WO2023231203A1 - Procédé et appareil de prédiction d'efficacité de médicament basés sur un modèle de cellule numérique, support et dispositif - Google Patents

Procédé et appareil de prédiction d'efficacité de médicament basés sur un modèle de cellule numérique, support et dispositif Download PDF

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WO2023231203A1
WO2023231203A1 PCT/CN2022/115815 CN2022115815W WO2023231203A1 WO 2023231203 A1 WO2023231203 A1 WO 2023231203A1 CN 2022115815 W CN2022115815 W CN 2022115815W WO 2023231203 A1 WO2023231203 A1 WO 2023231203A1
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digital
biochemical
cell model
drug
mutant
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PCT/CN2022/115815
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Chinese (zh)
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WO2023231203A9 (fr
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李国亮
姜树嘉
李林峰
胡健
闫峻
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医渡云（北京）技术有限公司
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Priority claimed from CN202210613704.0A external-priority patent/CN117198387A/zh
Priority claimed from CN202210613715.9A external-priority patent/CN117198388A/zh
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Publication of WO2023231203A1 publication Critical patent/WO2023231203A1/fr
Publication of WO2023231203A9 publication Critical patent/WO2023231203A9/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage

Definitions

  • Embodiments of the present disclosure relate to the technical field of digital cell models, specifically, to a method for constructing a digital mutant cell model and its device, a drug efficacy prediction method and its device, storage media, electronic equipment, and digital cell systems.
  • the purpose of this disclosure is to overcome the above-mentioned shortcomings of the prior art and provide a method for constructing a digital mutant cell model and its device, a drug efficacy prediction method and its device, storage media, electronic equipment, and a digital cell system.
  • a method for constructing a digital mutant cell model including:
  • the pre-built digital normal cell model includes a biochemical component pool and multiple signaling pathway units;
  • the biochemical component pool includes multiple biochemical component information, and the biochemical component information includes the concentration and concentration of biochemical components. /or position information;
  • the signal pathway unit is used to simulate the signal pathway of biological cells;
  • the signal pathway unit includes at least one biochemical reaction module, and the biochemical reaction module is used to simulate the signal pathway unit using a set of biochemical process equations biochemical processes that occur.
  • a device for constructing a digital mutant cell model configured to obtain a digital mutant cell model based on multi-omics data of mutant cells and a pre-constructed digital normal cell model; wherein, the The pre-built digital normal cell model includes a biochemical component pool and multiple signaling pathway units; the biochemical component pool includes multiple biochemical component information, and the biochemical component information includes concentration and/or location information of biochemical components. ;
  • the signal pathway unit is used to simulate the signal pathway of biological cells; the signal pathway unit includes at least one biochemical reaction module, and the biochemical reaction module is used to simulate the biochemical process occurring in the signal pathway unit using a set of biochemical process equations .
  • a drug efficacy prediction method including:
  • the pre-constructed digital normal cell model is simulated iteratively until it reaches a steady state, and the cell phenotypic index of the digital normal cell model in the steady state is obtained as the normal cell phenotypic index;
  • the digital mutant cell model performs iterative simulation until a steady state is reached, and the cell phenotypic index of the digital mutant cell model in the steady state is obtained as the mutant cell phenotypic index;
  • the mutated cell model subjected to digital drug intervention is simulated iteratively until it reaches a steady state, and the post-intervention cell phenotype index of the mutated cell model subjected to digital drug intervention in the steady state state is obtained;
  • a device for predicting drug efficacy including:
  • a mutation model module configured to construct a digital mutant cell model based on the pre-built digital normal cell model and the multi-omics data of the mutant cells
  • the normal index module is configured to cause the digital normal cell model to iteratively simulate until it reaches a steady state, and obtain the cell phenotypic index of the digital normal cell model in the steady state as the normal cell phenotypic index;
  • the mutation index module is configured to make the digital mutant cell model perform iterative simulation until it reaches a steady state, and obtain the cell phenotypic index of the digital mutant cell model in the steady state as the mutant cell phenotypic index;
  • the drug intervention module is configured to construct a mutated cell model for digital drug intervention based on the digital mutated cell model and drug information in the steady state;
  • the intervention index module is configured to enable the digital drug-intervention mutant cell model to iteratively simulate until it reaches a steady-state state, and obtain the post-intervention cell phenotype index of the digital drug-intervention mutant cell model in the steady-state state;
  • the efficacy prediction module is configured to obtain drug efficacy prediction results based on the normal cell phenotypic index, the mutant cell phenotypic index, and the post-intervention cell phenotypic index.
  • a computer-readable storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, the above-mentioned method for constructing a digital mutant cell model is implemented, or the above-mentioned method for constructing a digital mutant cell model is implemented. methods for predicting drug efficacy.
  • a digital cell system including the above-mentioned device for constructing a digital mutant cell model, and further including:
  • Digital Cell Engine used to run digital cell models
  • the drug efficacy analysis engine is used to predict drug efficacy based on the running results of the digital cell engine.
  • Figure 1 schematically shows a flow chart of a method for constructing a digital cell model according to an exemplary embodiment of the present disclosure.
