted topological features are encoded to obtain a persistent barcode representation of the gene regulatory network. Understandably, this step involves in-depth feature mining of high-throughput transcriptome data. Its advantage lies in bypassing the limitations of pre-defined gene lists or pathways in traditional differential expression analysis, directly extracting global and robust features from the data's geometry and topology. It also allows us to discover global network changes formed by the synergistic effects of multiple genes that might be overlooked by traditional methods (e.g., DON exposure may weaken the topological connectivity of gene modules related to "cell adhesion," i.e., shorten their feature barcodes). This provides highly condensed and structurally rich feature inputs for subsequent fusion of other modalities and construction of predictive models. In this step, step S2 includes steps S21, S22, S23, and S24.

[0020] Step S21: Perform gene expression relationship measurement learning on the transcriptome sequencing data. Specifically, the difference and correlation between gene expression vectors in the sample space are calculated by weighting Mahalanobis distance with gene function annotation and prior knowledge of known pathways, and a high-dimensional distance matrix reflecting the functional correlation between genes is obtained. Understandably, this step first integrates prior knowledge from pre-defined databases such as gene function annotation, gene ontology (GO), and KEGG pathway databases. Specifically, the system constructs a weighted gene function association network: if two genes participate in the same biological process, are located in the same organelle, or belong to the same signaling pathway, their functional association weight is high. This weight matrix, defined by prior knowledge, is cleverly embedded in the metric learning of Mahalanobis distance to constrain or guide the calculation of the covariance matrix. The process can be understood as: "shrinking" the expression distance between functionally closely related gene pairs, while "expanding" the expression distance between functionally unrelated gene pairs.

[0021] First, a gene expression matrix is ​​constructed with m samples and n genes. Then, gene function annotation information obtained from authoritative databases (such as GO and KEGG) is used to calculate a functional association strength based on prior knowledge for each gene pair. This strength is determined by calculating the Jaccard index, resulting in a weighted gene function association matrix. Next, the sample covariance matrix of the gene expression matrix is ​​calculated, and the weighted gene function association matrix is ​​used to modulate the sample covariance matrix. Graph Laplacian regularization is employed to obtain a covariance estimation matrix. Finally, Mahalanobis distance is calculated on each pair of vectors in the covariance estimation matrix to obtain the final high-dimensional distance matrix reflecting the functional correlation between genes. The Mahalanobis distance calculation formula is shown below: ; Where, d cu e represents the Mahalanobis distance between genes. c Let e ​​be the indicator vector of gene c.u Let be the indicator vector for gene u, and Τ be the transpose symbol. It is the inverse matrix of the modulated covariance estimation matrix.

[0022] Through this processing, the resulting high-dimensional distance matrix is ​​no longer merely a mathematical representation of expression differences, but a biological distance matrix reflecting the functional correlation between genes. In this matrix, two genes with similar expression patterns and related biological functions will have a very small distance between them; conversely, even if their expression patterns are coincidentally similar but their functions are unrelated, the distance will be maintained at a relatively large value. This effectively filters out noise in high-dimensional data and highlights biologically meaningful co-expression patterns.

[0023] Step S22: Construct the topological structure of the gene expression space based on the high-dimensional distance matrix. In this step, by taking each gene as a vertex and setting filtering parameters according to the distance matrix, a complex sequence that evolves with toxin concentration is constructed. The complex sequence is a sequence that reflects the birth and death process of gene co-expression communities under different toxin concentrations. Understandably, this step first takes a high-dimensional distance matrix as input, where each element represents the degree of difference in gene expression patterns after biological function weighting. The system treats each gene as a vertex, constructing an initial graph structure. Subsequently, a filtering parameter is introduced, which is the "maximum functional distance allowed between genes for connections." The process of the filtering parameter value changing from small to large simulates the process of constructing a fragmented to complete gene co-expression network under gradually relaxed connection criteria.

[0024] Then, for each specific toxin concentration (corresponding to a specific experimental group), the algorithm sets an initial filtering parameter and constructs a corresponding Rips complex. In this invention, the rule for the Rips complex is: when the distance between any two genes in a group is less than or equal to the current filtering parameter, they form a simplex (for example, if the distance between two gene points is less than or equal to the filtering parameter, they are connected as an edge; if the distance between any three gene points is less than or equal to the filtering parameter, they form a triangular face). As the filtering parameter gradually increases, more and more connections are established, and individual points and edges gradually form larger connected branches, loop structures, and cavities, thus forming an increasingly complex complex structure.

[0025] Finally, the series of complexes constructed under different filtering parameters are arranged in order to obtain the complex sequence that evolves with the filtering parameters.

