Cell carrier information processing method and system

Abstract

The invention relates to the technical field of cell carrier information processing, in particular to a cell carrier information processing method and system. The method comprises the following steps: acquiring sample plant part data, selecting a proper plant tissue culture medium according to the data, and culturing a plant cell carrier, so as to obtain plant cell carrier data; according to plant character improvement demand data, carrying out multi-gene improvement on the cell carrier, and carrying out gene stability detection; carrying out cell carrier inoculation on the sample plant part based on gene stability data, and carrying out environmental adaptability detection and stability evaluation; detecting the gene transduction efficiency of the cell carrier, analyzing the reliability of a gene improvement strategy, and optimizing a multiple gene improvement strategy according to an analysis result; according to the invention, cell carrier information processing is optimized, so that cell carrier information processing is more accurate and efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of cell carrier information processing, and in particular to a cell carrier information processing method and system. Background Art

[0002] Plant cell vectors show great application potential in crop improvement, pest control, and cultivation of stress-resistant plants. Traditional plant cell vector technology mainly relies on plant virus vectors, Agrobacterium-mediated vectors, and direct transformation methods, but these methods also face certain limitations, such as low transduction efficiency, poor gene stability, and uneven expression levels of exogenous genes. In order to improve the transduction efficiency and stability of plant cell vectors, researchers are constantly exploring new vector systems and optimization schemes. The selection of cell vectors, the method of gene introduction, the stability of the vector, and the regulation of gene expression all have an important influence on the effect of gene improvement. Traditional gene transduction methods often rely on the transformation of a single gene and lack efficient integration of multiple gene improvements, resulting in unstable gene combinations or unsatisfactory effects in the process of plant improvement. However, a cell vector information processing method has the problem of inaccurate evaluation of cell vector stability and inaccurate prediction of cell vector gene expression. Summary of the invention

[0003] Based on this, it is necessary to provide a cell carrier information processing method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a cell carrier information processing method comprises the following steps:

[0005] Step S1: obtaining sample plant part data; selecting a culture medium according to the sample plant part data to obtain plant tissue culture medium data; culturing a plant cell carrier according to the sample plant part data and the plant tissue culture medium data to obtain plant cell carrier data;

[0006] Step S2: obtaining plant trait improvement demand data; performing multiple gene improvements on plant cell vector data according to the plant trait improvement demand data to obtain cell vector multiple gene improvement data; performing plant cell vector gene stability detection according to the cell vector multiple gene improvement data to obtain plant cell vector gene stability data;

[0007] Step S3: performing cell vector inoculation on the sample plant part data according to the plant cell vector gene stability data to obtain cell vector inoculation data; performing environmental adaptability detection on the plant cell vector inoculation data to obtain cell vector environmental adaptability data; performing cell vector stability assessment according to the cell vector environmental adaptability data to obtain plant cell vector stability data;

[0008] Step S4: performing cell vector gene transduction efficiency detection based on the plant cell vector stability data to obtain cell vector gene transduction efficiency data; performing gene improvement strategy reliability analysis on the cell vector gene transduction efficiency data to obtain vector gene improvement strategy reliability data; performing improvement and optimization on the cell vector multiple gene improvement data based on the vector gene improvement strategy reliability data to obtain vector multiple gene improvement optimization data.

[0009] Each step of the cell vector information processing method of the present invention not only improves the gene transduction efficiency of the plant cell vector through precise data processing and optimization, but also significantly enhances gene stability, expression coordination and environmental adaptability, promotes the overall efficiency of plant gene improvement, obtains plant sample data and combines accurate culture medium selection to create excellent conditions for the cultivation of cell vectors. This method can select the most suitable culture medium according to the characteristics of different plant parts, improve the survival rate and division activity of cells, and lay a solid foundation for subsequent gene transduction and improvement. On this basis, further through multiple gene improvements, the gene improvement plan can be tailored according to the needs of different plant traits. Through precise gene improvement and stability detection, the stable expression of exogenous genes is ensured, the gene silencing or instability phenomenon that occurs is avoided, and the stability of transgenic plants and the reliability of genetic transmission are improved. In the cell vector inoculation and environmental adaptability detection links, through meticulous adaptability evaluation, the performance of the cell vector under different environmental conditions is ensured, and the cell vector is prevented from losing function or failing to maintain good gene expression in harsh environments. This process helps to improve the adaptability of plants in actual agricultural production and enhances the stability of plant cell vectors in long-term cultivation. In addition, through the detection of gene transduction efficiency of cell vectors, the inefficiency or instability in the transduction process can be discovered in time, ensuring that each cell vector can achieve the expected effect in the gene improvement process. The reliability analysis of the gene improvement strategy further optimizes the strategy of the entire gene improvement, avoiding the negative effects caused by improper gene interaction or uncoordinated gene expression. By optimizing the gene improvement strategy, the maximization effect of multiple gene improvement data can be ensured, so that the obtained plants not only have the target traits, but also show excellent gene expression levels and stability in each link. This layer-by-layer optimization strategy not only ensures the high efficiency of improved plants, but also reduces the risks in the experiment, making the process of gene improvement more accurate and reliable. Therefore, the present invention is an optimization process made to a traditional cell vector information processing method and system, which solves the problem of inaccurate stability evaluation of cell vectors and inaccurate prediction of cell vector gene expression in a traditional cell vector information processing method and system, and improves the accuracy of stability evaluation of cell vectors and the accuracy of prediction of cell vector gene expression.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: obtaining sample plant part data;

[0012] Step S12: selecting plant tissue culture medium according to the sample plant part data, thereby obtaining plant tissue culture medium data;

[0013] Step S13: performing plant tissue structure culture on the sample plant part data according to the plant tissue culture medium data to obtain plant tissue structure data;

[0014] Step S14: performing plant cell carrier culture according to the plant tissue structure data and the plant tissue culture medium data, thereby obtaining plant cell carrier data.

[0015] The present invention can provide ideal conditions for subsequent cell culture by accurately obtaining sample plant part data and selecting suitable plant tissue culture medium, thereby improving the survival rate and division activity of plant cells. According to the characteristics and requirements of the sample plant parts, selecting a suitable culture medium can optimize the growth environment of plant cells, promote efficient cell division and tissue formation, and lay the foundation for the subsequent transformation of plant cell vectors. Through plant tissue structure culture, the tissue development process of plants in a natural state can be better simulated, thereby improving the culture quality of cells and gene transduction efficiency. At the same time, this process helps to determine the best cell tissue type, so that the transformation effect of subsequent cell vectors is maximized. Combining plant tissue structure data with culture medium information, further optimizing the culture conditions of plant cell vectors can improve the integration efficiency and expression stability of exogenous genes, thereby providing high-quality cell vectors for plant gene improvement. This series of steps ensures the culture effect of plant cell vectors by accurately controlling each link, providing a strong guarantee for subsequent genetic modification and improvement.

[0016] Preferably, step S14 comprises the following steps:

[0017] Step S141: extracting plant tissue structure cells according to the plant tissue structure data, thereby obtaining plant tissue structure cell data;

[0018] Step S142: performing cell division activity detection on the plant tissue structure cell data, thereby obtaining plant cell division activity data;

[0019] Step S143: Calculating the cell division rate of the plant cell division activity data to obtain cell division rate data;

[0020] Step S144: performing high transformation efficiency detection according to the plant tissue structure cell data to obtain plant tissue structure high transformation efficiency data;

[0021] Step S145: selecting cell carrier tissues from the plant tissue structure cell data according to the plant tissue structure high transformation efficiency data and the cell division rate data to obtain plant cell carrier tissue data;

[0022] Step S146: performing plant cell carrier culture on the plant cell carrier tissue data according to the plant tissue culture medium data, thereby obtaining plant cell carrier data.

[0023] The present invention can obtain a cell population with high potential by extracting cells from plant tissue structure data, ensuring that the cells obtained in subsequent culture have excellent growth and transformation performance. Cell division activity detection helps to screen out cells with high division ability, which is crucial for improving the efficiency and quality of cell culture. By calculating the cell division rate, the growth conditions of the cells can be further optimized to ensure that the cells can proliferate at the optimal rate during the culture process to avoid stagnant or uneven cell growth. In addition, high transformation efficiency detection can ensure that the selected cells have stronger gene absorption and integration capabilities, further improving the success rate of genetic modification. On this basis, through the combination of cell division rate and transformation efficiency, the most suitable cell type for vector culture can be scientifically selected, thereby improving the transformation effect of the vector. Finally, by combining suitable plant tissue culture medium for cell vector culture, the growth stability and transduction efficiency of the cells can be further improved, providing high-quality vector cells for subsequent gene modification. This series of steps realizes the all-round optimization of cell culture, transformation efficiency and genetic modification, ensuring the high efficiency, stability and operability of the cell vector.

[0024] Preferably, step S2 comprises the following steps:

[0025] Step S21: Acquiring plant trait improvement demand data;

[0026] Step S22: performing multiple gene improvement on the plant cell vector data according to the plant trait improvement requirement data to obtain cell vector multiple gene improvement data;

[0027] Step S23: performing gene expression coordination detection according to the cell vector multiple gene improvement data to obtain gene expression coordination data;

[0028] Step S24: performing gene silencing prediction based on the cell vector multiple gene improvement data to obtain cell vector gene silencing data;

[0029] Step S25: Perform plant cell vector gene stability detection according to the gene expression coordination data and the cell vector gene silencing data to obtain plant cell vector gene stability data.

