Cancer cell reverse reprogramming treatment system based on multi-mode AI digital model

Abstract

The invention relates to the technical field of biomedical engineering, and discloses a cancer cell reverse reprogramming treatment system based on a multi-mode AI digital model, and the system comprises a high-order nonlinear dynamic model module which is used for describing a high-order nonlinear dynamic model of cancer cell state change; the multi-omics data acquisition module is used for acquiring multi-omics data of cancer cells; the optimal control module is used for performing optimal control according to the high-order nonlinear dynamic model and the multi-omics data; and the quantum calculation optimization module is used for implementing the optimal control and accelerating the solution of the optimal control problem through quantum calculation. The cancer cell reverse reprogramming treatment system based on the multi-modal AI digital model is adopted, a high-order nonlinear dynamic model and multi-omics data collection are combined, the technical effect of accurately modeling the cancer cell state is achieved, and the dynamic change of cancer cells can be more comprehensive by integrating multi-dimensional data.

Description

Technical Field

[0001] The present invention relates to the field of biomedical engineering technology, and specifically to a cancer cell reverse reprogramming treatment system based on a multimodal AI digital model. Background Art

[0002] The technical goal of reverse reprogramming of cancer cells is to transform cancer cells from a pathological state to a healthy state through precise intervention measures. The treatment of liver cancer cells is extremely complex because the state evolution of cells is not only affected by genetic factors, but also involves multi-dimensional interactions such as cell metabolism and epigenetics. By combining graph neural networks (GNN) and conditional generative adversarial networks (cGAN) to simulate these complex biological processes, cell state changes under different intervention conditions can be generated. How to accurately find the optimal treatment path in these complex data and models makes the reverse reprogramming process efficient and accurate.

[0003] Currently, many cancer treatments still rely on a single type of omics data (such as the genome, metabolome or epigenome). Although it can provide certain aspects of cellular information, it cannot fully reflect the dynamic changes of cancer cells. Traditional methods focus more on gene expression data, but they cannot capture the complex interactions between genes, metabolites and epigenetics within cells. Therefore, existing technologies often ignore these intertwined and complex biological characteristics when modeling the state of cancer cells, resulting in low accuracy and reliability of the model, and difficulty in sustaining or personalizing the treatment effect.

[0004] In the existing technology, most cancer treatment plans rely on classical optimization methods to derive the optimal intervention path. In high-dimensional dynamic systems, classical optimization algorithms often face the problems of huge computational complexity and long computational time. Especially when dealing with multi-omics data and high-order nonlinear dynamic changes of cancer cells, the efficiency and accuracy of traditional algorithms are often difficult to meet the needs of real-time treatment. Therefore, when optimizing treatment plans, the existing technology cannot respond quickly to changes in the state of cancer cells, resulting in delayed adjustments during the treatment process, which may affect the treatment effect. Summary of the invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a cancer cell reverse reprogramming treatment system based on a multimodal AI digital model, which solves the problem that traditional methods focus more on gene expression data but cannot capture the complex interactions between genes, metabolites and epigenetics within cells.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a cancer cell reverse reprogramming treatment system based on a multimodal AI digital model, comprising:

[0007] High-order nonlinear dynamics model module, which is used to describe the high-order nonlinear dynamics model of cancer cell state changes;

[0008] Multi-omics data acquisition module, used to obtain multi-omics data of cancer cells;

[0009] An optimal control module, for performing optimal control according to the high-order nonlinear dynamics model and multi-omics data;

[0010] The quantum computing optimization module is used to implement the optimal control and accelerate the solution of the optimal control problem through quantum computing.

[0011] Preferably, the high-order nonlinear dynamics model module uses ordinary differential equations to represent the changes in cancer cell states over time, and the model includes:

[0012] Gene expression equation, which describes the changes in gene expression of cancer cells over time;

[0013] Metabolite concentration equation, which describes the changes in metabolite concentrations within cancer cells;

[0014] Epigenetic equations that describe the effects of epigenetic modifications on the state of cancer cells.

[0015] Preferably, the multi-omics data acquisition module comprises:

[0016] A transcriptome data collection unit, used to obtain gene expression information of cancer cells, wherein the information is collected by high-throughput RNA sequencing technology;

[0017] A metabolomics data acquisition unit, used to obtain the metabolite concentration of cancer cells, wherein the concentration information is measured by mass spectrometry or nuclear magnetic resonance technology;

[0018] The epigenetic data acquisition unit is used to obtain epigenetic modification data of cancer cells, and the data is analyzed by chromatin immunoprecipitation sequencing technology or methylation chip technology.

[0019] Preferably, the optimal control module includes: an objective function definition unit, which is used to define an optimal control objective function, wherein the objective function calculates and optimizes the control strategy by minimizing the gap between the cancer cell state and the target health state, and the objective function is:

[0020]

[0021] in, is the high-order derivative of the cell state, representing the dynamic evolution of cancer cells, is the dynamic evolution of the target health state, u(t) is the control strategy, and u target(t) is the target control strategy, λ and γ are the weights of the control cost term and the target control strategy; the control strategy derivation unit is used to derive the optimal control strategy through the variational method and the Lagrange multiplier method according to the objective function and the constraints.

