Construction method and device of digital cell model, equipment and storage medium

Abstract

The invention provides a construction method and device of a digital cell model, equipment and a storage medium, and relates to the technical field of biological artificial intelligence. The construction method of the digital cell model comprises the following steps: carrying out cell language compiling through an intelligent processing module, and obtaining recompiled data; generating a digital cell model based on the recompiled data; finely adjusting the digital cell model; and obtaining a simulation result through the fine-tuned digital cell model, and verifying the simulation result. According to the embodiment of the invention, the real cell dynamic state can be preliminarily simulated, and the dynamic change and interaction of molecules in cells can be accurately reflected.

Description

Technical Field

[0001] The present application relates to the field of biological artificial intelligence technology, and specifically, to a method, device, equipment and storage medium for constructing a digital cell model. Background Art

[0002] With the application of multi-omics technology in cell biology, biological data has exploded, driving the development of biological artificial intelligence systems. At present, with the improvement of computing power, artificial intelligence models can use huge data sets and parameters to demonstrate high accuracy in predicting biomolecular structures and interactions, and can even simulate the early evolution of macromolecules, providing convincing evidence for the realization of integrated digital cells based on extensive molecular quantitative data and interaction networks.

[0003] The inventors of this application have found that the current artificial intelligence model technology solution mainly has the following defects:

[0004] 1) Data processing bottleneck: Current methods mainly rely on manual curation of multimodal data or simple processing of single data types. With the advancement of observation technology, the amount and complexity of data have increased exponentially, making manual processing infeasible.

[0005] 2) Insufficient data integration: Existing databases are usually simple aggregations of a single data type, lacking organic connections between different data types, making it difficult to explore deep relationships between data.

[0006] 3) Model limitations: Current cell models are mainly based on simple cell systems (such as JCVI-syn3A and mycoplasma), or focus only on a single data layer (such as gene expression). These models cannot simulate the real state of more complex cells and lack multi-level integrated networks.

[0007] 4) Mechanistic simulation: Current methods lack deep integration of artificial intelligence and cannot capture the true dynamics of complex cellular systems.

[0008] 5) Insufficient explainability: Existing AI-assisted prediction models often find it difficult to provide biologically plausible explanations for their results, and biological evidence at the algorithmic level is scarce. Summary of the invention

[0009] According to one aspect of the present application, a method for constructing a digital cell model is provided, comprising: compiling a cell language through an intelligent processing module and obtaining recompiled data; generating a digital cell model based on the recompiled data; fine-tuning the digital cell model; obtaining simulation results through the fine-tuned digital cell model, and verifying the simulation results.

[0010] According to some embodiments, before the cellular language is compiled through the intelligent processing module and the recompiled data is obtained, it also includes: obtaining multiple preset intelligent models; fine-tuning the multiple preset intelligent models through preset multimodal biological data; and constructing an intelligent processing module based on the multiple fine-tuned preset intelligent models.

[0011] According to some embodiments, cellular language compilation is performed through an intelligent processing module to obtain recompiled data, including: identifying multiple data types and biological information contained in multimodal biological data through an intelligent processing module; based on the biological information, performing cellular language compilation on the multimodal biological data through an intelligent processing module to obtain recompiled data; based on multiple data types, performing dimensionality reduction processing on the recompiled data through an intelligent processing module; and constructing connections between different types of data in the recompiled data.

[0012] According to some embodiments, based on biological information, multimodal biological data are compiled into a cellular language through an intelligent processing module to obtain recompiled data, including: compiling the multimodal biological data into a cellular language through an intelligent processing module to obtain a unified token of the multimodal biological data; and constructing a mapping relationship between the unified token and the tokens corresponding to the data of different data types in the multimodal biological data.

[0013] According to some embodiments, a digital cell model is generated based on the recompiled data, including: acquiring cell component structure information and spatiotemporal information in multimodal biological data; based on the recompiled data that has been subjected to dimensionality reduction processing, combining the cell component structure information and spatiotemporal information, constructing a digital cell model through a preset modeling method.