[0030] The present invention can clarify the specific goals of plant improvement and ensure the accuracy of the direction of gene improvement by obtaining plant trait improvement demand data. On this basis, multiple gene improvements can simultaneously improve multiple target traits, thereby enhancing the comprehensive performance of plants, such as disease resistance, yield or stress tolerance. Gene expression coordination detection helps to evaluate the collaborative work of multiple genes in cell vectors, ensuring that the expression of each gene does not conflict and plays the best effect. Through gene silencing prediction, the gene silencing phenomenon that occurs can be effectively prevented, and the target gene cannot be normally expressed due to silencing, thereby improving the stability and long-term effect of gene improvement. Finally, gene stability detection combines gene expression coordination and gene silencing data, comprehensively evaluates the gene stability of cell vectors, ensures that the modified gene is expressed stably in the plant body for a long time, and provides reliable protection for subsequent gene transfer and trait inheritance. The beneficial effect of this series of steps is to achieve accurate gene improvement, improve transformation efficiency and reduce potential gene expression instability problems, and ensure the efficiency and sustainability of plant trait improvement.

[0031] Preferably, step S22 includes the following steps:

[0032] Step S221: designing a cell vector gene modification strategy according to the plant trait improvement demand data to obtain cell vector gene modification strategy data;

[0033] Step S222: collecting transformation strategy genes from the cell vector gene transformation strategy data to obtain vector transformation strategy multiple gene data;

[0034] Step S223: evaluating the vector tolerance of the plant cell vector data according to the vector transformation strategy multiple gene data to obtain vector gene tolerance data;

[0035] Step S224: performing gene aggregation according to the multiple gene data of the vector transformation strategy and the vector gene bearing capacity data, thereby obtaining multiple gene aggregation data;

[0036] Step S225: Perform multiple gene improvements on the plant cell vector data according to the multiple gene aggregation data to obtain cell vector multiple gene improvement data.

[0037] The present invention can ensure that the genetic modification work is accurately targeted at specific target traits by designing the genetic modification strategy according to the plant trait improvement demand data, thereby improving the transformation efficiency and effect. The genetic data required for the transformation strategy can ensure that the gene combination involved in the genetic modification is optimal, thereby improving the accuracy and diversity of the genetic modification. The vector bearing capacity assessment helps to screen out the cell carrier that can carry the target gene, ensuring that in the subsequent transformation process, the carrier can withstand and stably express multiple target genes, thereby avoiding the occurrence of carrier overload or unstable gene expression. The gene polymerization step maximizes the genetic modification effect by effectively combining multiple target genes, ensuring that different genes can work together without antagonism or adverse interactions, and by performing multiple gene improvements on the plant cell carrier, multiple optimized traits can be introduced in the same plant, thereby improving the comprehensive performance of the plant, such as disease resistance, yield, etc. The beneficial effect of this series of steps is that through systematic gene design, screening and optimization, the success rate and stability of multiple genetic modifications are improved, providing strong technical support for plant trait improvement.

[0038] Preferably, step S23 includes the following steps:

[0039] Step S231: performing gene interaction analysis based on the cell vector multiple gene improvement data to obtain multiple gene interaction data;

[0040] Step S232: evaluating the gene synergistic effect according to the multiple gene interaction data to obtain multiple gene synergistic effect data;

[0041] Step S233: predicting gene function complementarity enhancement on the multiple gene synergistic effect data to obtain gene function complementarity enhancement data;

[0042] Step S234: predicting gene co-expression enhancement according to the multiple gene synergistic effect data and the gene function complementarity enhancement data to obtain cell vector gene co-expression enhancement data;

[0043] Step S235: performing gene expression coordination detection according to the gene function complementarity enhancement data and the cell vector gene co-expression enhancement data to obtain gene expression coordination data.

[0044] The present invention can reveal the interaction between different genes by analyzing the gene interaction of the cell vector multiple gene improvement data, thereby providing a scientific basis for subsequent genetic modification and ensuring the collaborative work of each gene in the cell. Gene synergy effect evaluation helps to optimize the gene combination, ensure that multiple genes play a role at the same time, avoid negative effects on each other, and improve the overall benefit of genetic modification. Through the prediction of gene function complementarity enhancement, the complementary effect between genes can be identified and strengthened, thereby enhancing the functional performance of each gene and improving the effect of plant trait improvement. Gene co-expression enhancement prediction further optimizes the cooperation between genes, ensures that the expression of genes in cells can be coordinated, thereby improving the transduction efficiency and stability of cell vectors, and through gene expression coordination detection, it can ensure that the expression of multiple genes in cells reaches the best balance, improves the stability and predictability of gene expression, thereby ensuring the success rate and long-term effect of plant improvement. Overall, these steps ensure the efficiency, stability and sustainability of multiple gene modification by accurately optimizing the synergy and expression regulation of genes.

[0045] Preferably, step S24 comprises the following steps:

[0046] Step S241: evaluating the gene antagonistic effect based on the cell vector multiple gene improvement data to obtain multiple gene antagonistic effect data;

[0047] Step S242: performing gene expression competition growth evaluation on the multiple gene antagonism effect data to obtain gene expression competition growth data;

[0048] Step S243: performing gene expression inhibition detection according to the gene expression competition growth data to obtain cell vector gene expression inhibition data;

[0049] Step S244: performing gene expression reduction calculation on the cell vector gene expression inhibition data to obtain gene expression reduction data;

[0050] Step S245: predicting the collapse of the gene regulatory network according to the gene expression competition growth data to obtain the gene regulatory network collapse data;

[0051] Step S246: Gene silencing prediction is performed based on the gene regulatory network collapse data and the gene expression decrease data to obtain cell vector gene silencing data.

[0052] The present invention can identify the negative interactions between different genes through the evaluation of gene antagonism effect, avoid the inhibition or interference of gene function in the process of multiple gene improvement, thereby optimizing the gene combination and ensuring the effectiveness of genetic modification. Gene expression competition growth evaluation helps to analyze the expression competition of multiple genes in cells, timely discover and solve the problem of imbalanced expression, and ensure that the target gene can fully play its role. Gene expression inhibition detection can effectively identify and avoid the expression inhibition phenomenon caused by gene interaction, thereby improving the stability of the transformation effect. Through the calculation of gene expression decrease, the reduction range of gene expression can be accurately evaluated, potential expression problems can be warned in advance, and a basis can be provided for optimizing gene expression. Gene regulation network collapse prediction helps to evaluate the imbalance of gene regulation system occurring in the process of multiple gene modification, avoid the influence of network collapse on the normal development and performance of plants, and through gene silencing prediction, the silencing phenomenon caused by the bad gene regulation of target genes can be prevented, and the stable inheritance and continuous expression of target traits can be ensured. This series of steps ensures the stability and long-term effect of genetic modification and maximizes the success rate of plant trait improvement by accurately evaluating and optimizing gene interaction and expression regulation.

[0053] Preferably, step S3 comprises the following steps:

[0054] Step S31: performing cell vector inoculation on sample plant part data according to plant cell vector gene stability data to obtain plant cell vector inoculation data;

[0055] Step S32: Performing environmental adaptability testing on the plant cell carrier inoculation data to obtain cell carrier environmental adaptability data;

[0056] Step S33: Calculating the cell carrier survival rate according to the cell carrier environmental adaptability data to obtain cell carrier survival rate data;

[0057] Step S34: evaluating the stability of the cell vector according to the cell vector survival rate data and the cell vector environmental adaptability data to obtain the plant cell vector stability data.

[0058] The present invention can ensure that the selected cell vector can be effectively inoculated in plant tissues by analyzing the gene stability data of the plant cell vector, thereby ensuring the smooth progress of genetic modification. On this basis, environmental adaptability testing is carried out to help evaluate the performance of the cell vector under different environmental conditions and ensure that the vector has good adaptability in different growth environments. The acquisition of environmental adaptability data enables subsequent cultivation and growth processes to be carried out under optimal conditions, thereby improving the success rate of transgenic plants. By calculating the survival rate of the cell vector, the growth state and adaptability of the plant cell after inoculation can be accurately understood. The level of the survival rate directly reflects the stability of the vector in the plant body and whether it can continue to exert the effect of genetic modification. In addition, by combining the survival rate data and the environmental adaptability data to evaluate the stability of the cell vector, the stability performance of the cell vector under the action of multiple environmental factors can be fully understood, and its performance and gene stability in the long-term growth process can be predicted. This comprehensive evaluation step can effectively identify the risk points, adjust the unstable vector or gene expression in advance, avoid unnecessary failure or inefficient gene expression phenomena, and integrate the stability data of the plant cell vector to ensure that the genetic modification effect remains stable in the plant body for a long time, and improve the yield, resistance or other trait improvement effects of transgenic plants. The beneficial effect of this method is that through systematic detection and evaluation, it not only improves the success rate of gene transduction, but also effectively ensures the long-term stability of genetic modification, providing reliable technical support for plant breeding and agricultural production.

[0059] Preferably, step S4 comprises the following steps:

[0060] Step S41: performing cell vector gene transduction efficiency detection according to the plant cell vector stability data to obtain cell vector gene transduction efficiency data;

[0061] Step S42: evaluating the exogenous gene expression of the cell vector gene transduction efficiency data to obtain the cell vector exogenous gene expression data;

[0062] Step S43: performing gene improvement strategy reliability analysis on the cell vector foreign gene expression data and the cell vector gene transduction efficiency data to obtain vector gene improvement strategy reliability data;

[0063] Step S44: improving and optimizing the cell vector multiple gene improvement data according to the vector gene improvement strategy reliability data to obtain the vector multiple gene improvement optimization data.