[0022] Preferably, the quantum computing optimization module includes:

[0023] A quantum variational optimization unit, which is used to accelerate the solution of optimal control problems. The quantum variational optimization algorithm represents different control strategies through the superposition state of quantum bits, uses quantum gate operations for parallel calculations, and quickly searches for the optimal solution in a high-dimensional state space;

[0024] The quantum measurement and feedback unit is used to adjust and optimize the control strategy according to the results of quantum calculation, and feed back the optimization results to the optimal control module.

[0025] Preferably, the quantum computing optimization module includes:

[0026] The quantum measurement and feedback unit is used to optimize the control strategy according to the quantum calculation results, and feed back the optimal control strategy to the optimal control module to adjust the intervention path.

[0027] Preferably, the system comprises:

[0028] The graph neural network module is used to construct the gene and metabolite regulatory network of cancer cells through the relationship between nodes and edges, capture the regulatory mechanism of cell state changes, and identify key regulatory factors;

[0029] The graph convolutional network unit is used to update node features through graph convolution operations and model the regulatory relationship between genes and metabolites using the adjacency matrix and weight matrix.

[0030] Preferably, the graph neural network module updates node features through a graph convolutional network, and models the interaction between genes and metabolites in combination with an adjacency matrix and a weight matrix.

[0031] Preferably, the system comprises:

[0032] The conditional generative adversarial network module is used to simulate the state migration of cancer cells under different intervention conditions, generate cell state predictions under intervention, and provide feedback data for the optimal control module.

[0033] A method for reverse reprogramming a cancer cell therapeutic system, comprising the following steps:

[0034] Obtain multi-omics data of cancer cells, including transcriptome, metabolome, and epigenome data;

[0035] Use high-order nonlinear dynamic models to describe changes in cancer cell states and take multi-omics data as input;

[0036] defining an optimal control objective function according to the high-order nonlinear dynamics model and multi-omics data, and deriving an optimal control strategy;

[0037] Use the Quantum Computing Optimization Module to accelerate the solution of optimal control problems through quantum variational optimization algorithms;

[0038] Establish cancer cell regulatory networks through graph neural network modules to capture the relationships between genes and metabolites;

[0039] A conditional generative adversarial network module is used to simulate the migration of cell states and provide data support for optimizing treatment plans.

[0040] The present invention provides a cancer cell reverse reprogramming treatment system based on a multimodal AI digital model, which has the following beneficial effects:

[0041] 1. The present invention adopts a cancer cell reverse reprogramming treatment system based on a multimodal AI digital model, combined with a high-order nonlinear dynamic model and multi-omics data collection, to achieve the technical effect of accurately modeling the state of cancer cells. Compared with the treatment methods that only rely on a single omics data or linear model in the prior art, the present invention integrates multi-dimensional data to make the dynamic changes of cancer cells more comprehensive and accurate. Prediction, providing higher accuracy for subsequent treatment plans.

[0042] 2. The present invention introduces a quantum computing optimization module and uses a quantum variational optimization algorithm to accelerate the solution of the optimal control problem, achieving the technical effect of greatly improving the computing efficiency and solution speed. Compared with the traditional classical optimization method in the prior art, the present invention not only shortens the calculation time when optimizing high-dimensional dynamic systems, but also can handle more complex cell state changes, greatly improving the implementation efficiency of the treatment plan.

[0043] 3. The present invention adopts a method combining graph neural network (GNN) and conditional generative adversarial network (cGAN) modules to simulate the state migration of cancer cells under different interventions in real time. Compared with the treatment schemes in the prior art that lack dynamic adjustment and accurate prediction, the present invention captures the complex regulatory network inside the cell through GNN, and generates more accurate cell state predictions through cGAN, so that the treatment scheme can be optimized in real time in a dynamic environment.

[0044] 4. The optimal control module of the present invention provides a solution for dynamically adjusting the intervention strategy by combining the variational method, the Lagrange multiplier method and quantum computing optimization. Different from the static or single optimization methods in the prior art, the present invention can adjust the treatment path according to the real-time feedback information of cancer cells, ensure the continuous effectiveness during the treatment process, and minimize side effects, which significantly improves the personalization and safety of cancer treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a system framework diagram of the present invention;

[0046] Figure 2 It is a schematic diagram of the multi-omics data acquisition module of the present invention;

[0047] Figure 3 This is a schematic diagram of the quantum computing optimization module of the present invention;

[0048] Figure 4 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Please see attached Figure 1 -Attached Figure 3 The embodiment of the present invention provides a cancer cell reverse reprogramming treatment system based on a multimodal AI digital model, comprising:

[0051] High-order nonlinear dynamics model module, which is used to describe the high-order nonlinear dynamics model of cancer cell state changes;

[0052] Multi-omics data acquisition module, used to obtain multi-omics data of cancer cells;

[0053] An optimal control module, for performing optimal control according to the high-order nonlinear dynamics model and multi-omics data;

[0054] The quantum computing optimization module is used to implement the optimal control and accelerate the solution of the optimal control problem through quantum computing.

[0055] High-order nonlinear dynamics model module

[0056] The core of this module is to construct a high-order nonlinear dynamic model of how the state of cancer cells changes over time. The state of cancer cells is not only affected by genetic factors, but also closely related to metabolites, environmental factors, etc. Therefore, the model needs to comprehensively consider the interaction of multiple factors.

[0057] The model uses ordinary differential equations (ODE) to describe the state changes of cancer cells. For example, changes in gene expression, metabolite concentrations, and epigenetics will all have an impact on the cell state. By setting higher-order differential equations, the details of cell changes, especially nonlinear coupling relationships, can be better captured. As time goes by, the various states of cells gradually change, and these changes are accurately represented by equations, providing an accurate basis for the formulation of subsequent control strategies.