  • Figure 2 schematically illustrates an operational logic architecture diagram of a digital cell model according to an example embodiment of the present disclosure.
  • FIG. 3 schematically shows a flow chart of a method for constructing a digital cell model according to an exemplary embodiment of the present disclosure.
  • Figure 4 schematically shows a flow chart of a method for constructing a cell phenotype index according to an exemplary embodiment of the present disclosure.
  • Figure 5 schematically illustrates a biochemical process definition information according to an example embodiment of the present disclosure.
  • FIG. 6 schematically shows a schematic diagram of obtaining biochemical component information according to a machine learning algorithm according to an example embodiment of the present disclosure.
  • Figure 7 schematically illustrates a schematic diagram of obtaining a mathematical model of a biochemical process according to a machine learning algorithm according to an example embodiment of the present disclosure.
  • Figure 8 schematically shows a structural diagram of a device for acquiring a digital mutant cell model according to an exemplary embodiment of the present disclosure.
  • Figure 9 schematically shows a flow chart of a method for predicting drug efficacy according to an exemplary embodiment of the present disclosure.
  • Figure 10 schematically shows a structural diagram of a drug efficacy prediction method device according to an exemplary embodiment of the present disclosure.
  • Figure 11 schematically shows a structural diagram of a digital cell system according to an example embodiment of the present disclosure.
  • FIG. 12 schematically shows a structural diagram of an electronic device according to an example embodiment of the present disclosure.
  • Example embodiments will now be described more fully with reference to the accompanying drawings.
  • Example embodiments may, however, be embodied in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concepts of the example embodiments.
  • the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments.
  • numerous specific details are provided to provide a thorough understanding of embodiments of the disclosure.
  • those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details described, or other methods, components, devices, steps, etc. may be adopted.
  • well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the disclosure.
  • Embodiments of the present disclosure provide a method for constructing a digital mutant cell model, which method includes:
  • the pre-built digital normal cell model includes a biochemical component pool and multiple signaling pathway units;
  • the biochemical component pool includes multiple biochemical component information, and the biochemical component information includes the concentration and concentration of biochemical components. /or position information;
  • the signal pathway unit is used to simulate the signal pathway of biological cells;
  • the signal pathway unit includes at least one biochemical reaction module, and the biochemical reaction module is used to simulate the signal pathway unit using a set of biochemical process equations biochemical processes that occur.
  • the pre-constructed digital normal cell model can be adjusted based on multi-omics data of mutant cells.
  • the adjusted digital normal cell model as a digital mutant cell model, can simulate the biochemical process of mutant cells and can then be used to study the occurrence, development and treatment of diseases (such as tumors).
  • the pre-built digital normal cell model is a digital cell model
  • the digital cell model can be constructed using the following method:
  • Step S110 construct an initial digital cell model based on biochemical information;
  • the digital cell model includes a biochemical component pool and multiple signal pathway units;
  • the biochemical component pool includes multiple biochemical component information, and the biochemical component
  • the information includes concentration and/or location information of biochemical components;
  • the signal pathway unit is used to simulate the signal pathway of biological cells;
  • the signal pathway unit includes at least one biochemical reaction module, and the biochemical reaction module is used to utilize biochemical process equations
  • the group simulates the biochemical processes occurring in the signaling pathway unit;
  • Step S120 perform iterative simulation on the initial digital cell model to simulate the biochemical processes occurring in biological cells
  • Step S130 After the digital cell model reaches a steady state, determine a target digital cell model based on the current initial digital cell model.
  • the digital cell model further includes a gene expression unit, which is used to simulate gene expression of biological cells; the gene expression unit includes at least one gene expression module, and the gene expression module It is used to simulate the gene expression occurring in the gene expression unit using a set of biochemical process equations.
  • the operation mode and sequence of the gene expression unit are basically the same as those of the signaling pathway unit. In other words, except for the different biochemical processes they simulate, the two are consistent at the computational level. Therefore, the calculation order, calculation method, update method, construction method, etc. of the signal pathway unit can also be applied to the gene expression unit. Therefore, in this disclosure, the signal pathway unit is only used as an example to introduce and explain the structure, execution rules, etc. of the digital cell model.
  • biochemical component information refers to the information of each biochemical component participating in the biochemical process. These biochemical components can serve as reaction substrates, reaction products, catalysts, carriers, or participate in biochemical processes in other roles during the biochemical process. process. From a chemical perspective, the types of biochemical component information can include but are not limited to monomeric proteins, multimeric proteins, variant proteins, glycoproteins, polypeptides, amino acids, DNA, RNA, nucleotides, polysaccharides and other types that participate in biochemical processes. components. In embodiments of the present disclosure, the biochemical component information at least includes the concentration of the biochemical component. In some embodiments, the biochemical component information may also include location information of the biochemical component.
  • different biochemical components can be assigned different IDs or names, such as different numbers, and the IDs or names assigned to the biochemical components are used to distinguish different biochemical components.
  • the ID or name of the biochemical component can also be added to the biochemical component information.
  • the same biochemical component can play different roles in different biochemical processes.