[0064] The present invention can quantify the success rate of gene transduction by detecting the gene transduction efficiency of cell vectors on the stability data of plant cell vectors, ensure that the gene can be effectively introduced into the target plant cells, thereby laying the foundation for subsequent gene improvement and expression. This detection step can not only help identify efficient vector systems, but also optimize experimental design. Next, by evaluating the expression of exogenous genes on the gene transduction efficiency data of cell vectors, the expression level and stability of exogenous genes in plant cells can be judged, providing data support for further gene function research. The stability of exogenous gene expression directly affects the effect of improving plant traits, so evaluating and optimizing it is a key step to ensure the success of genetic engineering. The data obtained in this process can provide important information for the improvement of gene expression, thereby further improving the resistance, yield or traits of plants. In addition, combining the gene transduction efficiency and exogenous gene expression data to carry out gene improvement strategy reliability analysis, it is possible to evaluate the effectiveness and feasibility of different improvement strategies, and provide a scientific basis for subsequent experiments. Through this reliability analysis, it is possible to quickly judge which improvement strategies have a higher success rate, avoid investing a lot of time and resources in ineffective improvement work, and improve research efficiency. Finally, based on the reliability data of the vector gene improvement strategy, the multiple gene improvements of the cell vector can be optimized. This optimization process can further improve the performance of the vector in genetic modification, not only ensuring the efficiency and stability of gene introduction, but also enabling the improved plants to have more excellent trait performance. By continuously optimizing the multiple gene improvement data, it is ensured that the gene improvement effect under different experimental conditions is maximized, and significant improvements in plant traits are achieved.

[0065] The present invention also provides a cell carrier information processing system for executing the cell carrier information processing method as described above, the cell carrier information processing system comprising:

[0066] Plant cell carrier culture module: obtain sample plant part data; select culture medium according to the sample plant part data to obtain plant tissue culture medium data; culture plant cell carrier according to the sample plant part data and plant tissue culture medium data to obtain plant cell carrier data;

[0067] Gene stability detection module: obtain plant trait improvement demand data; perform multiple gene improvements on plant cell vector data according to the plant trait improvement demand data to obtain cell vector multiple gene improvement data; perform plant cell vector gene stability detection according to the cell vector multiple gene improvement data to obtain plant cell vector gene stability data;

[0068] Cell vector stability assessment module: perform cell vector inoculation on sample plant part data according to plant cell vector gene stability data to obtain cell vector inoculation data; perform environmental adaptability test on plant cell vector inoculation data to obtain cell vector environmental adaptability data; perform cell vector stability assessment according to cell vector environmental adaptability data to obtain plant cell vector stability data;

[0069] Gene improvement and optimization module: perform cell vector gene transduction efficiency detection based on plant cell vector stability data to obtain cell vector gene transduction efficiency data; perform gene improvement strategy reliability analysis on cell vector gene transduction efficiency data to obtain vector gene improvement strategy reliability data; perform improvement and optimization on cell vector multiple gene improvement data based on vector gene improvement strategy reliability data to obtain vector multiple gene improvement optimization data.

[0070] The invention has the beneficial effect that each step of the cell vector information processing method not only improves the gene transduction efficiency of the plant cell vector through precise data processing and optimization, but also significantly enhances gene stability, expression coordination and environmental adaptability, promotes the overall efficiency of plant gene improvement, obtains plant sample data and combines accurate culture medium selection to create excellent conditions for the cultivation of cell vectors. This method can select the most suitable culture medium according to the characteristics of different plant parts, improve the survival rate and division activity of cells, and lay a solid foundation for subsequent gene transduction and improvement. On this basis, further through multiple gene improvements, the gene improvement plan can be tailored according to the needs of different plant traits. Through precise gene improvement and stability detection, the stable expression of exogenous genes is ensured, the gene silencing or instability phenomenon is avoided, and the stability of transgenic plants and the reliability of genetic transmission are improved. In the cell vector inoculation and environmental adaptability detection links, the performance of the cell vector under different environmental conditions is ensured through meticulous adaptability evaluation, and the cell vector is prevented from losing function or failing to maintain good gene expression in harsh environments. This process helps to improve the adaptability of plants in actual agricultural production and enhances the stability of plant cell vectors in long-term cultivation. In addition, through the detection of gene transduction efficiency of cell vectors, the inefficiency or instability in the transduction process can be discovered in time, ensuring that each cell vector can achieve the expected effect in the gene improvement process. The reliability analysis of the gene improvement strategy further optimizes the strategy of the entire gene improvement, avoiding the negative effects caused by improper gene interaction or uncoordinated gene expression. By optimizing the gene improvement strategy, the maximization effect of multiple gene improvement data can be ensured, so that the obtained plants not only have the target traits, but also show excellent gene expression levels and stability in each link. This layer-by-layer optimization strategy not only ensures the high efficiency of improved plants, but also reduces the risks in the experiment, making the process of gene improvement more accurate and reliable. Therefore, the present invention is an optimization process made to a traditional cell vector information processing method and system, which solves the problem of inaccurate stability evaluation of cell vectors and inaccurate prediction of cell vector gene expression in a traditional cell vector information processing method and system, and improves the accuracy of stability evaluation of cell vectors and the accuracy of prediction of cell vector gene expression. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a schematic diagram of the steps of a cell carrier information processing method and system;

[0072] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0073] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0074] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0075] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0076] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0077] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0078] To achieve this, please refer to Figures 1 to 3 , a cell carrier information processing method, comprising the following steps:

[0079] Step S1: obtaining sample plant part data; selecting a culture medium according to the sample plant part data to obtain plant tissue culture medium data; culturing a plant cell carrier according to the sample plant part data and the plant tissue culture medium data to obtain plant cell carrier data;

[0080] Step S2: obtaining plant trait improvement demand data; performing multiple gene improvements on plant cell vector data according to the plant trait improvement demand data to obtain cell vector multiple gene improvement data; performing plant cell vector gene stability detection according to the cell vector multiple gene improvement data to obtain plant cell vector gene stability data;

[0081] Step S3: performing cell vector inoculation on the sample plant part data according to the plant cell vector gene stability data to obtain cell vector inoculation data; performing environmental adaptability detection on the plant cell vector inoculation data to obtain cell vector environmental adaptability data; performing cell vector stability assessment according to the cell vector environmental adaptability data to obtain plant cell vector stability data;

[0082] Step S4: performing cell vector gene transduction efficiency detection based on the plant cell vector stability data to obtain cell vector gene transduction efficiency data; performing gene improvement strategy reliability analysis on the cell vector gene transduction efficiency data to obtain vector gene improvement strategy reliability data; performing improvement and optimization on the cell vector multiple gene improvement data based on the vector gene improvement strategy reliability data to obtain vector multiple gene improvement optimization data.

[0083] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a process flow of a cell carrier information processing method of the present invention. In this example, the cell carrier information processing method includes the following steps:

[0084] Step S1: obtaining sample plant part data; selecting a culture medium according to the sample plant part data to obtain plant tissue culture medium data; culturing a plant cell carrier according to the sample plant part data and the plant tissue culture medium data to obtain plant cell carrier data;

[0085] In an embodiment of the present invention, plant parts with specific traits are selected as samples, such as leaves, roots or stems. The surface of the plant parts is sterilely cut and plant tissue samples are collected with sterile tools. The collected plant parts are preliminarily examined at the cell level using a microscope to ensure that they are not contaminated. According to the characteristics of the plant parts (such as the hardness and activity of the tissue), the corresponding culture medium is selected, such as using conventional plant tissue culture medium such as MS culture medium and B5 culture medium, the components of the culture medium include nitrogen source, carbon source, minerals, vitamins, etc., and the pH is adjusted to a suitable range (such as 5.8-6.0). Next, the plant tissue is cultured using the culture medium to observe whether the plant cells can divide and grow normally. In this process, a temperature-controlled incubator is used to maintain a suitable temperature (such as 25°C) and provide sufficient light (16 hours of light, 8 hours of darkness) to promote the growth of plant cells. During the culture process, the growth state and cell activity of the plant cells in the culture dish are regularly checked to determine the effectiveness of the culture medium. The plant cell carrier data obtained by cell microscope observation.

[0086] Step S2: obtaining plant trait improvement demand data; performing multiple gene improvements on plant cell vector data according to the plant trait improvement demand data to obtain cell vector multiple gene improvement data; performing plant cell vector gene stability detection according to the cell vector multiple gene improvement data to obtain plant cell vector gene stability data;

[0087] In an embodiment of the present invention, when obtaining plant trait improvement demand data, the improvement demand of the target plant can be determined by questionnaire survey, literature research or professional database, such as improving drought resistance, disease resistance or improving yield. Based on these needs, genes related to the target trait are selected, such as known drought resistance genes or disease resistance genes. Using technologies such as CRISPR-Cas9, gene guns or Agrobacterium transformation, multiple gene improvements are performed for plant cell vectors. For example, multiple drought resistance genes are introduced into plant cells, and the selected genes are introduced into plant cells using an Agrobacterium-mediated method. After the multiple gene improvements are completed, gene stability detection is required. The insertion of exogenous genes in plant cells is verified by PCR technology, and whether the integration of genes is stable is detected by Southern Blot analysis to ensure that the target gene is stably expressed in plant cells. The stability and expression of genes in plants are further confirmed by gene expression analysis (such as RT-PCR, qPCR), thereby obtaining stable plant cell vector gene stability data.