[0058] The advantage of this high-order nonlinear modeling method is that it can simulate more complex cell behaviors. Conventional linear models may not be able to accurately capture the mutation process of cancer cells, drug response, and cancer progression. However, this nonlinear dynamic model can truly reflect the state changes of cells under the intervention of different biological factors, thereby improving the accuracy and effectiveness of treatment.

[0059] Multi-omics data acquisition module

[0060] Multi-omics data is the key to personalized cancer treatment. By collecting transcriptome, metabolome, and epigenomic data of cancer cells, the system can fully understand the biological characteristics of cancer cells and provide sufficient data support for modeling. Specifically:

[0061] Transcriptome data: RNA sequencing is used to obtain the expression of each gene in cancer cells. These data reveal the activity of genes in cells under specific conditions and can help us identify key oncogenes and their response to treatment.

[0062] Metabolome data: Mass spectrometry or nuclear magnetic resonance technology can be used to measure the concentration of metabolites in cancer cells. Metabolites not only affect the energy metabolism of cells, but may also play an important role in the proliferation and metastasis of cancer cells.

[0063] Epigenetic data: Chromatin immunoprecipitation sequencing (Ch IP-seq) or methylation chip technology can capture epigenetic modifications in cells and reflect the regulatory mechanism of gene expression.

[0064] The comprehensive use of these data provides us with detailed information on cancer cells at the genetic, metabolic, and epigenetic levels, greatly improving the accuracy of the model and the ability to predict treatment. Through precise multi-omics data, the system can identify the "weaknesses" of cancer cells, thereby providing precise guidance for subsequent treatment paths.

[0065] Optimal control module

[0066] The optimal control module is the core component of the system. Its goal is to restore cancer cells from an abnormal state to a healthy state by optimizing intervention strategies. Optimal control does not simply pursue the shortest time or lowest cost, but rather a comprehensive optimization based on the dynamic state of cancer cells.

[0067] In this module, we first measure the gap between the cell state and the target health state by defining an objective function. In the design of the objective function, we not only consider the precise recovery of the cell state, but also consider factors such as resource consumption and drug side effects during the treatment process. Through the variational method and Lagrange multiplier method, we can derive the optimal intervention strategy. This process is dynamic. As the state of cancer cells changes, the control strategy will be continuously adjusted to meet new challenges.

[0068] This treatment method based on optimal control not only takes into account the efficacy, but also can effectively reduce unnecessary side effects and drug usage, thereby maximizing the safety and effectiveness of treatment.

[0069] Quantum Computing Optimization Module

[0070] The quantum computing optimization module is a highlight of this invention. By introducing the quantum variational optimization algorithm (QVO), we can accelerate the solution process of the optimal control problem. Traditional optimization methods, especially when dealing with high-dimensional and complex data, often have huge computational complexity and low efficiency. Quantum computing greatly improves the speed of problem solving through quantum superposition and quantum parallelism.

[0071] Quantum variational optimization algorithms use the parallel processing power of quantum bits to evaluate a large number of possible strategies in a shorter time when exploring control strategies. This provides us with more powerful computing power for decision-making in cancer treatment, which can quickly respond to changes in cancer cells and treatment progress.

[0072] The advantage of quantum computing is its strong adaptability. As the optimization process deepens, quantum computing can provide more accurate optimization solutions, and through the quantum measurement feedback mechanism, it can continuously adjust the optimal control strategy. This allows the treatment plan to continuously adapt and optimize in a dynamic environment, improving the level of personalized treatment.

[0073] Graph Neural Network Module

[0074] Genes and metabolites in cancer cells do not exist in isolation, but interact with each other through complex regulatory networks. Therefore, graph neural networks (GNNs) are introduced to model the regulatory networks of cancer cells. Through graph neural networks, we can capture the relationship between genes and metabolites and identify key factors that have an important impact on the growth and metastasis of cancer cells.

[0075] GNN can not only analyze a single node (gene or metabolite) but also analyze the mutual influence between nodes by updating nodes and edges. Using graph convolutional networks (GCN), we can iteratively update the features of each node and optimize the regulatory network of cancer cells. This process can reveal the intrinsic mechanism of cancer cell state changes and further provide a theoretical basis for the optimization of treatment plans.

[0076] Conditional Generative Adversarial Network (cGAN) Module

[0077] In order to more accurately simulate the state migration of cancer cells under different treatment conditions, we introduced the conditional generative adversarial network (cGAN). cGAN can simulate the behavior of cancer cells under different intervention conditions through adversarial training of the generator and the discriminator. The generator generates predicted cell states based on the control strategy, while the discriminator determines whether the generated state is reasonable.

[0078] This adversarial training method enables the generator to better learn the state change patterns of cancer cells, thereby providing more accurate prediction data for the optimal control module. Through continuous iterative training, the generator can generate cell state data that is more in line with the actual treatment effect, providing data support for the adjustment and optimization of treatment plans.

[0079] System Integration and Advantages

[0080] The system of the present invention integrates multiple advanced technologies to build a comprehensive cancer cell reverse reprogramming treatment platform. The organic combination of various modules enables the system to have efficient modeling, prediction and optimization capabilities. From the collection of multi-omics data to the optimal control of cell status, the entire process realizes personalized treatment and improves the accuracy and effectiveness of treatment.