[0014] According to some embodiments, the digital cell model is fine-tuned, including: obtaining biological information and cell characteristics of preset cells; decoding the digital cell model based on the mapping relationship and the biological information of the preset cells; and fine-tuning the decoded digital cell model according to the cell characteristics so that the digital cell model can simulate the preset cells.

[0015] According to some embodiments, simulation results are obtained through a fine-tuned digital cell model and the simulation results are verified, including: inputting recompiled data corresponding to preset signal mediating factors and preset cells into the digital cell model to obtain simulation results; comparing and verifying the simulation results with experimental data, and generating verification results; and feeding back the verification results to the intelligent processing module.

[0016] According to one aspect of the present application, a device for constructing a digital cell model is provided, comprising: a data compilation module, used to compile a cell language through an intelligent processing module and obtain recompiled data; a model construction module, used to generate a digital cell model based on the recompiled data; a model training module, used to fine-tune the digital cell model; and a simulation verification module, used to obtain simulation results through the fine-tuned digital cell model and verify the simulation results.

[0017] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method.

[0018] According to one aspect of the present application, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the aforementioned method is implemented.

[0019] According to the embodiments of the present application, automated data integration and standardization can be achieved, thereby improving data processing efficiency; achieving effective connections between data layers; integrating a variety of biological data types through the constructed digital cell model, so that the digital cell model shows higher accuracy and stability in predicting cell behaviors and reactions, and helps to reveal complex cell dynamics and their biological mechanisms; the constructed digital cell model is easy to expand and maintain, can adapt to future technological developments and changes in data types, and has long-term applicability and performance stability.

[0020] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application.

[0022] Figure 1 A flow chart showing a method for constructing an intelligent processing module according to an exemplary embodiment of the present application.

[0023] Figure 2 A flow chart showing a method for constructing a digital cell model according to an exemplary embodiment of the present application.

[0024] Figure 3 A block diagram showing a device for constructing a digital cell model according to an exemplary embodiment of the present application.

[0025] Figure 4 A block diagram of an electronic device according to an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0026] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0027] The described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application may be put into practice without one or more of these specific details, or other modes, components, materials, devices or operations may be adopted. In these cases, known structures, methods, devices, realizations, materials or operations will not be shown or described in detail.

[0028] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0029] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0030] The present application provides a method, device, equipment and storage medium for constructing a digital cell model, which can construct a digital cell model integrating multiple biological data types, can preliminarily simulate real cell dynamics, and more accurately reflect the dynamic changes of intracellular molecules and their interactions, thereby promoting the prediction and understanding of biological mechanisms.

[0031] A method, device, equipment and storage medium for constructing a digital cell model according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0032] Figure 1 A flow chart showing a method for constructing an intelligent processing module according to an exemplary embodiment of the present application.

[0033] like Figure 1 As shown, in step S100, the construction device obtains multiple preset intelligent models.

[0034] For example, in step S100, the construction device obtains multiple preset intelligent models according to the application scenario.

[0035] The construction device obtains multiple preset intelligent models that match the application scenario to build an intelligent processing module and configures computing resources (such as GPU) for it.

[0036] According to some embodiments, the preset intelligent model includes an artificial intelligence large model based on a general artificial intelligence framework (such as GPT, Llama, etc.).

[0037] In step S110, the construction device fine-tunes multiple preset intelligent models using preset multimodal biological data.

[0038] For example, in step S110, the construction device obtains preset multimodal biological data and uses it to fine-tune multiple preset intelligent models.

[0039] A device is constructed to obtain preset multimodal biological data.

[0040] According to some embodiments, multimodal biological data includes multiple types of biological data, including multiple sets of sequencing data (such as genome, epigenome, transcriptome, proteome and metabolome, etc.), biological macromolecule structure data, spatiotemporal information and cell phenotype information.

[0041] According to some embodiments, the construction device further sets a data preprocessing code for the preset multimodal biological data to guide the intelligent processing module to perform data preprocessing on the multimodal biological data, wherein the data preprocessing includes data sorting and cleaning, data dimensionality reduction, etc.