[0088] Step S3: performing cell vector inoculation on the sample plant part data according to the plant cell vector gene stability data to obtain cell vector inoculation data; performing environmental adaptability detection on the plant cell vector inoculation data to obtain cell vector environmental adaptability data; performing cell vector stability assessment according to the cell vector environmental adaptability data to obtain plant cell vector stability data;

[0089] In an embodiment of the present invention, after obtaining the gene stability data of the cell vector, a suitable plant part (such as a leaf or a stem tip, etc.) is selected for cell vector inoculation. During inoculation, the genetically modified cell vector is transplanted to the plant part, and the inoculation operation is performed under sterile conditions to ensure that any contamination is avoided during the operation. The inoculated plant cells need to be placed in a special incubator for growth and adaptation, and the culture environment is controlled at a suitable temperature and humidity. The healing of the inoculated part is regularly observed and the growth state is recorded. In order to further evaluate the environmental adaptability of the cell vector, the survival and adaptability of the plant cell vector can be tested by simulating different environmental conditions (such as temperature, humidity, light, etc.). For example, simulated tests of stress conditions such as drought resistance, low temperature, and high temperature are performed, and growth indicators (such as root length, number of leaves, stem thickness, etc.) are used to evaluate the adaptability of plant cells. According to the experimental data of different environmental adaptability, the stability of the plant cell vector can be further analyzed to ensure that the modified plant can grow stably under different environments, and the stability data of the plant cell vector is obtained.

[0090] Step S4: performing cell vector gene transduction efficiency detection based on the plant cell vector stability data to obtain cell vector gene transduction efficiency data; performing gene improvement strategy reliability analysis on the cell vector gene transduction efficiency data to obtain vector gene improvement strategy reliability data; performing improvement and optimization on the cell vector multiple gene improvement data based on the vector gene improvement strategy reliability data to obtain vector multiple gene improvement optimization data.

[0091] In the embodiment of the present invention, the detection of the gene transduction efficiency of the cell vector needs to be verified by an actual transduction experiment, and flow cytometry, fluorescence microscopy or real-time fluorescence quantitative PCR technology is used to detect the transduction efficiency of exogenous genes in plant cells. For example, the transduction efficiency is evaluated by whether there is a fluorescent signal in the plant cells after genetic modification. Flow cytometry can screen cells, measure the expression of exogenous genes, and judge the level of transduction efficiency. The reliability analysis of the gene improvement strategy can verify the stability and effect of the improvement strategy by repeated experiments. For example, different batches of cell vectors are selected for gene transduction experiments, and the results are statistically analyzed to evaluate the reliability of the gene improvement strategy. If the gene transduction efficiency is low, the gene improvement strategy can be further optimized. By adjusting the design of the vector, optimizing the selection of genes, or changing the transformation conditions (such as changing the concentration of the transforming agent, the transformation temperature, etc.), the transduction efficiency is higher. When optimizing multiple gene improvements, the data such as transduction efficiency, gene expression, stability, etc. can be combined for comprehensive evaluation to obtain the best gene improvement optimization strategy, ensuring that plant cells can show improved properties in the best state.

[0092] Preferably, step S1 comprises the following steps:

[0093] Step S11: obtaining sample plant part data;

[0094] Step S12: selecting plant tissue culture medium according to the sample plant part data, thereby obtaining plant tissue culture medium data;

[0095] Step S13: performing plant tissue structure culture on the sample plant part data according to the plant tissue culture medium data to obtain plant tissue structure data;

[0096] Step S14: performing plant cell carrier culture according to the plant tissue structure data and the plant tissue culture medium data, thereby obtaining plant cell carrier data.

[0097] In the embodiment of the present invention, the target plant species is selected and the appropriate plant parts are determined for sampling. According to the experimental requirements, roots, stems, leaves, flowers and other parts can be selected, among which the common sample sources are the apical meristem, embryo or leaf of the plant. Through aseptic operation, the plant parts are cut with a sterilized scalpel or scissors. For the collection of plant samples, it should be ensured that the collection process is pollution-free to avoid adverse effects of the external environment on plant cells. During collection, care should be taken to avoid mechanical damage to the sampling part to retain sufficient cell activity. The collected plant part samples are placed in a sterile container, and the hydration of the plant samples is maintained with an appropriate buffer or sterile water, and the samples are transferred to a sterile workbench as soon as possible for subsequent processing. According to the requirements of different plant tissues, common culture media such as MS culture medium, B5 culture medium or N6 culture medium are selected. These culture media are widely used in plant tissue culture and contain basic salts, carbon sources, nitrogen sources, vitamins and hormones required for plant growth. For the culture of certain specific parts (such as leaves or roots), it is also necessary to add appropriate amounts of auxin (such as IAA, NAA) or cytokinin (such as BAP, KT) and other plant hormones according to the requirements of plant growth regulators. By analyzing the characteristics of the sample plant parts, a suitable culture medium formula is formulated, and the pH value is adjusted to 6.0-6.5 to ensure that plant cells can grow and divide normally in the culture medium. According to the data formula of the selected culture medium, prepare a sufficient amount of culture medium solution to ensure the uniformity and sterility of the culture medium. After the culture medium is selected, the plant tissue structure culture is carried out. The prepared plant sample parts are cut into small pieces, usually the size of each sample is about 0.5cm×0.5cm, to ensure that the surface is smooth and remove unnecessary tissue parts. The cut plant parts should be immediately placed in the prepared plant tissue culture medium to ensure that the surface contacting the culture medium is uniform and uncontaminated. The culture dish is placed under light conditions for culture, with a light intensity of 50-60μmol / m2 / s, and an appropriate temperature range (usually 25℃) is maintained to promote cell division and plant tissue growth. During this process, observe whether the plant tissue develops in the culture medium and forms callus or other tissue structures. Examine the cultured plant tissue under a microscope, record its growth status and cell division, and replace the culture medium regularly to ensure that the plant tissue structure grows stably under good culture conditions and obtain plant tissue structure data. When the structural data of the plant tissue culture stage is stable, the cell carrier is cultured. At this point, the plant tissue that has been formed can undergo further cell division and proliferation. According to the growth status of the plant tissue, the plant tissue fragments are transferred to a culture dish containing the culture medium to ensure that the plant cells can fully contact the culture medium. During the culture process, the culture conditions are adjusted regularly to control the temperature (usually between 25-28°C), humidity and light cycle (16 hours of light, 8 hours of darkness). According to the growth needs of the plant tissue, specific hormones need to be added in time to promote cell division or specialization.The cultivation of cell carriers will promote the formation of cell colonies and generate cell carriers through the regulation of plant hormones, nutrients and environmental conditions in the culture medium. In this process, cells gradually form tissues through division and proliferation until the required cell number and biological characteristics are reached. The morphological characteristics, division activity and cell density of cells are observed under a microscope, and the development of cells is recorded to obtain plant cell carrier data.

[0098] Preferably, step S14 comprises the following steps:

[0099] Step S141: extracting plant tissue structure cells according to the plant tissue structure data, thereby obtaining plant tissue structure cell data;

[0100] Step S142: performing cell division activity detection on the plant tissue structure cell data, thereby obtaining plant cell division activity data;

[0101] Step S143: Calculating the cell division rate of the plant cell division activity data to obtain cell division rate data;

[0102] Step S144: performing high transformation efficiency detection according to the plant tissue structure cell data to obtain plant tissue structure high transformation efficiency data;

[0103] Step S145: selecting cell carrier tissues from the plant tissue structure cell data according to the plant tissue structure high transformation efficiency data and the cell division rate data to obtain plant cell carrier tissue data;

[0104] Step S146: performing plant cell carrier culture on the plant cell carrier tissue data according to the plant tissue culture medium data, thereby obtaining plant cell carrier data.