[0026] Step S23: Extract topological invariants based on the complex sequence, wherein the continuous homology values ​​of homology groups in each dimension are calculated by continuous homology theory, and the birth and death processes of topological features at different scales are tracked to obtain a set of topological features used to describe the stability of gene regulatory networks, wherein the topological features are connected branches, loop structures and cavities. Understandably, this step iterates through each complex in the complex sequence (corresponding to a filter parameter value). For each complex, this step calculates its homology groups in each dimension. Homology groups are a core concept in algebraic topology, used to describe the "hole" structure of a complex in a specific dimension: the generators of a 0-dimensional homology group represent connected components, the number of which reflects the number of independent communities in the gene expression network; the generators of a 1-dimensional homology group represent loop structures, i.e., closed loops formed by a series of edges connected end-to-end, which may correspond to a functional loop or feedback regulatory pathway; the generators of a 2-dimensional homology group represent cavities, i.e., closed three-dimensional spaces enclosed by surfaces, which may symbolize more complex gene regulatory modules.

[0027] This is achieved by tracking the "lifecycle" of each topological feature: recording at which filter parameter value (birth scale) it first appears, and at which filter parameter value (death scale) it is "filled" or submerged due to the establishment of larger connections. The persistent homology value of a feature is represented by this pair (birth scale, death scale). Its lifecycle length is the birth scale minus the death scale.

[0028] In this invention, a long-lived one-dimensional loop corresponds to a gene co-expression feedback loop that remains stable under various connection conditions (such as a core regulatory circuit related to tight junction protein synthesis). However, exposure to vomitoxin may cause the loop to "die" prematurely, i.e., its lifespan is significantly shortened, indicating that the stability of this functional circuit is disrupted by the toxin, making it fragile and prone to breakage.

[0029] This invention transforms dynamic, difficult-to-analyze complex sequences into a quantitative set of topological features containing information on birth, extinction, and life cycle. This feature set is no longer the original gene expression levels, but a mathematical description of the higher-order connection patterns and structural stability of the gene network.

[0030] Step S24: Perform topological feature encoding based on the topological feature set, wherein persistent cohomological pairs with a lifecycle length exceeding a preset threshold are selected from the topological feature set and mapped to a point set on a two-dimensional plane to obtain a persistent barcode representation characterizing the stability of the gene regulatory network.

[0031] It is understandable that this step filters out topological features that exist only briefly and are likely to represent noise (such as features with short lifecycles) by setting a preset threshold (such as the lifetime length needing to be greater than the median lifetime length of all features), while retaining those core topological features that are stable and significant over a wide range of scales.

[0032] Subsequently, each selected topological feature is encoded as a point on a two-dimensional plane: the x-coordinate of the point represents its birth scale, and the y-coordinate represents its extinction scale. Therefore, the lifetime of a feature is equal to the vertical distance from that point to the diagonal. This set of all points is then grouped by dimension and stored on a two-dimensional plane to obtain the final "persistent barcode" representation. In this representation, a point far from the diagonal represents a long-lived, highly stable topological structure; while a point close to the diagonal represents a transient and unstable structure.

[0033] Step S3: The persistent barcode representation, immunofluorescence quantitative data and electron microscopy image data are fused and processed, and the fused data are modeled for feature evolution by a preset neural differential equation to obtain a topological dynamic characterization that supports cell barrier function. Understandably, this step integrates and evolves three types of heterogeneous data—topological features characterizing macroscopic gene network stability (persistent barcodes), mesoscopic protein distribution (immunofluorescence), and microscopic ultrastructure (electron microscopy)—within a unified mathematical framework, thereby achieving a systematic quantitative modeling of the complex biological process supporting cell barrier function. In this step, step S3 includes steps S31, S32, S33, and S34.

[0034] Step S31: Perform unified embedding of multimodal data based on the persistent barcode representation, wherein the lifecycle of feature points in the barcode is mapped to a high-dimensional vector and aligned in the latent space with the integrity index of the tight junction structure of the blood-testis barrier extracted from the immunofluorescence quantitative data and electron microscopy image data to obtain an initial joint feature vector; Understandably, this step first maps the lifetime of each topological feature point of the persistent barcode to a set of basis functions (Gaussian kernels) using Betti curves, and then superimposes the contributions of all features to generate a smooth, fixed-dimensional function vector. Secondly, the immunofluorescence quantitative data (such as the fluorescence intensity of ZO-1 and CX43) are standardized to eliminate batch effects, followed by dimensionality reduction using principal component analysis (PCA) to extract the core feature vectors that best represent the expression level and distribution pattern of tight junction proteins. Finally, a pre-trained deep learning segmentation model (U-Net) is used to automatically identify and segment the connective structures of the blood-testis barrier. Subsequently, feature vectors such as continuous length, tortuosity, and the number and area of ​​broken gaps are extracted from the segmentation results. The deep learning segmentation model is learned from historical electron microscopy image data and connective structure data of the blood-testis barrier. This results in a semantically unified latent space with re-encoded and aligned fused representations.