[0081] Through the acceleration of quantum computing, the system can quickly adjust treatment strategies in complex dynamic environments, avoiding the computational bottlenecks of traditional methods. The addition of the graph neural network module enables a more comprehensive modeling of the interactions between various factors in cancer cells, improving the scientificity and reliability of the treatment plan. The conditional generative adversarial network further enhances the model's predictive ability, making treatment more personalized and accurate.

[0082] Module 1: High-order nonlinear dynamics model module

[0083] The high-order nonlinear dynamic model module in this embodiment is used to describe the dynamic process of cancer cell state changes, especially the evolution law under different intervention measures. This module constructs a cancer cell state change model through ordinary differential equations (ODE) to ensure that the biological characteristics of the cells can be accurately reflected. The state of cancer cells is not only determined by factors such as gene expression, metabolite concentration, and epigenetic modification, but also there are complex interactions between these factors. Therefore, the use of high-order nonlinear dynamic models can better capture these nonlinear and coupling effects, thereby providing more accurate model predictions.

[0084] To achieve this goal, the high-order nonlinear dynamic model models the evolution of cancer cell states in multiple dimensions. Factors at different biological levels are described by corresponding equations, which include data from multiple levels such as genes, metabolites, and epigenetics. The model can track changes in cell states over time and simulate the responses and changing trends of cancer cells under different interventions.

[0085] Specifically, the high-order nonlinear dynamics model module includes but is not limited to the following aspects:

[0086] First, the model in this embodiment uses ordinary differential equations (ODE) to model the state changes of cancer cells. The state vector x(t) of the cancer cell at time t contains multiple components, each of which represents a biological level in the cancer cell, such as gene expression, metabolite concentration, and epigenetic modification. The evolution of the cancer cell state can be represented by an equation of the following form:

[0087]

[0088] in, is a state vector, representing multiple biological characteristics of cancer cells at time t (e.g., gene expression, metabolite concentration, etc.); is the control vector, representing intervention measures (such as drug concentration, gene editing, etc.); f n (x(t),u(t),t) is a high-order nonlinear function that describes the state changes of cancer cells, reflecting the nonlinear coupling between multiple factors inside the cell.

[0089] In general, f n(x(t), u(t), t) includes the feedback relationship between genes and metabolism inside cancer cells, and their interactive effects with external interventions. Specifically, the biological responses of cancer cells are usually nonlinear. These nonlinear effects will lead to the emergence of higher-order derivatives of the state equation, making the model more biologically realistic. As an option, higher-order derivative terms can be introduced into the model to represent the acceleration and jerk effects of the cancer cell state. These effects usually play an important role in processes such as cell cycle or drug response. By introducing these higher-order derivative terms, the changes of cancer cells under different environmental conditions can be more accurately captured. In particular, in some complex biological processes, the dynamic response of cells cannot be described by simple first-order derivatives. In one possible implementation, the higher-order kinetic equation can include sub-equations of the following form to describe the changes in cell states at different biological levels:

[0090]

[0091] Among them, x i (t) represents the state of the i-th biological level (such as gene expression, metabolite concentration, etc.), and f i (x1(t),x2(t),...,x n (t),u(t),t) is a function that describes the evolution of this biological level over time. This function not only takes into account the current state, but also includes feedback effects and intervention measures at other levels.

[0092] Specifically, the gene expression equation, metabolite concentration equation, and epigenetic modification equation can be modeled as nonlinear functions, respectively, reflecting the complex interactions between them. The feedback relationship between gene expression and metabolite concentration, as well as the impact of gene epigenetic modification on cell state, will all be quantified through these equations and incorporated into the model.

[0093] In some embodiments, considering the time-varying characteristics of the dynamic changes of cancer cells, the equation can also be updated in real time based on experimental data. For example, after the cells undergo drug intervention, the parameters in the state equation may change, and the system will update these parameters based on real-time feedback to better simulate the cell's response.

[0094] In addition, the control strategy u(t) in the model may also be dynamically adjusted according to the current state of the cancer cells. For example, certain intervention measures (such as drug concentration or gene editing intensity) may be feedback adjusted according to the current state of the cancer cells to ensure that the cells can develop towards the target healthy state.

[0095] In summary, the high-order nonlinear dynamic model module in this embodiment provides precise mathematical support for the reverse reprogramming of cancer cells by considering the multi-dimensional nonlinear changes of cell states. This module can not only simulate the evolution process of cancer cells under the influence of different biological factors, but also combine external intervention for dynamic control, providing the necessary foundation for subsequent optimal control strategies.

[0096] Through this precise modeling method, the present invention can significantly improve the effect of cancer treatment, especially when dealing with complex and dynamic cancer cell responses. Compared with traditional linear models, it can simulate the state changes of cancer cells more realistically and comprehensively.

[0097] Module 2: Multi-omics data collection module

[0098] The multi-omics data acquisition module in this embodiment is used to obtain multi-omics data of cancer cells. These data provide the system with information on multiple biological levels such as genes, metabolite concentrations and epigenetics of cancer cells, and provide necessary inputs for the high-order nonlinear dynamic model module. Through the collection of multi-omics data, the system can comprehensively describe the state of cancer cells and provide accurate basic data for subsequent optimal control and treatment plans. The collection of multi-omics data not only helps to understand the biological characteristics of cancer cells, but also reveals their response laws under different treatment conditions.