[0105] In an embodiment of the present invention, cells are extracted from plant tissues in culture according to plant tissue structure data. Using aseptic operation, plant tissues are cut into small pieces, usually each piece is about 1 mm3 in size. In order to improve the efficiency of cell extraction, enzymatic hydrolysis is used to soften the cell wall. Commonly used enzymes include cellulase, pectinase and hemicellulase, which can effectively decompose the cellulose and pectin components in the plant cell wall and promote the separation of cells. During the operation, the plant tissue block is immersed in a solution containing the above enzymes, the temperature is controlled between 30°C and 37°C, and the enzymatic hydrolysis time is 2-4 hours, and the specific time is adjusted according to the type of plant tissue and the hardness of the tissue. After the enzymatic hydrolysis is completed, a glass rod is used to stir gently to promote the release of cells. The plant tissue that is not completely separated is removed by sieve filtration (pore size is 70-100 μm), a solution containing plant cells is obtained, and the plant tissue structure cell data is recorded. The extracted plant tissue structure cell data is detected by cell division activity by staining. Commonly used stains include DAPI or Hoechst 33342, which can bind to DNA in the cell nucleus and emit fluorescent signals, making it easier to observe the cell division stage. The plant cell suspension is added to a glass slide, gently spread, and allowed to fix at room temperature for 10 minutes, after which the cells are washed with PBS buffer. After the cells are stained, a fluorescence microscope is used to observe and take cell images. By observing the morphological changes of the cell nucleus, such as the appearance of the nuclear division phase, the cell division state can be determined. The number of cells in different division stages is counted, and the division activity index is calculated to evaluate the division activity of plant cells. The distribution and morphology of the cell nucleus are analyzed according to the image analysis software to obtain the plant cell division activity data, and the results are recorded. According to the plant cell division activity data of the previous step, the cell division rate is calculated, the number of cells in the division phase is extracted from the division activity data, and the time range of the observation period is determined. For example, the cell division process within 24 hours or 48 hours is selected for observation. By counting cells, the number of cells undergoing division per unit time (such as per hour) is calculated. The cell division rate can be calculated by the formula: Division rate = number of dividing cells per unit time / total observation time. This calculation can quantitatively describe the degree of cell division activity. The higher the rate, the faster the cell proliferation. By analyzing this rate, the plant cell division rate data is obtained, and combined with the cell division activity data for comprehensive analysis, which provides a reference for subsequent cell culture and genetic modification. When testing the high transformation efficiency of plant tissue structural cells, the plant cell suspension is transferred to a solution containing a transformation reagent (such as PEG, polyethyleneimine (PEI) or gold particles). This method promotes the entry of exogenous genes into plant cells. Common transformation methods include gene gun method and electroporation method. In this step, the transformation experiment is carried out using gene gun method. The DNA to be transformed, such as the exogenous gene, is mixed with gold particles, and the gold particles are shot into the plant cells by high-pressure gas.After the transformation process, the cells are inoculated into a culture medium containing selective antibiotics to screen out successfully transformed cells. By observing the growth of the transformed cells, the transformation efficiency is calculated, that is, the ratio of the number of successfully transformed cells to the total number of cells, and the high transformation efficiency data of the plant tissue structure is obtained. According to the high transformation efficiency data of the plant tissue structure obtained above and the cell division rate data, the best performing cells are selected for screening of cell carrier tissues. Cells with high transformation efficiency and fast cell division rate are selected as target carrier cells. By comparing the transformation efficiency and division rate of different cell populations, cell populations with excellent transformation ability and high division rate are screened as cell carrier tissues. In order to further optimize the selection process of cell carrier tissues, control groups and experimental groups are set for comparative analysis, and data screening is performed by statistical methods to ensure that the selected cells have good stability and activity. According to these data, the most suitable cell population is selected as a carrier to obtain plant cell carrier tissue data. Select a suitable culture medium, such as MS culture medium, and add an appropriate amount of plant hormones such as cytokinin (BAP) or auxin (IAA) according to the growth requirements of the cells. Under a sterile environment, place the selected plant cell carrier tissue in a culture dish or culture bottle to ensure that the cells are fully exposed to the culture medium. During the culture process, control environmental conditions such as temperature (usually 25-28°C), humidity and light cycle (16 hours of light, 8 hours of darkness). Observe the growth status of the cells under a microscope and replace the culture medium regularly to maintain the nutrient supply of the cells. As the cells proliferate, collect the cells and perform subsequent genetic manipulations. The plant cell carrier data obtained through this step can be used for further genetic modification or cell function research.

[0106] Preferably, step S2 comprises the following steps:

[0107] Step S21: Acquiring plant trait improvement demand data;

[0108] Step S22: performing multiple gene improvement on the plant cell vector data according to the plant trait improvement requirement data to obtain cell vector multiple gene improvement data;

[0109] Step S23: performing gene expression coordination detection according to the cell vector multiple gene improvement data to obtain gene expression coordination data;

[0110] Step S24: performing gene silencing prediction based on the cell vector multiple gene improvement data to obtain cell vector gene silencing data;

[0111] Step S25: Perform plant cell vector gene stability detection according to the gene expression coordination data and the cell vector gene silencing data to obtain plant cell vector gene stability data.

[0112] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0113] Step S21: Acquiring plant trait improvement demand data;

[0114] In an embodiment of the present invention, the process of obtaining plant trait improvement demand data begins with a detailed trait analysis of the target plant. This step is usually performed by collecting phenotypic data of existing plant varieties and market demand for crops. The specific operation is to record key traits such as growth cycle, yield, disease and pest resistance, and stress tolerance of each plant variety through field trials or historical trait data collection. Use remote sensing technology or drone technology to monitor field plots and regularly obtain data on the impact of environmental factors such as soil and climate on plant growth. Combine these data with the needs assessment of agricultural experts to clarify which traits need to be improved first (such as increasing yield, enhancing disease resistance, etc.). Generate plant trait improvement demand data through data mining and statistical analysis.

[0115] Step S22: performing multiple gene improvement on the plant cell vector data according to the plant trait improvement requirement data to obtain cell vector multiple gene improvement data;

[0116] In an embodiment of the present invention, multiple candidate genes related to the target trait are selected, which are related to the disease resistance gene, drought resistance gene or nutrient absorption gene of the plant. Then, these genes are introduced into the plant cell vector by genetic engineering methods. Commonly used vectors include plant transformation vectors, such as Ti plasmids or gene guns. In operation, the target gene is inserted into the vector genome using molecular cloning technology, and the modified vector is introduced into the plant cell by Agrobacterium-mediated transformation or gene guns. During the transformation process, appropriate selection marker genes, such as antibiotic resistance genes, are used to screen successfully transformed cells. The success of multiple gene introduction is confirmed by PCR and gene sequencing, and the cell vector multiple gene improvement data is obtained.

[0117] Step S23: performing gene expression coordination detection according to the cell vector multiple gene improvement data to obtain gene expression coordination data;

[0118] In the embodiment of the present invention, the cell vector improved by multiple genes is subjected to gene expression coordination detection to ensure that the expression of the target genes does not interfere with each other and can work together. This step uses real-time quantitative PCR (qPCR) and Western blot analysis to monitor the expression level of the target gene, extracts the total RNA of the transformed plant cells, performs reverse transcription synthesis of cDNA, and selects key genes as detection targets. The expression level of each target gene is detected by qPCR technology, and compared with the control group, to evaluate whether the gene expression is within the desired range. In addition, Western blot is used to analyze the protein expression level to ensure that the expression of the target protein is not inhibited. According to the results of qPCR and Western blot, the expression coordination of each gene is calculated to ensure that the gene expression after the multiple gene improvement is coordinated, and the gene expression coordination data is obtained.

[0119] Step S24: performing gene silencing prediction based on the cell vector multiple gene improvement data to obtain cell vector gene silencing data;

[0120] In an embodiment of the present invention, gene silencing prediction is to ensure that there will be no unexpected gene silencing phenomenon after multiple gene improvements. In this step, after constructing transgenic plant cells, the mRNA level of the target gene is detected using RT-qPCR technology to observe whether gene expression inhibition occurs. In order to evaluate gene silencing, a specific RNA interference (RNAi) method or CRISPR-Cas9 system is used to predict and screen the regulatory elements that cause silencing. In the transformed plant cells, the expression level of the exogenous gene is detected, and the probability of gene silencing is evaluated by comparing the gene expression of the original plant cells with that of the transformed cells. If a significant decrease in the expression level of the target gene is observed, RNAi technology can be used to further suppress unnecessary silencing phenomena. Cell vector gene silencing data is obtained by reverse transcription, gene expression analysis and silencing effect evaluation.

[0121] Step S25: Perform plant cell vector gene stability detection according to the gene expression coordination data and the cell vector gene silencing data to obtain plant cell vector gene stability data.

[0122] In the embodiment of the present invention, according to the gene expression coordination data and gene silencing data obtained in steps S23 and S24, it is determined whether the expression of each target gene in the cell vector is stable. Through long-term cultivation of plant cells and regular sampling, the target gene is detected using technologies such as qPCR, Southern blot and Northern blot. qPCR is used to detect the expression of the gene, Southern blot is used to analyze whether the gene is stably integrated into the plant genome, and Northern blot is used to confirm the stability of RNA. In addition, the intergenerational test of transgenic plants is adopted to track and observe the stable genetic transmission of exogenous genes in offspring plants. If the stable expression of the target gene is observed in multiple generations, and there is no gene silencing or unstable phenomenon, it shows that the gene stability of the plant cell vector is good. The plant cell vector gene stability data is obtained by this process.

[0123] Preferably, step S22 includes the following steps:

[0124] Step S221: designing a cell vector gene modification strategy according to the plant trait improvement demand data to obtain cell vector gene modification strategy data;

[0125] Step S222: collecting transformation strategy genes from the cell vector gene transformation strategy data to obtain vector transformation strategy multiple gene data;

[0126] Step S223: evaluating the vector tolerance of the plant cell vector data according to the vector transformation strategy multiple gene data to obtain vector gene tolerance data;

[0127] Step S224: performing gene aggregation according to the multiple gene data of the vector transformation strategy and the vector gene bearing capacity data, thereby obtaining multiple gene aggregation data;

[0128] Step S225: Perform multiple gene improvements on the plant cell vector data according to the multiple gene aggregation data to obtain cell vector multiple gene improvement data.