[0042] Based on the multimodal biological data, the construction device fine-tunes and trains multiple preset intelligent models through a preset multimodal data processing pipeline to learn how to recognize and process the multimodal biological data.

[0043] In step S120, the construction device constructs an intelligent processing module based on the fine-tuned multiple preset intelligent models.

[0044] For example, in step S120, after model fine-tuning training, the construction device constructs an intelligent processing module through multiple preset intelligent models that have been fine-tuned.

[0045] According to some embodiments, the construction device can identify and process multimodal biological data of different data types through an intelligent processing module, and reduce the dimension of all data to a unified dimension that can be directly related during the data processing process.

[0046] Figure 2 A flow chart showing a method for constructing a digital cell model according to an exemplary embodiment of the present application.

[0047] like Figure 2 As shown, in step S200, the construction device compiles the cellular language through the intelligent processing module and obtains recompiled data.

[0048] For example, in step S200, the construction device processes the multimodal biological data through the intelligent processing module to compile the multimodal biological data into a cellular language and generate recompiled data.

[0049] The construction device identifies multiple data types and biological information contained in the multimodal biological data through the constructed intelligent processing module.

[0050] According to some embodiments, the construction device may automatically design corresponding algorithms through an intelligent processing module to obtain multiple data types and biological information in multimodal biological data.

[0051] The construction device performs data preprocessing on the multimodal biological data through the intelligent processing module, and compiles the multimodal biological data into a cellular language according to the biological information contained in the multimodal biological data and the current application scenario to obtain recompiled data.

[0052] According to some embodiments, the construction device automatically compiles the multimodal biological data into a cellular language according to the current application scenario through an intelligent processing module based on the biological information contained in the multimodal biological data, and obtains a unified token of the multimodal biological data.

[0053] According to some embodiments, the construction device constructs a mapping relationship between a unified token of multimodal biological data and tokens corresponding to data of different data types in the multimodal biological data through an intelligent processing module.

[0054] According to some embodiments, the recompiled data includes a unified token of multimodal biological data and a mapping relationship between the unified token and tokens corresponding to data of different data types. The recompiled data retains biological information in the multimodal biological data and is easy to combine multiple modalities.

[0055] According to multiple data types in the multimodal biological data, the construction device performs dimensionality reduction processing on the recompiled data through an intelligent processing module, so that the biological data of multiple data types are unified into a dimension that can be correlated with each other.

[0056] According to some embodiments, the construction device forms an intelligent database based on the recompiled data that has undergone dimensionality reduction processing, and performs feature extraction, feature alignment, and feature fusion of the recompiled data through the intelligent database.

[0057] According to the biological information in the recompiled data, the construction device can obtain the connection between different types of data in the recompiled data in the intelligent database.

[0058] In step S210 , the construction device generates a digital cell model based on the recompiled data.

[0059] For example, in step S210 , the construction device constructs a digital cell model using a preset modeling method by recompiling the data.

[0060] The construction device first obtains the cell component structure information and spatiotemporal information contained in the multimodal biological data.

[0061] The construction device combines the recompiled data that has undergone dimensionality reduction processing with the cell component structure information and spatiotemporal information, and constructs a digital cell model using a preset modeling method.

[0062] According to some embodiments, the preset modeling method includes a deep learning algorithm (such as a deep neural network, a convolutional neural network, etc.) to adapt to the data structure of the recompiled data in the intelligent database.

[0063] In step S220 , the construction device fine-tunes the digital cell model.

[0064] For example, in step S220, the construction device fine-tunes the digital cell model by presetting cell characteristics.

[0065] A device is constructed to obtain biological information and cell characteristics of preset cells.

[0066] According to the mapping relationship between the unified token of multimodal biological data and the tokens corresponding to data of different data types in the multimodal biological data, a device is constructed to decode the digital cell model to realize the translation function between the biological information of the preset cell and the digital cell model.

[0067] The construction device fine-tunes the decoded digital cell model according to the cell characteristics of the preset cells, so that the digital cell model can simulate the preset cells.