[0099] In the present invention, the multi-omics data include but are not limited to transcriptome data, metabolome data and epigenomic data. Each type of data corresponds to different biological characteristics of cancer cells, and they interact with each other and jointly affect the state of cells. Through the comprehensive analysis of these data, the system can more accurately identify the characteristics and treatment targets of cancer cells.

[0100] In this embodiment, the transcriptome data acquisition unit is used to obtain gene expression information of cancer cells. Gene expression refers to the intensity of transcriptional activity of each gene in cancer cells at a specific time point, reflecting the level of gene regulation in cells. Through high-throughput RNA sequencing technology, the expression of each gene can be accurately measured and converted into digital signals for subsequent modeling and control.

[0101] In general, RNA sequencing technology can provide accurate gene expression profiles, identify possible mutations, upregulated or downregulated genes in cancer cells, and provide support for modeling the dynamic evolution of cancer cells. Through these data, the system can identify key oncogenes and intervene in subsequent optimal control modules.

[0102] As an option, RNA sequencing data can be further jointly analyzed with other omics data to find the synergistic effects of cancer cells at different omics levels. For example, changes in gene expression may affect the generation of metabolites, and this relationship will be further expanded and analyzed in the subsequent data fusion process.

[0103] The metabolomics data acquisition unit is used to obtain the concentration information of metabolites in cancer cells. Metabolites are chemical substances produced in cells that reflect the metabolic state of cells. Cancer cells usually have unique metabolic characteristics, such as high glycolysis and metabolic adaptation in low oxygen environments. Therefore, metabolomics data is an indispensable part of cancer cell status analysis.

[0104] Metabolite concentrations can be measured by mass spectrometry (MS) or nuclear magnetic resonance (NMR) techniques. These techniques can provide detailed information about all small molecule metabolites in cancer cells, including sugars, lipids, amino acids and other intracellular small molecules. Mass spectrometry is particularly suitable for large-scale metabolomics studies, which can quickly identify metabolites and determine their concentration changes.

[0105] Specifically, metabolomics data provides important support for the dynamic modeling of cancer cells. The metabolic state of cancer cells directly affects their proliferation, survival, and metastasis capabilities. For example, changes in the concentration of certain metabolites may directly reflect the response of cancer cells to drugs. Therefore, collecting metabolomics data is crucial for developing precise treatment plans.

[0106] In one possible implementation, metabolite concentrations can be combined with transcriptome data for analysis to reveal the relationship between gene expression and metabolic responses. This fusion of multi-omics data helps to systematically and deeply understand the metabolic characteristics of cancer cells and provide theoretical support for subsequent intervention strategies.

[0107] The epigenetic data acquisition unit is used to obtain epigenetic modification information of cancer cells. Epigenetic modification refers to the process by which gene expression is regulated without changing the genome sequence, usually manifested as DNA methylation, histone modification, etc. Changes in the epigenetic group play an important role in the occurrence and progression of cancer, so its collection and analysis are particularly important for reverse reprogramming therapy of cancer cells.

[0108] Epigenomic data can be collected through chromatin immunoprecipitation sequencing (Ch IP-seq) or DNA methylation chip technology, which can analyze the epigenetic modification patterns of the genome in cancer cells and reveal the gene expression regulation mechanism of cells.

[0109] For example, changes in DNA methylation may lead to the silencing of certain tumor suppressor genes or the activation of oncogenes. These changes can affect the proliferation, apoptosis, and metastasis of cancer cells. By collecting epigenomic data, the system can monitor the epigenetic state of cancer cells and provide support for subsequent treatment decisions.

[0110] Alternatively, analysis of epigenomic data can be combined with transcriptomic data to help researchers reveal how epigenetic modifications regulate gene expression. For example, in some cancer types, epigenetic modifications of genes may be key factors driving the abnormal proliferation of cancer cells. By precisely controlling these modifications, the normal function of genes can be restored, thereby reversing the malignant state of cancer cells.

[0111] In this embodiment, transcriptomic data, metabolomic data and epigenomic data will be integrated through specific data processing and analysis tools. Generally, omics data from different sources need to be standardized to ensure data comparability between different experimental techniques. In the data integration process, multidimensional data fusion technology is used to combine data at different levels to form a comprehensive description of the cancer cell status. This multi-omics data fusion method can reveal the complex biological mechanisms of cancer cells and provide more accurate input for subsequent modeling and intervention.

[0112] Specifically, the data integration module can use machine learning or deep learning algorithms to combine the characteristics of the transcriptome, metabolome and epigenome to perform feature extraction and data dimensionality reduction. These data will be used as input for the high-order nonlinear dynamic model module to further improve the accuracy and reliability of the model.

[0113] In summary, the multi-omics data acquisition module in this embodiment provides comprehensive data support for subsequent high-order nonlinear dynamics modeling by collecting transcriptome, metabolome and epigenomic data of cancer cells. By comprehensively analyzing these omics data, the system can more accurately depict the dynamic state of cancer cells and reveal the interactions between various biological levels in cells. The design of this module lays the foundation for the implementation of reverse reprogramming therapy for cancer cells and provides reliable data support for subsequent optimal control and treatment plans.

[0114] Module 3: Optimal Control Module

[0115] In this embodiment, the optimal control module is one of the core components of the present invention. Its purpose is to restore the state of cancer cells from an abnormal state to a healthy state by optimizing the control strategy based on the dynamic evolution model of cancer cells and multi-omics data. This module not only considers the biological dynamic changes of cancer cells, but also considers the effect and cost of implementing the control strategy, ensuring that the treatment process achieves the optimal treatment effect while reducing resource waste and the occurrence of side effects.