[0129] In an embodiment of the present invention, according to the plant trait improvement demand data, such as the disease resistance, drought tolerance or high yield of the target plant, the gene modification strategy of the cell vector is designed. This process is carried out by analyzing the genomic information of existing plant varieties and their relationship with trait improvement. Using a genomics data platform, such as the NCBI gene bank or the plant genome database, candidate genes related to the target trait are screened out. Then, based on the association between the target trait and the gene, the functional genes involved in the key trait are determined by a gene network analysis method (such as Gene Ontology analysis, KEGG pathway analysis), and then a modification strategy is designed, and multiple genes are selected for expression regulation to ensure that the gene modification scheme can effectively improve the plant's trait. Through the above analysis, cell vector gene modification strategy data is generated, including candidate genes and their expression regulation methods. Based on the cell vector gene modification strategy designed in step S221, the modification strategy gene collection is carried out. This process collects gene information related to the target trait, such as gene sequence, functional description and expression in plants, by consulting literature and public databases. Gene synthesis technology is used to synthesize specific fragments of the target gene to ensure that it has efficient expression ability. Then, using molecular cloning technology, these gene fragments are inserted into appropriate plant transformation vectors to form genetic modification vectors. These multiple genes are introduced into plant cells through transformation methods such as Agrobacterium-mediated method or gene gun method. In order to optimize expression, it is also necessary to design and introduce gene regulatory elements (such as promoters, terminators, etc.) into the vector. After completing the collection and construction of all gene fragments, the multiple gene data of the vector transformation strategy are obtained, and the constructed vector is transformed into plant cells (such as by Agrobacterium transformation method or gene gun transformation method) using plant cell culture technology. The transformed cells will be cultured under different culture conditions, and the tolerance of the vector will be evaluated by detecting the cell growth state, gene expression and gene integration efficiency. PCR analysis is used to confirm whether the exogenous gene is successfully integrated into the plant genome, Southern blot is used to detect the stability of gene integration in the genome, and RT-qPCR technology is used to detect the expression level of the exogenous gene. If the plant cells can still grow normally under high concentration screening agents, and the exogenous genes show stable expression, it means that the tolerance of the vector is strong. Through this series of evaluations, the carrier gene tolerance data is obtained, and after completing the carrier tolerance evaluation, gene polymerization is performed to optimize the multiple gene combination. The goal of this step is to ensure that multiple genes can be expressed in a coordinated manner and can play a synergistic role in plant cells. According to the vector gene tolerance data, the most appropriate gene combination is selected to avoid selecting genes that burden plant cells. Through gene aggregation methods, such as site-specific transformation technology or genome editing technology (such as CRISPR / Cas9), multiple genes are inserted into plant cell vectors in a suitable order and position. During the aggregation process, the interaction between genes is considered to ensure that there is no antagonism between different genes.In this process, bioinformatics tools (such as gene interaction network analysis) can be used to predict gene interactions and select gene combinations with synergistic effects. After the polymerization is completed, genetic analysis techniques (such as Southern blot) are used to confirm the insertion position and integrity of the polymerized genes to ensure that the vector after gene polymerization can stably express the required multiple genes and obtain multiple gene polymerization data. After obtaining the multiple gene polymerization data, the plant cell vector is improved with multiple genes. This step is to transform the polymerized gene vector into plant cells again for genetic modification. In this process, Agrobacterium-mediated transformation or gene gun method is used to introduce vectors with multiple genes into plant cells. The transformed cells are screened and cultured, and cells with successful gene integration and stable expression are selected. Next, qPCR and Western blot and other technologies are used to detect the expression levels of multiple genes to ensure that all target genes can be correctly expressed and function in cells. At the same time, genome analysis is performed to confirm whether the multiple genes are stably integrated into the plant genome and do not cause gene silencing or adverse mutations. By evaluating the growth status, phenotypic characteristics, etc. of plant cells, it is detected whether the trait improvement has been successfully achieved, and based on the experimental results, the multiple gene improvement data of the cell vector is obtained.

[0130] Preferably, step S23 includes the following steps:

[0131] Step S231: performing gene interaction analysis based on the cell vector multiple gene improvement data to obtain multiple gene interaction data;

[0132] Step S232: evaluating the gene synergistic effect according to the multiple gene interaction data to obtain multiple gene synergistic effect data;

[0133] Step S233: predicting gene function complementarity enhancement on the multiple gene synergistic effect data to obtain gene function complementarity enhancement data;

[0134] Step S234: predicting gene co-expression enhancement according to the multiple gene synergistic effect data and the gene function complementarity enhancement data to obtain cell vector gene co-expression enhancement data;

[0135] Step S235: performing gene expression coordination detection according to the gene function complementarity enhancement data and the cell vector gene co-expression enhancement data to obtain gene expression coordination data.

[0136] In an embodiment of the present invention, a gene interaction network is constructed by bioinformatics tools (such as STRING database, GeneMANIA, Cytoscape, etc.), and the cooperative relationship between genes is predicted based on the interaction of genes. The key genes related to the target trait are selected as the analysis object, and the interaction network of these genes is determined by literature data and experimental results. Gene interaction analysis combines transcriptome data to calculate the interaction intensity between each gene, identify gene pairs with similar functions or regulatory pathway interactions, and further verify them. In this process, gene co-expression analysis is used to identify genes with similar expression patterns under the same biological conditions, and the obtained multiple gene interaction data are used. Multiple gene interaction data are used to verify whether there is a positive or negative synergistic effect between genes through gene expression data (such as RNA-seq) and biological experiments (such as gene knockout, overexpression, etc.). Gene expression profile analysis can be used to compare the expression levels of multiple genes in plant cells after transformation, and analyze the effects of different gene combinations. Through bioinformatics analysis, regression analysis, gene ontology analysis (GO) and pathway enrichment analysis (KEGG) are used to evaluate whether multiple gene combinations can enhance target traits. Experimentally, the transformed plant cells were cultured to observe their growth phenotype, stress resistance and other characteristics, and the expression of each gene was quantitatively measured by qPCR, Western blot and other means. Based on these experimental data, it is evaluated whether there is a synergistic effect between the genes, and multiple gene synergistic effect data are generated. Based on the gene synergistic effect data, by comparing the expression levels of different genes, it is identified which genes can be complementary to each other to enhance the target traits. For example, some genes are weakly expressed under certain conditions, but can significantly improve specific traits when expressed together with other genes. By combining functional annotation data (such as GO annotation, Pathway enrichment analysis, etc.), the complementary effect between genes is evaluated to predict which genes have a complementary enhancement effect on the target traits. In the experiment, the model after plant cell transformation was used to observe the effects of each gene expressed alone and in combination, and the complementarity of gene function was verified by phenotypic analysis (such as plant growth, stress resistance, yield, etc.). On this basis, the gene expression data is used to analyze the synergistic effect of genes and predict which gene combinations can more effectively improve the target traits. Obtain data on complementary enhancement of gene functions, and use gene expression profile data (such as RNA-seq) to analyze the co-expression patterns of genes in the transformed cells. Gene co-expression network analysis (such as WGCNA) is used to identify whether there is a consistent expression trend between genes, and pathway enrichment analysis and GO analysis are used to determine whether co-expressed genes are concentrated in certain key biological processes. Then, based on the gene co-expression pattern, predict the impact of multiple genes on the target trait when expressed simultaneously, especially to improve the target trait through synergy between genes.By statistical methods (such as cluster analysis, principal component analysis, etc.), the consistency and synergy of gene expression are evaluated, and the selection of co-expressed genes is further optimized. Finally, based on the predicted results, the gene combination that can achieve the best co-expression is selected to form the cell carrier gene co-expression enhancement data. Based on the gene function complementarity enhancement data and the gene co-expression enhancement data, the transformed plant cells are selected as experimental objects to perform quantitative detection of gene expression levels. By qPCR, RNA-seq and other technologies, the expression changes of the target gene under different conditions are determined, especially in the case of co-expression, whether the gene expression shows a coordinated trend. Combined with the gene expression spectrum, the expression data of multiple genes are analyzed using statistical methods (such as Pearson correlation coefficient, Spearman correlation coefficient, etc.) to evaluate whether their expression levels are highly correlated. Further, Western blot or immunofluorescence staining and other methods are used to detect the protein level of the target gene to ensure its stable expression in the cell. Through the above experiments, gene expression coordination data is obtained.

[0137] Preferably, step S24 includes the following steps:

[0138] Step S241: evaluating the gene antagonistic effect based on the cell vector multiple gene improvement data to obtain multiple gene antagonistic effect data;

[0139] Step S242: performing gene expression competition growth evaluation on the multiple gene antagonism effect data to obtain gene expression competition growth data;

[0140] Step S243: performing gene expression inhibition detection according to the gene expression competition growth data to obtain cell vector gene expression inhibition data;

[0141] Step S244: performing gene expression reduction calculation on the cell vector gene expression inhibition data to obtain gene expression reduction data;

[0142] Step S245: predicting the collapse of the gene regulatory network according to the gene expression competition growth data to obtain the gene regulatory network collapse data;

[0143] Step S246: Gene silencing prediction is performed based on the gene regulatory network collapse data and the gene expression decrease data to obtain cell vector gene silencing data.