[0035] Step S32: Based on the initial joint feature vector and the preset neural differential equation with toxin exposure time as the implicit time parameter, the dynamic interaction and evolution law among all topological features of the supporting cells under the action of toxin were simulated, and a dynamic model of the change of the barrier function of the supporting cells over time was obtained. It is understood that this step uses the initial joint feature vector as the initial state of the neural differential equation. This vector contains the comprehensive state of the blood-testis barrier at the gene expression, protein distribution, and ultrastructure levels at the initial moment of toxin exposure (e.g., at the lowest concentration or zero time). The neural differential equation is as follows: ; in, f is the time derivative of the state vector. θ Z(t) is the vector-valued function parameterized by the neural network, where θ is the learnable parameters (weights and biases) of the neural network, and Z(t) is the time-varying state vector, where t is time.

[0036] By integrating this neural differential equation using the fourth-order Runge-Kutta method, the continuous evolution trajectory of the state vector from the initial state (0 μM) to the target value as the toxin dose changes can be calculated. This trajectory simulates the dynamic interaction between the initial joint eigenvectors under increasing toxin stress.

[0037] This step allows us to query features at any intermediate state by changing the input concentration parameter, thereby enabling a detailed characterization of the functional decline process and prediction of intermediate states.

[0038] Step S33: Solve the dynamic model by evolution of joint feature vectors. The neural differential equation is integrated from the start time to the end time of toxin exposure using the adjoint method to obtain the joint feature vectors after the evolution of time parameters. The topological features of the joint feature vectors are the result of the interaction of the toxin perturbation. It is understood that the trained dynamic model (differential equation) and initial joint feature vector in this step serve as input. The system state is integrated from the start to the end of the toxin exposure time using the adjoint method. During integration, the adjoint states of the state variables are calculated in reverse and in parallel, thereby efficiently obtaining the system's state value at each toxin exposure time. This yields the evolved joint feature vector, which contains the interaction results of all modal features of the system after the complete toxin exposure process, such as gene network instability and protein degradation. The dimension representing the integrity of tight connections in the vector is significantly reduced.

[0039] It is understandable that this step uses the adjoint method to solve the complete trajectory of the system state with the toxin exposure concentration, thereby obtaining the final state of the system's multimodal characteristics at the endpoint concentration, and obtaining the cumulative effect of the topological features of the joint feature vector on the toxin perturbation.

[0040] Step S34: Generate a topological dynamics representation based on the evolved joint feature vector, wherein the dimensions representing the blood-testis barrier function are decoded from the evolved joint feature vector. The dimensions representing the blood-testis barrier function include the expression dynamics of tight junction-related proteins, the topological stability of gene regulatory networks, and the physical structural integrity of intercellular connections, thereby obtaining a topological dynamics representation that supports the cell barrier function.

[0041] It is understandable that this step involves processing the input of the post-evolutionary joint feature vector into a pre-defined decoder. During the training phase, this decoder uses samples labeled as "barrier intact" or "barrier severely damaged" based on pathological results for supervised learning. This allows it to learn to assign higher weights to specific dimensions strongly correlated with barrier function in the joint vector (topological stability dimension of gene regulatory networks, expression dynamics dimension of tight junction-related proteins, and physical structural integrity dimension of intercellular connections), while suppressing noise dimensions unrelated to function. The decoder includes an input layer, a computational layer, a semantic mapping layer, a normalization layer, and an output layer. The semantic mapping layer extracts information from the samples related to the topological stability dimension of gene regulatory networks, the expression dynamics dimension of tight junction-related proteins, and the physical structural integrity dimension of intercellular connections, respectively. After training, only a few samples have non-zero weights. The original feature dimensions corresponding to these non-zero weights are interpreted as the factors contributing most to the specific dimensions. The computational layer is trained using L1 regularization, aiming to make as many elements of the weight matrix as possible zero.

[0042] Through the encoder, this step decodes the vector representing the dimensions of the blood-testis barrier function from the evolved joint feature vector. This includes data on the topological stability of the gene regulatory network, the expression dynamics of tight junction-related proteins, and the physical structural integrity of intercellular connections.

[0043] Step S4: Based on the topological dynamics characterization and the flow cytometry data, a cell state phase transition model is constructed using cellular automata and percolation theory, and cell state simulation is performed based on the cell state phase transition model to obtain the critical damage threshold for the integrity of the blood-testis barrier. Understandably, this step constructs a cross-scale model using cellular automata and percolation theory to simulate the dynamic phase transition process of the supporting cell population from a healthy state to functional collapse under the influence of toxins, and precisely locates the critical damage threshold for the integrity of the blood-testis barrier. In this step, step S4 includes steps S41, S42, S43, and S44.