[0068] For example, if the preset cell is an immune cell, the construction device obtains the cell characteristics of the immune cell, including cell surface proteins, intracellular signaling proteins, effector proteins and genes, epigenetic characteristics, signaling pathways, transcription factors and other information. The construction device provides the cell characteristics of the immune cell to the digital cell model for fine-tuning training. Among them, the more and more accurate the information provided (such as the values ​​of mass spectrometry flow sequencing of multiple cell surface receptors and their surface antigens, the expression of genes in cells that reflect the characteristics of immune cells, etc.), the more accurately the digital cell model can simulate immune cells.

[0069] In step S230 , the construction device obtains simulation results through the fine-tuned digital cell model and verifies the simulation results.

[0070] For example, in step S230, based on the recompiled data corresponding to the preset cells and the preset signal mediating factors, the construction device obtains the simulation results of the preset cells through the fine-tuned digital cell model, and verifies the simulation results.

[0071] The construction device inputs the recompiled data corresponding to the preset signal mediating factors and the preset cells into the digital cell model to simulate the preset cells and obtain the simulation results.

[0072] According to some embodiments, the signal mediators include cytokines, receptor ligands, etc., which are used to bind to cell receptors to generate a cascade amplification reaction inside the cell to produce a phenotypic change. The signal mediators can be selected according to the type of preset cells.

[0073] According to some embodiments, the simulation of preset cells includes real cell simulation, cell development evolution and molecular mechanism mining.

[0074] The device is constructed to compare and verify the simulation results generated by the digital cell model with the experimental data, and generate a verification result to determine the consistency of the simulation results with the experimental data.

[0075] According to some embodiments, the validation results include predictions of cell behavior and explanations of biological mechanisms.

[0076] According to some embodiments, the construction device feeds back the verification result to the intelligent processing module to perform iterative optimization of the intelligent processing module.

[0077] According to the embodiments of the present application, a variety of biological data types can be integrated through the constructed digital cell model, so that the digital cell model shows higher accuracy and stability in predicting cell behavior and response.

[0078] Figure 3 A block diagram showing a device for constructing a digital cell model according to an exemplary embodiment of the present application.

[0079] like Figure 3 As shown, the construction device 300 includes a data compilation module 310 , a model construction module 320 , a model training module 330 and a simulation verification module 340 .

[0080] The data compilation module 310 identifies multiple data types and biological information contained in the multimodal biological data through the constructed intelligent processing module.

[0081] The data compilation module 310 performs data preprocessing on the multimodal biological data through the intelligent processing module, and compiles the multimodal biological data into a cellular language according to the biological information contained in the multimodal biological data and the current application scenario to obtain recompiled data.

[0082] According to the multiple data types in the multimodal biological data, the data compilation module 310 performs dimensionality reduction processing on the recompiled data through the intelligent processing module, so that the biological data of multiple data types are unified into a dimension that can be correlated with each other.

[0083] According to the biological information in the recompiled data, the data compilation module 310 obtains the connection between different types of data in the recompiled data.

[0084] The model building module 320 obtains the cell component structure information and spatiotemporal information contained in the multimodal biological data.

[0085] The model building module 320 combines the recompiled data that has undergone dimensionality reduction processing with the cell component structure information and spatiotemporal information, and builds a digital cell model using a preset modeling method.

[0086] The model training module 330 obtains biological information and cell characteristics of preset cells.

[0087] According to the mapping relationship between the unified token of the multimodal biological data and the tokens corresponding to the data of different data types in the multimodal biological data, the model training module 330 decodes the digital cell model to realize the translation function between the biological information of the preset cell and the digital cell model.

[0088] The model training module 330 fine-tunes the decoded digital cell model according to the cell characteristics of the preset cells, so that the digital cell model can simulate the preset cells.

[0089] The simulation verification module 340 inputs the recompiled data corresponding to the preset signal mediating factors and the preset cells into the digital cell model to simulate the preset cells and obtain simulation results.

[0090] The simulation verification module 340 compares and verifies the simulation results generated by the digital cell model with the experimental data, and generates a verification result to determine the consistency between the simulation results and the experimental data.