[0144] In an embodiment of the present invention, based on the cell vector multiple gene improvement data, bioinformatics tools (such as GeneMANIA, STRING database, etc.) are used to analyze the interaction between multiple improved genes, with particular attention to gene pairs that cause antagonistic effects. Then, experimentally, by constructing a gene co-expression network, transcriptome data (such as RNA-seq) are used to evaluate the changes in gene expression under different treatment conditions. By calculating the expression correlation (such as negative correlation) between genes and comparing the changes in gene expression, the gene pairs that cause gene antagonistic effects are identified. These antagonistic effects are manifested as a decrease in the expression level of certain genes after other gene modifications, or the stability of expression is affected. The evaluation results of the gene antagonistic effect will obtain multiple gene antagonistic effect data, and by constructing a multiple gene expression model, the expression of each gene in the cell is analyzed to identify the competitive relationship between expressions. Using high-throughput RNA-seq technology, gene expression data of cells after transformation and after treatment with different gene combinations are collected. Using expression data, a competitive growth model is used to evaluate whether the expression level of each gene is inhibited or enhanced by other genes under certain conditions. The evaluation of competitive growth effect is mainly through analyzing the correlation between gene expression (for example, using linear regression to analyze the expression relationship between genes) and monitoring the dynamic changes of gene expression under specific conditions. In this way, gene expression competitive growth data is obtained, and gene expression inhibition detection aims to verify whether there is expression inhibition caused by the interaction between genes by analyzing the gene expression competitive growth data. Specifically, through high-throughput technologies such as RNA-seq, the expression data of target genes and genes in the transformed cells are obtained, and the expression levels under different gene modification conditions are compared. In the experiment, after the cell vector is transformed, the expression level of a specific gene is quantitatively detected by methods such as qPCR and Western blot to observe whether it shows an inhibitory decrease under different conditions. By calculating the expression changes of each gene, statistical analysis (such as paired t-test, ANOVA, etc.) is used to determine whether there is significant inhibition of gene expression under certain gene modification conditions. Based on these experimental data, cell vector gene expression inhibition data is formed, and RNA-seq technology is used to collect the expression data of each gene in the transformed cell vector. In the experiment, the relative expression of the target gene is measured and compared with the unmodified cell control group. In the comparison results, the expression changes of the target genes after genetic modification are analyzed, especially the genes with decreased expression. By using statistical methods (such as differential analysis, fold change calculation, etc.), the expression decrease ratio of each gene is calculated, and the effect of different genetic modification conditions on the expression is further evaluated. Through this calculation, the gene expression decrease data is obtained, and the gene regulatory network is constructed through bioinformatics methods to analyze the regulatory role of each gene and its relationship with genes.Gene expression competition growth data were used to check whether the gene regulatory network was disordered in the case of multiple gene modification, especially whether there was disorder in the expression competitive genes. Regulatory network analysis tools (such as Cytoscape, Gephi, etc.) were used to analyze the regulatory relationship between genes and evaluate whether negative feedback, activation or inhibition effects between genes occurred after the modification, leading to the "collapse" of the network. Through the prediction of regulatory network collapse, it is possible to identify which genes are inconsistent or collapsed in multiple gene modifications. Gene silencing prediction is based on gene regulatory network collapse data and gene expression decrease data to evaluate whether some genes are silenced during multiple gene modification. By analyzing the gene regulatory network collapse data, it is possible to identify whether the regulatory effect of key genes is broken or disappears, resulting in a significant decrease in their expression. Combined with the gene expression decrease data, it is further verified whether these genes are in a silent state. Through experimental methods such as qPCR and Northern blot, it is verified whether the transcription level of any gene is significantly lower than the normal level to confirm whether it has entered a silent state. Through gene silencing prediction, it is possible to identify which genes have been silenced during multiple genetic modifications, whether this has affected the improvement of target traits, and provide data support for subsequent gene editing optimization to form cell vector gene silencing data.

[0145] Preferably, step S3 comprises the following steps:

[0146] Step S31: performing cell vector inoculation on sample plant part data according to plant cell vector gene stability data to obtain plant cell vector inoculation data;

[0147] Step S32: Performing environmental adaptability testing on the plant cell carrier inoculation data to obtain cell carrier environmental adaptability data;

[0148] Step S33: Calculating the cell carrier survival rate according to the cell carrier environmental adaptability data to obtain the cell carrier survival rate data;

[0149] Step S34: evaluating the stability of the cell vector according to the cell vector survival rate data and the cell vector environmental adaptability data to obtain the plant cell vector stability data.

[0150] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0151] Step S31: performing cell vector inoculation on sample plant part data according to plant cell vector gene stability data to obtain plant cell vector inoculation data;

[0152] In an embodiment of the present invention, based on the gene stability data of the plant cell vector, those cell vectors that show excellent gene stability are selected as inoculation objects. The selected cell vector will be accurately implanted into a specific part of the plant, such as a bud, root or leaf. During inoculation, standard plant tissue culture techniques are used, such as micromanipulation or laser-assisted microinjection technology under a sterile environment to directly access the cell vector to the plant tissue. Ensure that the cell vector can smoothly fuse in the plant tissue and undergo necessary development. After the inoculation is completed, the growth of the inoculated cells is recorded and saved to generate plant cell vector inoculation data.

[0153] Step S32: Performing environmental adaptability testing on the plant cell carrier inoculation data to obtain cell carrier environmental adaptability data;

[0154] In the embodiment of the present invention, the environmental adaptability test is to evaluate the adaptability of the inoculated plant cell carrier under specific external environmental conditions, select appropriate growth conditions, including temperature, humidity, light intensity, etc., and place the inoculated plant cell carrier under these environmental conditions. The inoculated plant tissue will be exposed to different environmental stress factors, such as temperature fluctuations, changes in light cycle or different levels of water supply. In this process, the growth state of the plant cell carrier is regularly observed, including cell division, expansion, and necrosis or wilting. By recording the growth state of the plant cell carrier in detail and combining plant phenotypic analysis tools such as image acquisition and processing software, the environmental adaptability is quantitatively evaluated, and the environmental adaptability data of the cell carrier is obtained based on the observed data.

[0155] Step S33: Calculating the cell carrier survival rate according to the cell carrier environmental adaptability data to obtain cell carrier survival rate data;

[0156] In the embodiment of the present invention, the growth conditions of the plant cell carrier after inoculation under different environments are recorded, with particular attention paid to the vitality, division ability and phenotypic characteristics of the cells. The proportion of surviving cells is determined by cell counting (such as cell counting under a microscope) and phenotypic scoring (such as color changes of plant leaves, extension range of cells, etc.). At the same time, combined with plant physiological detection, such as chlorophyll content, resistance-related enzyme activity, etc., it is further confirmed whether the cell carrier is affected by environmental pressure. The survival rate data of the cell carrier is obtained by calculating the ratio of the number of surviving cells to the total number of inoculated cells.

[0157] Step S34: evaluating the stability of the cell vector according to the cell vector survival rate data and the cell vector environmental adaptability data to obtain the plant cell vector stability data.

[0158] In an embodiment of the present invention, plant growth monitoring equipment (such as environmental sensors, image analyzers, etc.) is used to observe the inoculated plants for a long time, and the survival status of the cell carrier and its adaptability under different environmental conditions are recorded. In this process, a biostatistical method is used to analyze the long-term performance of the cell carrier in the plant in combination with environmental adaptability data and survival rate data. Through statistical analysis, the changes in the survival rate of the cell carrier under different environmental conditions are compared to identify the decline phenomenon or adaptation failure that occurs in an unstable environment. The stability assessment includes not only the growth of the cells, but also the persistence of its gene expression, the stability of plant traits, etc., and the stability data of the plant cell carrier will be obtained.

[0159] Preferably, step S4 comprises the following steps:

[0160] Step S41: performing cell vector gene transduction efficiency detection according to the plant cell vector stability data to obtain cell vector gene transduction efficiency data;

[0161] Step S42: evaluating the exogenous gene expression of the cell vector gene transduction efficiency data to obtain the cell vector exogenous gene expression data;

[0162] Step S43: performing gene improvement strategy reliability analysis on the cell vector foreign gene expression data and the cell vector gene transduction efficiency data to obtain vector gene improvement strategy reliability data;

[0163] Step S44: improving and optimizing the cell vector multiple gene improvement data according to the vector gene improvement strategy reliability data to obtain the vector multiple gene improvement optimization data.

[0164] In the embodiment of the present invention, according to the stability data of the plant cell vector obtained in the early stage, the cell vector that has been confirmed to be stable is selected to carry out the gene transduction experiment. In the cell culture process, the transduction method of the application standard, such as agrobacterium-mediated transduction or gene gun transduction, etc., is used to introduce the foreign gene into the plant cell vector. To ensure the accurate evaluation of the transduction efficiency, a reporter gene (such as GUS or GFP gene) can be selected, and the expression of the reporter gene can be measured by fluorescence microscopy or chemiluminescence. The expression quantity of the reporter gene in the plant cell after the transduction is analyzed by statistics, and the quantity of the original cell vector is combined to calculate the transduction efficiency. This process can use flow cytometer or fluorescence imaging technology to detect the change of transduction efficiency in real time, obtain the cell vector gene transduction efficiency data, and select the plant cell vector that is successfully transduced according to the cell vector gene transduction efficiency data obtained in step S41, and further analyze the expression level of the foreign gene in the cell. Adopt real-time quantitative PCR (qPCR) technology or Western blotting method, detect the transcription and translation level of the foreign gene. qPCR is used to quantitatively analyze the expression of exogenous gene mRNA, and select appropriate internal reference genes (such as Actin or GAPDH) for standardization to ensure the accuracy of the experimental results. At the same time, Western blotting can be used to detect the expression of exogenous gene proteins and observe the expression and distribution of proteins. If the exogenous gene expression is normal and stable, the data should show high expression levels of target gene mRNA and protein. Through these analysis methods, the cell vector exogenous gene expression data is obtained, the relationship between the transduction efficiency data and the exogenous gene expression data is analyzed, and the correlation between the success rate of gene transduction and the exogenous gene expression level is evaluated. Statistical analysis methods, such as regression analysis or variance analysis, are used to analyze the stability and consistency of exogenous gene expression under different transduction efficiencies. If the transduction efficiency is high and the exogenous gene expression is stable, it means that the gene improvement strategy has a high reliability. On this basis, the adaptability of the gene improvement strategy in different plant parts and under different environmental conditions is further evaluated to determine whether it can stably express exogenous genes for a long time. Combined with these data, the reliability data of the vector gene improvement strategy is obtained. Combined with the reliability data obtained in step S43, the effect of the current multiple gene improvement is analyzed to identify potential gene antagonism or unstable expression. Statistical and bioinformatics methods (such as genome data mining, gene interaction network analysis, etc.) are used to identify those gene combinations that have a negative impact on the improvement effect and adjust them. For example, if certain gene combinations show low expression stability or poor transduction efficiency, more suitable alternative genes can be selected or the construction method of the optimized vector can be used to improve the transduction efficiency and gene expression stability. At the same time, the cell vector multiple gene improvement strategy is redesigned and adjusted according to the optimization results to ensure that the multiple gene improvement scheme has good performance in multiple plant varieties and growth environments.After optimization, the data obtained are the vector multiple gene improvement optimization data.