[0044] Step S41: Map the topological dynamics representation into a two-dimensional grid, where each cell represents a supporting cell, and its initial state is assigned by the flow cytometry detection data to obtain a spatial distribution model reflecting the initial state of the supporting cell population. Understandably, this step first deconstructs the topological dynamics representation. The "physical structural integrity of intercellular connections" dimension is directly extracted and converted into an intercellular connection strength value between 0 and 1 using a sigmoid function. This value quantifies the ability of a specific cell to form and maintain functional connections with other cells in its local environment. Simultaneously, the other two dimensions of this representation (the expression dynamics of tight junction-related proteins and the physical structural integrity of intercellular connections) are combined into a single cell functional integrity index using a weighted formula, reflecting the intrinsic health of the cell when it is decoupled from its community. The weighted formula is shown below: ; in, As an indicator of cell function integrity, To tightly link the weights of the expression dynamics dimension of related proteins, To score the expression dynamic dimension of tightly linked proteins, The weights represent the topological stability dimension of the gene regulatory network. The score represents the topological stability dimension of the gene regulatory network.

[0045] Subsequently, this step constructs a two-dimensional grid to simulate the actual monolayer spread of supporting cells on the basement membrane. Each cell in the grid represents a supporting cell. The initial state of each cell is generated based on a probabilistic model of the flow cytometry data, resulting in a spatial distribution model (two-dimensional grid) reflecting the initial state of the supporting cell population. Specifically, the apoptosis rate and cell cycle arrest ratio detected in the flow cytometry data are first considered as population statistical priors. For example, if the flow cytometry data shows an initial apoptosis rate of 5%, approximately 5% of the cells in the grid are randomly initialized to either an "apoptotic" or "severely damaged" state.

[0046] Step S42: Define the cell state evolution rules based on the spatial distribution model, wherein, based on the neighbor coupling rule of percolation theory, simulate the diffusion of apoptosis signals through cell connections in the population to obtain the evolution function that supports the dynamic interaction of cell populations under toxin stress. Understandably, this step defines a state transition probability function, which determines the likelihood that a cell will change its state (e.g., from "healthy" to "stressed" or "apoptotic") at the next time step. The state transition probability of this cell is a weighted sum of the initial apoptosis rate and neighbor pressure in the flow cytometry data, and the weighted sum formula is shown below: ; in, Let σ be the state transition probability, σ be the logistic function, and α be the preset self-vulnerability weight coefficient. The data is a weighted average of three dimensions: topological stability of the gene regulatory network, expression dynamics of tight junction-related proteins, and physical structural integrity of intercellular connections, representing the vulnerability of the network itself. β is the neighbor pressure weighting coefficient. All neighboring cells of the current cell belong to set Ω. For cell connection strength, Let γ be the Kronecker increment function, which means that the function value is 1 when the neighboring cell j is in the state of "apoptosis" and 0 otherwise. γ is the bias term.

[0047] Step S43: Based on the cellular automata model, iteratively apply the evolution function to simulate the dynamic process of supporting cell population from the onset of apoptosis to the formation of connected apoptotic clusters larger than the preset area under exposure to toxins from low dose to high dose. Record the size of the largest connected apoptotic cluster in the population after each simulation to obtain a data sequence reflecting the degree of connectivity collapse of the cell population under each toxin level. Understandably, this step initiates an iterative simulation of cellular automata on a two-dimensional grid. For each set toxin level, the cellular automata performs multiple independent simulations (to eliminate the influence of randomness). In each iteration of each simulation, the cellular automata not only updates the cell state, but also, after each simulation reaches a steady state (or a preset number of iterations), records the grid area (i.e., the number of cells contained) occupied by the largest connected apoptotic cluster. When the size of the connected apoptotic cluster is small, it indicates that apoptosis is sporadic and isolated, unable to affect population function; while when the size of the connected apoptotic cluster suddenly increases and spans the entire system scale, it signifies a collapse in cell population connectivity.

[0048] Finally, by traversing a series of toxin levels from low to high and performing multiple simulations at each level, a data sequence of the degree of cell population connectivity collapse (the size sequence of apoptotic clusters) was obtained for each toxin level.

[0049] Step S44: Analyze the data sequence, wherein by identifying mutation points in the data sequence, the dose corresponding to the mutation point is the critical point for the occurrence of percolation phase transition, and the critical damage threshold for the integrity of the blood-testis barrier is obtained.

[0050] Understandably, this step analyzes the simulated data sequence, where the critical damage threshold is the mean of all mutation points (points where the size change of apoptotic clusters exceeds a preset threshold), thereby accurately identifying the critical toxin dose at which the hemorrhagic testis barrier function undergoes systemic collapse. This step avoids the limitations of traditional toxicology that relies on subjective pathological observation to determine thresholds, providing a data-driven, computationally derived inflection point.