[0091] Figure 4 A block diagram of an electronic device according to an exemplary embodiment of the present application is shown.

[0092] like Figure 4 As shown, the electronic device 600 is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0093] like Figure 4 As shown, the electronic device 600 is in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc. The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 executes the methods described in this specification according to various exemplary embodiments of the present application. For example, the processing unit 610 may execute the following: Figure 2 The method shown in .

[0094] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0095] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0096] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0097] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0098] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by combining software with necessary hardware. The technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal or a network device, etc.) to execute the method according to the embodiment of the present application.

[0099] The software product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0100] Computer readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program codes contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0101] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0102] The computer-readable medium carries one or more programs. When the one or more programs are executed by a device, the computer-readable medium implements the aforementioned functions.

[0103] Those skilled in the art will appreciate that the above modules can be distributed in the device according to the description of the embodiment, or can be changed accordingly and only used in one or more devices different from the embodiment. The modules of the above embodiments can be combined into one module, or further divided into multiple sub-modules.

[0104] The embodiments of the present application are described in detail above, and the description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, changes or deformations made by those skilled in the art based on the idea of ​​the present application, the specific implementation method and the scope of application of the present application, all belong to the scope of protection of the present application. In summary, the content of this specification should not be understood as limiting the present application.

Claims

1. A method for constructing a digital cell model, characterized in that: include: Compile the cell language through the intelligent processing module and obtain the recompilation data; generating a digital cell model based on the recompiled data; Fine-tuning the digital cell model; The simulation results are obtained through the fine-tuned digital cell model and verified.

2. The method according to claim 1, characterized in that: Before performing cellular language compilation by the intelligent processing module and obtaining recompilation data, the method further includes: Get multiple preset smart models; Fine-tuning the plurality of preset intelligent models by using preset multimodal biological data; The intelligent processing module is constructed based on a plurality of fine-tuned preset intelligent models.

3. The method according to claim 2, characterized in that Compile the cell language through the intelligent processing module and obtain the recompilation data, including: identifying, by the intelligent processing module, a plurality of data types and biological information contained in the multimodal biological data; Based on the biological information, the intelligent processing module performs cellular language compilation on the multimodal biological data to obtain the recompiled data; Based on the multiple data types, the intelligent processing module performs dimensionality reduction processing on the recompiled data; and A connection between different types of data in the recompiled data is constructed.

4. The method according to claim 3, characterized in that Based on the biological information, the multimodal biological data is subjected to cellular language compilation by the intelligent processing module to obtain the recompiled data, including: Compiling the multimodal biological data into a cellular language by the intelligent processing module to obtain a unified token of the multimodal biological data; A mapping relationship between the unified token and tokens corresponding to data of different data types in the multimodal biological data is constructed.

5. The method according to claim 3, characterized in that: Based on the recompiled data, a digital cell model is generated, comprising: Acquiring cell component structure information and spatiotemporal information in the multimodal biological data; Based on the recompiled data that has been subjected to dimensionality reduction processing, the digital cell model is constructed by combining the cell component structure information and the spatiotemporal information through a preset modeling method.

6. The method according to claim 4, characterized in that Fine-tuning the digital cell model includes: Obtain biological information and cell characteristics of preset cells; Decoding the digital cell model based on the mapping relationship and the biological information of the preset cells; The decoded digital cell model is fine-tuned according to the cell characteristics so that the digital cell model can simulate the preset cell.

7. The method according to claim 6, characterized in that Acquire simulation results using the fine-tuned digital cell model and validate the simulation results, including: Inputting the preset signal mediating factors and the recompiled data corresponding to the preset cells into the digital cell model to obtain the simulation result; Comparing and verifying the simulation results with experimental data, and generating verification results; The verification result is fed back to the intelligent processing module.

8. A device for constructing a digital cell model, characterized in that: include: A data compilation module is used to compile the cell language through the intelligent processing module and obtain recompiled data; A model building module, for generating a digital cell model based on the recompiled data; A model training module, used for fine-tuning the digital cell model; The simulation verification module is used to obtain simulation results through the fine-tuned digital cell model and verify the simulation results.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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