[0165] The present invention also provides a cell carrier information processing system for executing the cell carrier information processing method as described above, the cell carrier information processing system comprising:

[0166] Plant cell carrier culture module: obtain sample plant part data; select culture medium according to the sample plant part data to obtain plant tissue culture medium data; culture plant cell carrier according to the sample plant part data and plant tissue culture medium data to obtain plant cell carrier data;

[0167] Gene stability detection module: obtain plant trait improvement demand data; perform multiple gene improvements on plant cell vector data according to the plant trait improvement demand data to obtain cell vector multiple gene improvement data; perform plant cell vector gene stability detection according to the cell vector multiple gene improvement data to obtain plant cell vector gene stability data;

[0168] Cell vector stability assessment module: perform cell vector inoculation on sample plant part data according to plant cell vector gene stability data to obtain cell vector inoculation data; perform environmental adaptability test on plant cell vector inoculation data to obtain cell vector environmental adaptability data; perform cell vector stability assessment according to cell vector environmental adaptability data to obtain plant cell vector stability data;

[0169] Gene improvement and optimization module: perform cell vector gene transduction efficiency detection based on plant cell vector stability data to obtain cell vector gene transduction efficiency data; perform gene improvement strategy reliability analysis on cell vector gene transduction efficiency data to obtain vector gene improvement strategy reliability data; perform improvement and optimization on cell vector multiple gene improvement data based on vector gene improvement strategy reliability data to obtain vector multiple gene improvement optimization data.

[0170] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A cell carrier information processing method, characterized in that: The following steps are involved: Step S1: obtaining sample plant part data; selecting a culture medium according to the sample plant part data to obtain plant tissue culture medium data; culturing a plant cell carrier according to the sample plant part data and the plant tissue culture medium data to obtain plant cell carrier data; Step S2: obtaining plant trait improvement demand data; performing multiple gene improvements on plant cell vector data according to the plant trait improvement demand data to obtain cell vector multiple gene improvement data; performing plant cell vector gene stability detection according to the cell vector multiple gene improvement data to obtain plant cell vector gene stability data; Step S3: performing cell vector inoculation on the sample plant part data according to the plant cell vector gene stability data to obtain cell vector inoculation data; performing environmental adaptability detection on the plant cell vector inoculation data to obtain cell vector environmental adaptability data; performing cell vector stability assessment according to the cell vector environmental adaptability data to obtain plant cell vector stability data; Step S4: performing cell vector gene transduction efficiency detection according to the plant cell vector stability data to obtain cell vector gene transduction efficiency data; performing gene improvement strategy reliability analysis on the cell vector gene transduction efficiency data to obtain vector gene improvement strategy reliability data; According to the reliability data of the vector gene improvement strategy, the cell vector multiple gene improvement data is improved and optimized to obtain the vector multiple gene improvement optimization data.

2. The cell carrier information processing method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining sample plant part data; Step S12: selecting plant tissue culture medium according to the sample plant part data, thereby obtaining plant tissue culture medium data; Step S13: performing plant tissue structure culture on the sample plant part data according to the plant tissue culture medium data to obtain plant tissue structure data; Step S14: performing plant cell carrier culture according to the plant tissue structure data and the plant tissue culture medium data, thereby obtaining plant cell carrier data.

3. The cell carrier information processing method according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: extracting plant tissue structure cells according to the plant tissue structure data, thereby obtaining plant tissue structure cell data; Step S142: performing cell division activity detection on the plant tissue structure cell data, thereby obtaining plant cell division activity data; Step S143: Calculating the cell division rate of the plant cell division activity data to obtain cell division rate data; Step S144: performing high transformation efficiency detection according to the plant tissue structure cell data to obtain plant tissue structure high transformation efficiency data; Step S145: selecting cell carrier tissues from the plant tissue structure cell data according to the plant tissue structure high transformation efficiency data and the cell division rate data to obtain plant cell carrier tissue data; Step S146: performing plant cell carrier culture on the plant cell carrier tissue data according to the plant tissue culture medium data, thereby obtaining plant cell carrier data.

4. The cell carrier information processing method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Acquiring plant trait improvement demand data; Step S22: performing multiple gene improvement on the plant cell vector data according to the plant trait improvement requirement data to obtain cell vector multiple gene improvement data; Step S23: performing gene expression coordination detection according to the cell vector multiple gene improvement data to obtain gene expression coordination data; Step S24: performing gene silencing prediction based on the cell vector multiple gene improvement data to obtain cell vector gene silencing data; Step S25: performing plant cell vector gene stability detection according to the gene expression coordination data and the cell vector gene silencing data to obtain plant cell vector gene stability data.

5. The cell carrier information processing method according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: designing a cell vector gene modification strategy according to the plant trait improvement demand data to obtain cell vector gene modification strategy data; Step S222: collecting transformation strategy genes from the cell vector gene transformation strategy data to obtain vector transformation strategy multiple gene data; Step S223: evaluating the vector tolerance of the plant cell vector data according to the vector transformation strategy multiple gene data to obtain vector gene tolerance data; Step S224: performing gene aggregation according to the multiple gene data of the vector transformation strategy and the vector gene bearing capacity data, thereby obtaining multiple gene aggregation data; Step S225: Perform multiple gene improvements on the plant cell vector data according to the multiple gene aggregation data to obtain cell vector multiple gene improvement data.

6. The cell carrier information processing method according to claim 4, characterized in that: Step S23 includes the following steps: Step S231: performing gene interaction analysis based on the cell vector multiple gene improvement data to obtain multiple gene interaction data; Step S232: evaluating the gene synergistic effect according to the multiple gene interaction data to obtain multiple gene synergistic effect data; Step S233: predicting gene function complementarity enhancement on the multiple gene synergistic effect data to obtain gene function complementarity enhancement data; Step S234: predicting gene co-expression enhancement according to the multiple gene synergistic effect data and the gene function complementarity enhancement data to obtain cell vector gene co-expression enhancement data; Step S235: performing gene expression coordination detection according to the gene function complementarity enhancement data and the cell vector gene co-expression enhancement data to obtain gene expression coordination data.

7. The cell carrier information processing method according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: evaluating the gene antagonistic effect based on the cell vector multiple gene improvement data to obtain multiple gene antagonistic effect data; Step S242: performing gene expression competition growth evaluation on the multiple gene antagonism effect data to obtain gene expression competition growth data; Step S243: performing gene expression inhibition detection according to the gene expression competition growth data to obtain cell vector gene expression inhibition data; Step S244: performing gene expression reduction calculation on the cell vector gene expression inhibition data to obtain gene expression reduction data; Step S245: predicting the collapse of the gene regulatory network according to the gene expression competition growth data to obtain the gene regulatory network collapse data; Step S246: Gene silencing prediction is performed based on the gene regulatory network collapse data and the gene expression decrease data to obtain cell vector gene silencing data.

8. The cell carrier information processing method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing cell vector inoculation on sample plant part data according to plant cell vector gene stability data to obtain plant cell vector inoculation data; Step S32: Performing environmental adaptability testing on the plant cell carrier inoculation data to obtain cell carrier environmental adaptability data; Step S33: Calculating the cell carrier survival rate according to the cell carrier environmental adaptability data to obtain the cell carrier survival rate data; Step S34: evaluating the stability of the cell vector according to the cell vector survival rate data and the cell vector environmental adaptability data to obtain the plant cell vector stability data.

9. The cell carrier information processing method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing cell vector gene transduction efficiency detection according to the plant cell vector stability data to obtain cell vector gene transduction efficiency data; Step S42: evaluating the exogenous gene expression of the cell vector gene transduction efficiency data to obtain the cell vector exogenous gene expression data; Step S43: performing gene improvement strategy reliability analysis on the cell vector foreign gene expression data and the cell vector gene transduction efficiency data to obtain vector gene improvement strategy reliability data; Step S44: improving and optimizing the cell vector multiple gene improvement data according to the vector gene improvement strategy reliability data to obtain the vector multiple gene improvement optimization data.

10. A cell carrier information processing system, characterized in that: Used to execute the cell carrier information processing method according to claim 1, the cell carrier information processing system comprises: Plant cell carrier culture module: obtain sample plant part data; select culture medium according to the sample plant part data to obtain plant tissue culture medium data; culture plant cell carrier according to the sample plant part data and plant tissue culture medium data to obtain plant cell carrier data; Gene stability detection module: obtain plant trait improvement demand data; perform multiple gene improvements on plant cell vector data according to the plant trait improvement demand data to obtain cell vector multiple gene improvement data; perform plant cell vector gene stability detection according to the cell vector multiple gene improvement data to obtain plant cell vector gene stability data; Cell vector stability assessment module: perform cell vector inoculation on sample plant part data according to plant cell vector gene stability data to obtain cell vector inoculation data; perform environmental adaptability test on plant cell vector inoculation data to obtain cell vector environmental adaptability data; perform cell vector stability assessment according to cell vector environmental adaptability data to obtain plant cell vector stability data; Gene improvement and optimization module: perform cell vector gene transduction efficiency detection based on plant cell vector stability data to obtain cell vector gene transduction efficiency data; perform gene improvement strategy reliability analysis on cell vector gene transduction efficiency data to obtain vector gene improvement strategy reliability data; perform improvement and optimization on cell vector multiple gene improvement data based on vector gene improvement strategy reliability data to obtain vector multiple gene improvement optimization data.

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