[0051] Step S5: Based on the critical damage threshold and the flow cytometry data, construct a potential well model to obtain a potential well model of toxin dose-barrier damage; Understandably, this step utilizes the population statistical noise information provided by the critical damage threshold and flow cytometry data to construct a stochastic dynamic potential well model, thereby quantitatively describing the nonlinear relationship between toxin dose and the functional state of the blood-testis barrier, and revealing its inherent randomness. Furthermore, the functional state of the blood-testis barrier is abstracted as the position of a particle in the energy landscape. When undisturbed by toxins, the testicular Sertoli cells are in a stable state representing "barrier integrity" (i.e., the bottom of the potential well). As the toxin dose increases, the energy landscape of the testicular Sertoli cells deforms. In this step, step S5 includes steps S51 and S52.

[0052] Step S51: By using the critical damage threshold as the bifurcation point and quantifying the barrier damage intensity using the cell apoptosis rate and cell cycle arrest ratio in the flow cytometry data, a set of coupling parameters relating to the toxin dose and barrier damage is obtained. Understandably, this step directly uses the critical damage threshold as a control parameter in the potential function. A higher apoptosis rate indicates greater uncertainty in cell fate. By calculating the variance of the apoptosis rate at different doses, the relationship between the apoptosis rate and the vomiting dose can be obtained. An increased cell cycle arrest ratio indicates disruption of the normal cell cycle process, a direct manifestation of functional impairment. A mapping function between toxin concentration and damage intensity is established through curve fitting. Furthermore, the critical damage threshold, the variance of the apoptosis rate at different doses, and the mapping function between toxin concentration and damage intensity are coupled to obtain a parameter set.

[0053] Step S52: Construct a stochastic dynamic potential well model based on the set of associated coupling parameters. The potential well model of toxin dose-barrier damage is obtained by using a non-equilibrium state function with toxin dose as the control parameter and barrier damage as the state variable.

[0054] This step uses the set of associated coupling parameters as the control parameters input to the model; the functional state of the blood-testis barrier (from intact to completely damaged) is defined as the system's state variable, thus obtaining a non-equilibrium state function, which is used as a potential well model of toxin dose-barrier damage. This model accurately reproduces and predicts the abrupt changes in system behavior near the critical dose, providing a dynamic explanation for understanding the suddenness and irreversibility of toxic effects. The non-equilibrium state function is shown below: ; in, γ is a non-equilibrium state function, x is the state variable of the blood-testis barrier, C is the toxin dose, C* is the critical damage threshold, and γ(C) is the potential well shape parameter, the value of which is calibrated by flow cytometry data.

[0055] Step S6: Process the potential well model based on path integral and variational method to generate the damage probability value of testicular supporting cells at each toxin concentration.

[0056] It is understandable that for any given toxin concentration, a theoretical probability of injury can be calculated. This changes the traditional binary "yes or no" judgment model of toxicology and enables quantitative risk assessment. In this step, step S6 includes steps S61 and S62.

[0057] Step S61: Use the path integral method to perform a weighted summation of the possible paths of the healthy state trap and the damaged state trap in all connected potential trap models. The weight is the Onsager-Machlup action of each path, so as to obtain the probability amplitude of the transition from the healthy state to the damaged state at each toxin concentration. It is understandable that this step treats the system evolution described by the potential well model as a stochastic process. There are infinitely many paths connecting the healthy state to the damaged state, and each path contributes to the final transition probability. The Onsager-Machlup action is defined as a weighting factor for each path, as shown below: ; in, For the Onsager-Machlup action, t a Let t be the initial time. b Where is the termination time, and D is the diffusion coefficient. The first derivative of the state variable with respect to time. Let V(x) be the gradient of the potential function, x be the state variable of the blood-testis barrier, t be the time, and d be the differential symbol.

[0058] Then, the path integral is calculated by weighted summation over all possible paths to determine the probability amplitude of the transition, which is used to quantify the probability of cell state transitions under the influence of toxins. The weighting formula is shown below: ; in, This indicates that the system at time t a In state x a (Healthy state), and at time t b Found in state x b The probability amplitude of (damaged state), The path integral represents the summation of all possible paths connecting the initial and final states. x(t) represents a function of the state variable over time, where x is the state variable of the blood-testis barrier and t represents time. This represents the Boltzmann weight of each path.

[0059] Step S62: By using variational methods, determine the extreme path that minimizes the Onsager-Machlup effect, calculate the quasi-potential function value of the extreme path to determine its transition probability, and obtain the most likely path for the collapse of the blood-testis barrier function and its probability of occurrence at each toxin concentration.

[0060] Understandably, this step uses variational methods to find the most probable (with the largest probability contribution) evolutionary path from all possible paths, and quantitatively calculates the probability of a state transition in the system based on this path. This achieves an accurate and interpretable probability prediction of the risk of blood-testis barrier collapse. Finding the most probable (with the largest probability contribution) evolutionary path from all possible paths is achieved by solving the corresponding Euler-Lagrange equation, the formula of which is shown below: ; Where L is the Lagrange quantity, and x is the state variable of the blood-testis barrier, whose value changes continuously from representing "health" to representing "damage". Let t be the first derivative of the state variable with respect to time. This represents the partial derivative of the Lagrange with respect to the state variable. Let denote the total derivative of the generalized momentum pair. This represents the partial derivative of the Lagrange with respect to the rate of change of state.