[0116] The optimal control module is closely related to the aforementioned high-order nonlinear dynamic model module and multi-omics data acquisition module. The former provides a mathematical model of the dynamic changes of cancer cells for this module, and the latter provides detailed input data of the state of cancer cells for this module. Based on this information, the optimal control module determines the optimal intervention path by solving the optimization problem, thereby gradually restoring the cancer cells to a healthy state.

[0117] The optimal control module in this embodiment first defines the optimal control objective function. This objective function is used to measure the gap between the current state of cancer cells and the target health state, while considering the execution cost and treatment effect of the intervention strategy. Specifically, the optimal control objective function consists of the following parts:

[0118] State error term: measures the difference between the current cell state and the target health state.

[0119] Control cost item: represents the cost required for intervention measures, which aims to control the intensity of intervention measures and avoid side effects caused by excessive intervention.

[0120] Target control deviation item: Measures the deviation between the actual control strategy and the target control strategy, helping the system adjust the intervention path to ensure the scientificity and accuracy of the treatment.

[0121] The mathematical expression of the objective function is:

[0122]

[0123] in, It is the high-order derivative of the cancer cell state, indicating the dynamic evolution of the cell and reflecting processes such as cell proliferation and metastasis; is the dynamic evolution of the target health state, which is usually determined by experimental data or medical standards; u(t) is the control strategy vector, which represents the intervention measures (such as drug concentration, gene editing intensity, etc.); u target (t) is the target intervention strategy, which is usually set according to clinical guidance or treatment plan; λ and γ are the weight coefficients of the control cost term and the target control strategy deviation.

[0124] In this embodiment, the derivation of the control strategy is based on the variational method and the Lagrange multiplier method. By deriving the objective function J(u(t)) with respect to the control strategy u(t) and the cell state x(t), we can obtain the equation of the optimal control strategy. Specifically, firstly, a Lagrangian function is constructed, and the constraints of the control problem (such as the cancer cell state equation) and the objective function are combined to obtain the Lagrangian equation in the following form:

[0125]

[0126] Among them, λ1 is the Lagrange multiplier, which represents the weight of the state equation constraint, which is used to ensure that the evolution of the cell state conforms to the description of the high-order nonlinear dynamic model. The partial derivatives of the Lagrangian function L with respect to x(t) and u(t) are obtained to obtain a set of optimization equations. By solving these equations, the optimal control strategy u * (t) can be obtained.

[0127] In order to solve the above optimization problems, the system needs to use numerical optimization methods. In general, the solution process of the optimal control problem may involve a high-dimensional state space, so an efficient numerical algorithm is needed to solve it. For example, classical optimization algorithms such as gradient descent and Newton's method can be used, or combined with more modern optimization methods such as genetic algorithms and simulated annealing.

[0128] In a possible implementation, the optimal control module in this embodiment adopts an optimization algorithm based on iterative solution. First, the objective function value and its gradient are calculated according to the current cell state and control strategy. Then, the control strategy is gradually adjusted in an iterative manner until the objective function reaches a minimum value or converges. This method can ensure that the optimal intervention path is found under the constraints of multi-omics data and high-order kinetic models.

[0129] In some embodiments, the optimal control problem not only minimizes the objective function, but also needs to consider some physical, clinical or ethical constraints. For example, the intensity of the intervention measure u(t) may be limited by factors such as drug dosage and gene editing range, and the rate of change of the control strategy u(t) may also be subject to biological constraints. In this case, the change of the control strategy can be limited by adding constraints to the optimization problem. For example, the boundary conditions of the control strategy can be expressed as:

[0130] u min ≤u(t)≤u max

[0131] Among them, u min and u max They are the minimum and maximum values ​​of the control strategy, respectively. These values ​​can be set according to the specific treatment plan.

[0132] It is worth noting that the optimal control module in this embodiment is not static. During the treatment process, the state of cancer cells will change over time, so the control strategy needs to be adjusted dynamically. In actual operation, the system will update the optimal control strategy according to the real-time state of cancer cells, so as to better cope with changes in the state of cancer cells and fluctuations in external intervention conditions.

[0133] For example, when the cell's response to a drug weakens, the optimal control module will restore the therapeutic effect by adjusting the drug concentration or adding other treatment methods. This dynamic adjustment process can be automatically achieved by real-time monitoring of the cancer cell state and combining it with optimal control theory.

[0134] The optimal control module in this embodiment can provide a scientific and precise intervention path for the reverse reprogramming treatment of cancer cells through precise objective function definition and optimization algorithm. By modeling and solving the complex relationship between cancer cell status and treatment strategy, the system can automatically generate the optimal treatment plan and dynamically adjust the control strategy according to the real-time changes in cell status to ensure the maximization of treatment effect. At the same time, the design of the optimal control module takes into account cost and resource constraints, providing an efficient and sustainable solution for cancer treatment.

[0135] Module 4: Quantum Computing Optimization Module

[0136] In this embodiment, the quantum computing optimization module is used to accelerate the solution process of the optimal control problem, especially the optimization problem in high-dimensional and complex data environment. Since the dynamic evolution model of cancer cells involves a large amount of calculation and solution, the traditional classical optimization algorithm is less efficient when dealing with these large-scale problems. Quantum computing, through its unique parallel computing characteristics, can process a large number of possible solutions in a shorter time, thereby providing a more efficient optimization solution for the reverse reprogramming treatment of cancer cells.