[0061] Subsequently, the rate constant of this pathway from the healthy state to the damaged state was calculated using the Kramers formula, and it was used as the most likely pathway for the collapse of the blood-testis barrier function at each toxin concentration and its probability value.

[0062] Example 2

[0063] like Figure 2 As shown, this embodiment provides a predictive system for testicular Sertoli cell damage caused by vomiting toxins. See [link to documentation]. Figure 2 The system includes The acquisition unit 701 is used to acquire transcriptome sequencing data of testicular tissue of male animals under vomitoxin exposure conditions, immunofluorescence quantitative data of tight junction proteins of supporting cells, electron microscopy image data of blood-testis barrier, and flow cytometry data of apoptosis and cell cycle of supporting cells at different toxin concentrations. Extraction unit 702 is used to extract features from the transcriptome sequencing data based on the persistent cohomology theory, and to encode the extracted topological features to obtain a persistent barcode representation of the gene regulatory network. Modeling unit 703 is used to fuse the persistent barcode representation, immunofluorescence quantitative data and electron microscopy image data, and to perform feature evolution modeling on the fused data driven by a preset neural differential equation to obtain a topological dynamic characterization that supports cell barrier function. The simulation unit 704 is used to construct a cell state phase transition model based on the topological dynamics characterization and the flow cytometry detection data, using cellular automata and percolation theory, and to perform cell state simulation based on the cell state phase transition model to obtain the critical damage threshold for the integrity of the blood-testis barrier. The construction unit 705 is used to construct a potential well model based on the critical damage threshold and the flow cytometry detection data to obtain a potential well model of toxin dose-barrier damage. Processing unit 706 is used to process the potential well model based on path integral and variational method to generate the damage probability value of testicular supporting cells at each toxin concentration.

[0064] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0066] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting testicular Sertoli cell damage caused by vomitoxin, characterized in that, include: Transcriptome sequencing data of testicular tissue from male animals under vomitoxin exposure conditions, immunofluorescence quantitative data of tight junction proteins of supporting cells, electron microscopy images of the blood-testis barrier, and flow cytometry data of apoptosis and cell cycle of supporting cells at different toxin concentrations were obtained. Feature extraction was performed on the transcriptome sequencing data based on the persistent cohomology theory, and the extracted topological features were encoded to obtain a persistent barcode representation of the gene regulatory network. The persistent barcode representation, immunofluorescence quantitative data, and electron microscopy image data are fused and processed, and the fused data are then modeled using a preset neural differential equation to obtain a topological dynamic characterization that supports cell barrier function. Based on the aforementioned topological dynamics characterization and the aforementioned flow cytometry data, a cell state phase transition model is constructed using cellular automata and percolation theory. Based on the cell state phase transition model, cell state simulation is performed to obtain the critical damage threshold for the integrity of the blood-testis barrier. A potential well model is constructed based on the critical damage threshold and the flow cytometry data to obtain a potential well model of toxin dose-barrier damage. The potential well model was processed using path integral and variational methods to generate the probability value of testicular Sertoli cell damage for each toxin concentration.

2. The method for predicting testicular Sertoli cell damage caused by vomitoxin according to claim 1, characterized in that... Based on the transcriptome sequencing data, multi-scale topological features of the gene expression space are extracted using a continuous isohomology topological data analysis method, including: The transcriptome sequencing data is used to measure and learn gene expression relationships. Specifically, the difference and correlation between gene expression vectors in the sample space are calculated by weighting Mahalanobis distance with gene function annotation and prior knowledge of known pathways, and a high-dimensional distance matrix reflecting the functional correlation between genes is obtained. The topological structure of the gene expression space is constructed based on the high-dimensional distance matrix. In this process, by taking each gene as a vertex and setting filtering parameters according to the distance matrix, a complex sequence that evolves with toxin concentration is constructed. The complex sequence is a sequence that reflects the birth and death process of gene co-expression communities under different toxin concentrations. Based on the complex sequence, topological invariants are extracted, wherein the continuous homology values ​​of homology groups in each dimension are calculated by continuous homology theory, and the birth and death processes of topological features at different scales are tracked to obtain a set of topological features used to describe the stability of gene regulatory networks, wherein the topological features are connected branches, loop structures and cavities. Topological feature encoding is performed based on the aforementioned topological feature set. Specifically, persistent homology pairs with a lifecycle length exceeding a preset threshold are selected from the topological feature set and mapped to a point set on a two-dimensional plane to obtain a persistent barcode representation that characterizes the stability of the gene regulatory network.

3. The method for predicting testicular Sertoli cell damage caused by vomitoxin according to claim 1, characterized in that... The persistent barcode representation, immunofluorescence quantitative data, and electron microscopy image data are fused and processed, and feature evolution modeling is driven by a preset neural differential equation, including: Based on the persistent barcode representation, a unified embedding of multimodal data is performed, wherein the lifecycle of feature points in the barcode is mapped to a high-dimensional vector and aligned in the latent space with the integrity index of the tight junction structure of the blood-testis barrier extracted from immunofluorescence quantitative data and electron microscopy image data to obtain an initial joint feature vector. Based on the initial joint feature vector and the preset neural differential equation with toxin exposure time as the implicit time parameter, the dynamic interaction and evolution law among all topological features of supporting cells under the action of toxin were simulated, and a dynamic model of the change of the barrier function of supporting cells over time was obtained. The dynamic model is solved by evolution of joint feature vectors. The neural differential equation is integrated from the start time to the end time of toxin exposure using the adjoint method to obtain the joint feature vectors after the evolution of time parameters. The topological features of the joint feature vectors are the result of the interaction of toxin perturbations. A topological dynamics representation is generated based on the evolved joint feature vector, wherein dimensions representing the blood-testis barrier function are decoded from the evolved joint feature vector. These dimensions include the expression dynamics of tight junction-related proteins, the topological stability of the gene regulatory network, and the physical structural integrity of intercellular connections, thus obtaining a topological dynamics representation that supports the cell barrier function.

4. The method for predicting testicular Sertoli cell damage caused by vomitoxin according to claim 1, characterized in that... Based on the aforementioned topological dynamics characterization and the aforementioned flow cytometry data, a cell state phase transition model is constructed using cellular automata and percolation theory. Cell state simulation is then performed based on this model to obtain the critical damage threshold for the integrity of the blood-testis barrier, including: The topological dynamics representation is mapped to a two-dimensional grid, where each cell represents a supporting cell, and its initial state is assigned by flow cytometry data to obtain a spatial distribution model that reflects the initial state of the supporting cell population. Based on the spatial distribution model, the cell state evolution rules are defined, wherein the neighbor coupling rule based on the percolation theory is used to simulate the diffusion of apoptosis signals through cell connections in the population, thereby obtaining an evolution function that supports the dynamic interaction of cell populations under toxin stress. Based on the cellular automata model, the evolution function is iteratively applied to simulate the dynamic process of supporting cell populations from the onset of apoptosis to the formation of connected apoptotic clusters larger than a preset area under exposure to toxins from low to high doses. The size of the largest connected apoptotic cluster in the population is recorded after each simulation, and a data sequence reflecting the degree of connectivity collapse of the cell population under each toxin level is obtained. Based on the analysis of the data sequence, by identifying mutation points in the data sequence, the dose corresponding to the mutation point is the critical point for the occurrence of percolation phase transition, and the critical damage threshold for the integrity of the blood-testis barrier is obtained.

5. The method for predicting testicular Sertoli cell damage caused by vomitoxin according to claim 1, characterized in that... Based on the critical damage threshold and the flow cytometry data, a potential well model is constructed to obtain a toxin dose-barrier damage potential well model, including: By using the critical damage threshold as a bifurcation point and quantifying the barrier damage intensity using the apoptosis rate and cell cycle arrest ratio in the flow cytometry data, a set of coupling parameters relating to toxin dose and barrier damage is obtained. A stochastic dynamic potential well model is constructed based on the set of associated coupling parameters. Specifically, a potential well model of toxin dose-barrier damage is obtained by using a non-equilibrium state function with toxin dose as the control parameter and barrier damage as the state variable.

6. A predictive system for testicular Sertoli cell damage caused by vomitoxin, characterized in that, include: The acquisition unit is used to acquire transcriptome sequencing data of testicular tissue from male animals under vomitoxin exposure conditions, immunofluorescence quantitative data of tight junction proteins of supporting cells, electron microscopy image data of the blood-testis barrier, and flow cytometry data of apoptosis and cell cycle of supporting cells at different toxin concentrations. The extraction unit is used to extract features from the transcriptome sequencing data based on the persistent cohomology theory, and to encode the extracted topological features to obtain a persistent barcode representation of the gene regulatory network. The modeling unit is used to fuse the persistent barcode representation, immunofluorescence quantitative data and electron microscopy image data, and to perform feature evolution modeling on the fused data driven by a preset neural differential equation to obtain a topological dynamic characterization that supports cell barrier function. The simulation unit is used to construct a cell state phase transition model based on the topological dynamics characterization and the flow cytometry detection data, using cellular automata and percolation theory, and to perform cell state simulation based on the cell state phase transition model to obtain the critical damage threshold for the integrity of the blood-testis barrier. The construction unit is used to construct a potential well model based on the critical damage threshold and the flow cytometry detection data to obtain a potential well model of toxin dose-barrier damage. The processing unit is used to process the potential well model based on path integral and variational method to generate the damage probability value of testicular supporting cells at each toxin concentration.