[0137] The aforementioned high-order nonlinear dynamics model module and optimal control module provide the necessary data input for the quantum computing optimization module. The high-order dynamics model module provides the state and evolution equations of cancer cells, while the optimal control module optimizes the treatment path by defining the objective function. The core task of the quantum computing optimization module is to accelerate the solution of the optimal control problem through quantum algorithms, making the optimization process more efficient and providing support for the rapid adjustment of treatment plans.

[0138] In this embodiment, the quantum computing optimization module mainly adopts the quantum variational optimization algorithm (QVO). QVO represents different control strategies through the superposition state of quantum bits, thereby calculating multiple possible optimization paths in parallel. Generally, the quantum variational optimization algorithm can explore multiple control strategy spaces simultaneously through quantum gate operations, significantly improving computing efficiency.

[0139] Specifically, the basic principle of QVO is to use the quantum bits of a quantum computer to represent multiple states in the solution space. Each quantum bit can be in multiple states at the same time (i.e., superposition state), so that multiple control schemes can be processed in one calculation. This parallel computing capability greatly accelerates the solution process, especially when facing complex and high-dimensional dynamic systems.

[0140] The objective function is:

[0141]

[0142] in, is the high-order derivative of the cell state, representing the dynamic evolution of cancer cells, is the dynamic evolution of the target health state, u(t) is the control strategy, and u target (t) is the target control strategy, λ and γ are the weights of the control cost term and the target control strategy; the control strategy derivation unit is used to derive the optimal control strategy through the variational method and the Lagrange multiplier method according to the objective function and the constraints.

[0143] The quantum computing optimization module in this embodiment uses the superposition state characteristics of quantum bits for parallel computing. Quantum bits can represent multiple states simultaneously in quantum state space. Therefore, multiple control strategies can be evaluated at the same time. Through quantum gate operations, multiple control paths can be calculated in parallel in each time step, and the optimal control strategy can be quickly selected and updated based on the results of quantum computing.

[0144] For example, suppose we need to search for m control strategies during the optimization process. These strategies may change at multiple time points. The quantum computing optimization module processes these m control strategies simultaneously in the same calculation step through the acoustic addition state of the quantum bit, thereby reducing the calculation time. Finally, the quantum computing optimization module selects the optimal control strategy u through quantum measurement operations. * (t).

[0145] An important feature of the quantum computing optimization module is the quantum measurement and feedback mechanism. During the optimization process, the results of quantum computing are often given in the form of probability distribution. The optimal control strategy can be determined through quantum measurement. The measurement results will be used as feedback information and input into the optimal control module to guide subsequent intervention strategy updates.

[0146] Quantum measurement usually involves the "collapse" of the quantum bit state, that is, the quantum bit changes from a superposition state to a definite state. Through this process, the system can select the best control strategy and dynamically adjust the treatment plan based on these results. In practical applications, this feedback mechanism can adjust the treatment path according to the real-time changes of cancer cells to maximize the treatment effect.

[0147] The quantum computing optimization module in this embodiment can significantly improve the efficiency of solving the problem of dynamic control of cancer cells by using the quantum variational optimization algorithm. Compared with traditional classical computing methods, quantum computing has the following advantages:

[0148] Strong parallel computing capabilities: Quantum bits can represent multiple states at the same time, significantly speeding up the computing process.

[0149] High efficiency in solving high-dimensional problems: When faced with high-dimensional state space, the computing time of traditional classical computing will increase exponentially, while quantum computing can efficiently search for the optimal solution in multi-dimensional space.

[0150] Adapting to dynamic changes: The feedback mechanism of quantum computing enables the optimization strategy to be adjusted in real time according to the changes in the state of cancer cells, ensuring the flexibility of treatment plans.

[0151] The introduction of the quantum computing optimization module provides strong support for the reverse reprogramming treatment of cancer cells. During cancer treatment, the state and environment of cancer cells will change over time, so the optimal control strategy needs to be adjusted in real time. The quantum computing optimization module can quickly handle complex control problems and adjust the control path based on feedback from the treatment effect.

[0152] In some types of cancer, cells' response to drugs may gradually weaken, or the metabolic characteristics of cancer cells may change at different stages. The quantum computing optimization module can dynamically update the control strategy based on these changes and ensure continuous optimization of the treatment effect. With the introduction of quantum computing, the treatment plan can be accurately adjusted in a short time, significantly improving the treatment efficiency.

[0153] The quantum computing optimization module in this embodiment accelerates the solution of the optimal control problem through the quantum variational optimization algorithm. The parallel computing capability and high-dimensional optimization efficiency of quantum computing enable the reverse reprogramming treatment of cancer cells to be efficiently solved in a shorter time. In addition, the quantum measurement and feedback mechanism can ensure that the treatment plan is dynamically adjusted according to the real-time changes of cancer cells during the treatment process. This efficient and flexible optimization method provides a new idea and solution for cancer treatment.

[0154] Please see attached Figure 4 The present invention provides a method for reverse reprogramming a cancer cell treatment system, comprising the following steps:

[0155] Obtain multi-omics data of cancer cells, including transcriptome, metabolome, and epigenome data;

[0156] Use high-order nonlinear dynamic models to describe changes in cancer cell states and take multi-omics data as input;

[0157] defining an optimal control objective function according to the high-order nonlinear dynamics model and multi-omics data, and deriving an optimal control strategy;

[0158] Use the Quantum Computing Optimization Module to accelerate the solution of optimal control problems through quantum variational optimization algorithms;

[0159] Establish cancer cell regulatory networks through graph neural network modules to capture the relationships between genes and metabolites;

[0160] Use the conditional generative adversarial network module to simulate the migration of cell states and provide data support for optimizing treatment plans:

[0161] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Cancer cell reverse reprogramming treatment system based on multimodal AI digital model, characterized by: include: High-order nonlinear dynamics model module, which is used to describe the high-order nonlinear dynamics model of cancer cell state changes; Multi-omics data acquisition module, used to obtain multi-omics data of cancer cells; An optimal control module, for performing optimal control according to the high-order nonlinear dynamics model and multi-omics data; The quantum computing optimization module is used to implement the optimal control and accelerate the solution of the optimal control problem through quantum computing.

2. The cancer cell reverse reprogramming treatment system based on a multimodal AI digital model according to claim 1, characterized in that: The high-order nonlinear dynamics model module uses ordinary differential equations to represent the changes in cancer cell states over time. The model includes: Gene expression equation, which describes the changes in gene expression of cancer cells over time; Metabolite concentration equation, which describes the changes in metabolite concentrations within cancer cells; Epigenetic equations that describe the effects of epigenetic modifications on the state of cancer cells.

3. The cancer cell reverse reprogramming treatment system based on multimodal AI digital model according to claim 1, characterized in that: The multi-omics data acquisition module includes: A transcriptome data collection unit, used to obtain gene expression information of cancer cells, wherein the information is collected by high-throughput RNA sequencing technology; A metabolomics data acquisition unit, used to obtain the metabolite concentration of cancer cells, wherein the concentration information is measured by mass spectrometry or nuclear magnetic resonance technology; The epigenetic data acquisition unit is used to obtain epigenetic modification data of cancer cells, and the data is analyzed by chromatin immunoprecipitation sequencing technology or methylation chip technology.

4. The cancer cell reverse reprogramming treatment system based on multimodal AI digital model according to claim 1, characterized in that: The optimal control module includes: an objective function definition unit, which is used to define an optimal control objective function. The objective function calculates and optimizes the control strategy by minimizing the gap between the cancer cell state and the target health state. The objective function is: in, is the high-order derivative of the cell state, representing the dynamic evolution of cancer cells, is the dynamic evolution of the target health state, u ( t ) is the control strategy, u target ( t ) is the target control strategy, λ and γ are the weights of the control cost term and the target control strategy; a control strategy derivation unit is used to derive the optimal control strategy through variational method and Lagrange multiplier method according to the objective function and constraints.

5. The cancer cell reverse reprogramming treatment system based on multimodal AI digital model according to claim 1, characterized in that: The quantum computing optimization module includes: A quantum variational optimization unit, which is used to accelerate the solution of optimal control problems. The quantum variational optimization algorithm represents different control strategies through the superposition state of quantum bits, uses quantum gate operations for parallel calculations, and quickly searches for the optimal solution in a high-dimensional state space; The quantum measurement and feedback unit is used to adjust and optimize the control strategy according to the results of quantum calculation, and feed back the optimization results to the optimal control module.

6. The cancer cell reverse reprogramming treatment system based on multimodal AI digital model according to claim 1, characterized in that: The quantum computing optimization module includes: The quantum measurement and feedback unit is used to optimize the control strategy according to the quantum calculation results, and feed back the optimal control strategy to the optimal control module to adjust the intervention path.

7. The cancer cell reverse reprogramming treatment system based on multimodal AI digital model according to claim 1, characterized in that: The system comprises: The graph neural network module is used to construct the gene and metabolite regulatory network of cancer cells through the relationship between nodes and edges, capture the regulatory mechanism of cell state changes, and identify key regulatory factors; The graph convolutional network unit is used to update node features through graph convolution operations and model the regulatory relationship between genes and metabolites using the adjacency matrix and weight matrix.

8. The cancer cell reverse reprogramming treatment system based on multimodal AI digital model according to claim 7 is characterized in that: The graph neural network module updates node features through a graph convolutional network and models the interaction between genes and metabolites by combining the adjacency matrix and the weight matrix.

9. The cancer cell reverse reprogramming treatment system based on multimodal AI digital model according to claim 1, characterized in that: The system comprises: The conditional generative adversarial network module is used to simulate the state migration of cancer cells under different intervention conditions, generate cell state predictions under intervention, and provide feedback data for the optimal control module.

10. A method for a cancer cell reverse reprogramming treatment system, applied to the cancer cell reverse reprogramming treatment system based on a multimodal AI digital model as described in claims 1-9, characterized in that: The following steps are involved: Obtain multi-omics data of cancer cells, including transcriptome, metabolome, and epigenome data; Use high-order nonlinear dynamic models to describe changes in cancer cell states and take multi-omics data as input; defining an optimal control objective function according to the high-order nonlinear dynamics model and multi-omics data, and deriving an optimal control strategy; Use the Quantum Computing Optimization Module to accelerate the solution of optimal control problems through quantum variational optimization algorithms; Establish cancer cell regulatory networks through graph neural network modules to capture the relationships between genes and metabolites; A conditional generative adversarial network module is used to simulate the migration of cell states and provide data support for optimizing treatment plans.

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