7. The predictive system for testicular Sertoli cell damage caused by vomitoxin according to claim 6, characterized in that, The extraction unit includes: The first extraction subunit is used to perform gene expression relationship measurement learning on the transcriptome sequencing data. Specifically, the difference and correlation between gene expression vectors in the sample space are calculated by weighting Mahalanobis distance with gene function annotation and prior knowledge of known pathways, and a high-dimensional distance matrix reflecting the functional correlation between genes is obtained. The second extraction subunit is used to construct the topological structure of the gene expression space based on the high-dimensional distance matrix. In this subunit, by taking each gene as a vertex and setting filtering parameters according to the distance matrix, a complex sequence that evolves with toxin concentration is constructed. The complex sequence is a sequence that reflects the birth and death process of gene co-expression communities under different toxin concentrations. The third extraction subunit is used to extract topological invariants based on the complex sequence. The continuous homology value of the homology group in each dimension is calculated by the continuous homology theory. The birth and death process of topological features at different scales is tracked to obtain a set of topological features to describe the stability of the gene regulatory network. The topological features are connected branches, loop structures and cavities. The fourth extraction subunit is used to perform topological feature encoding based on the topological feature set. Specifically, by screening persistent homology pairs with a lifecycle length exceeding a preset threshold within the topological feature set, these pairs are mapped to a point set on a two-dimensional plane to obtain a persistent barcode representation that characterizes the stability of the gene regulatory network.

8. The predictive system for testicular Sertoli cell damage caused by vomitoxin according to claim 6, characterized in that, The modeling unit includes: The first modeling subunit is used to perform unified embedding of multimodal data based on the persistent barcode representation. The initial joint feature vector is obtained by mapping the lifecycle of feature points in the barcode to a high-dimensional vector and aligning it with the integrity index of the tight junction structure of the blood-testis barrier extracted from immunofluorescence quantitative data and electron microscopy image data in the latent space. The second modeling subunit is used to simulate the dynamic interaction and evolution of all topological features of supporting cells under the action of toxins based on the initial joint feature vector and the preset neural differential equation with the toxin exposure time as the implicit time parameter, and obtain the dynamic model of the barrier function of the supporting cells changing with time. The third modeling subunit is used to solve the dynamic model by evolution of the joint feature vector. The joint feature vector is obtained by integrating the neural differential equation from the start time to the end time of toxin exposure using the adjoint method. The topological features of the joint feature vector are the result of the interaction of the toxin perturbation. The fourth modeling subunit is used to generate a topological dynamics representation based on the evolved joint feature vector. The dimensions representing the blood-testis barrier function are decoded from the evolved joint feature vector. These dimensions include the expression dynamics of tight junction-related proteins, the topological stability of the gene regulatory network, and the physical structural integrity of intercellular connections, thus obtaining a topological dynamics representation that supports the cell barrier function.

9. The predictive system for vomitoxin-induced damage to testicular Sertoli cells according to claim 6, characterized in that, The simulation unit includes: The first simulation subunit is used to map the topological dynamics representation into a two-dimensional grid, where each cell represents a supporting cell, and its initial state is assigned by the flow cytometry detection data to obtain a spatial distribution model that reflects the initial state of the supporting cell population. The second simulation subunit is used to define the cell state evolution rules based on the spatial distribution model. The neighbor coupling rule based on the percolation theory is used to simulate the diffusion of apoptosis signals through cell connections in the population, thereby obtaining an evolution function that supports the dynamic interaction of the cell population under toxin stress. The third simulation subunit is used to iteratively apply evolution functions based on the cellular automata model to simulate the dynamic process of supporting cell populations from the onset of apoptosis to the formation of connected apoptotic clusters larger than a preset area under exposure to toxins from low to high doses. It also records the size of the largest connected apoptotic cluster in the population after each simulation, and obtains a data sequence reflecting the degree of connectivity collapse of the cell population under each toxin level. The fourth simulation subunit is used to perform analysis based on the data sequence, wherein by identifying mutation points in the data sequence, the dose corresponding to the mutation point is the critical point for the occurrence of percolation phase transition, and the critical damage threshold for the integrity of the blood-testis barrier is obtained.

10. The predictive system for vomitoxin-induced damage to testicular Sertoli cells according to claim 6, characterized in that, The building unit includes: The first construction subunit is used to obtain a set of coupling parameters relating to the relationship between toxin dose and barrier damage by using the critical damage threshold as a bifurcation point and quantifying the barrier damage intensity using the cell apoptosis rate and cell cycle arrest ratio in the flow cytometry data. The second construction subunit is used to construct a stochastic dynamic potential well model based on the set of coupling parameters according to the correlation relationship. The potential well model of toxin dose-barrier damage is obtained by using a non-equilibrium state function with toxin dose as the control parameter and barrier damage as the state variable.

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