  • a protein in an enzymatic reaction process (an exemplary biochemical reaction process) can serve as an enzyme in the enzymatic reaction process; in a protein synthesis process (another exemplary biochemical reaction process), the above Proteins, which are enzymes, may be synthesized in this process and thus serve as reaction products in the protein synthesis process.
  • the same substance (such as a substance with a chemical structure) can be separated due to its location or some differences (such as differences in charge characteristics, differences in dissociation levels, differences in spatial characteristics, etc.)
  • two different biochemical components for example, two different numbers are assigned respectively.
  • at least two biochemical components are the same chemical substance and are located at different locations within the cell.
  • the synthesized protein in a protein synthesis process, can be used as a biochemical component; in a protein targeted delivery process, the synthesized protein can be transported to a specific location to perform its function; in the In one example, the protein that is synthesized (that is, the protein before it is targeted for delivery) can be regarded as one biochemical component, and the protein that is targeted for delivery to a specific location can be regarded as another biochemical component.
  • the protein that is synthesized that is, the protein before it is targeted for delivery
  • the protein that is targeted for delivery to a specific location can be regarded as another biochemical component.
  • the concentration of each biochemical component is recorded in the biochemical component pool; the concentration of the biochemical component can be a mass concentration (such as g/mL, ⁇ g/mL, etc.), a molar concentration (such as ⁇ mol/L , mol/L, etc.) or use other units to characterize concentration.
  • the concentration of the biochemical component may be expressed in ⁇ mol/L.
  • a biochemical process refers to a change process of a biochemical component, which may be a change process of the component (chemical substance) itself (for example, changing from one chemical substance to another chemical substance) , can also refer to the process of changing the spatial distribution of components (such as the transport process of chemical substances within cells), or other processes that lead to changes in the type, distribution, function, etc. of chemical substances.
  • the biochemical process at least includes a biochemical reaction process and a biochemical transport process.
  • Biochemical processes result in changes in the concentrations of at least some of the biochemical components involved.
  • biochemical components as reaction substrates can be consumed, and biochemical components as reaction products can be generated; when only considering the biochemical reaction process, the biochemical process will cause the reaction to The concentration of the substrate decreases causing the concentration of the reaction products to increase.
  • the biochemical components before transport can be consumed during the biochemical transport process, and the biochemical components after transport can be generated during the biochemical transport process; when only considering the biochemical transport process, This biochemical process will cause the concentration of biochemical components before transport to decrease and the concentration of biochemical components after transport to increase.
  • a biochemical reaction module for simulating the biochemical process can be constructed according to the biochemical process, and the biochemical reaction module can include a set of biochemical process equations.
  • the biochemical reaction module is used to simulate the biochemical process occurring in the signaling pathway unit using a set of biochemical process equations.
  • a set of biochemical process equations can be used to simulate changes in the concentration of a biochemical component after a time step caused by a biochemical process.
  • the same biochemical process equation set can include multiple concentration change functions. Each concentration change function is used to describe the concentration change of a biochemical component in the biochemical process. The concentration change of each biochemical component in the biochemical process is represented by a represented by the concentration change function.
  • the concentration change function of each biochemical component in the biochemical process equation set and the relationship between the concentration change functions can reflect the role of each biochemical component in the biochemical process (such as reaction substrate, reaction product, enzyme etc.), mass conservation in biochemical processes, signal dependence in biochemical processes, etc.
  • the biochemical process equation set to simulate the biochemical process at least includes:
  • c(cat) represents the concentration of the enzyme in the biochemical process; the subsequent solution is only performed when the concentration of the enzyme is not zero, which reflects the dependence of the biochemical process on the enzyme; Before and after the biochemical process, the concentration change of c(cat) is 0, which means that the enzyme plays a catalytic role in the biochemical process without itself being consumed.
  • ⁇ c(Ant) represents the concentration change of the reaction substrate concentration after a time step t during the biochemical process.
  • ⁇ c (Product) represents the concentration change of the concentration of the reaction product in the biochemical process after a time step t
  • v (Product) represents the change rate of the concentration of the reaction product in the biochemical process
  • t represents a time step.
  • each signaling pathway is related to form an intracellular signaling network.
  • each required signal pathway can be obtained by searching medical literature, biological literature, performing link prediction, or other methods; and then constructing a signal pathway unit for describing the signal pathway.
  • the signaling pathway includes one or more biochemical processes.
  • a set of biochemical process equations that describe a biochemical process can be used to construct a signaling pathway unit that describes a signaling pathway.
  • each biochemical process in the signaling pathway described by the signaling pathway unit and the biochemical components involved in each biochemical process can be obtained.
  • the signaling pathway unit can be divided into at least a signal transduction module, a protein transport module, a cell cycle module, and a programmed cell death module.
  • the gene expression unit may include a gene expression module.
  • the signaling pathway unit is used to simulate at least one of intracellular signaling, protein transport, cell cycle, and programmed cell death, or to simulate intracellular gene expression (gene expression unit). It can be understood that the signaling pathway unit of the embodiment of the present disclosure can also be used to simulate signaling processes or biological processes in other biological cells.
  • the biochemical reaction module in the signaling pathway unit simulates processes such as intracellular gene expression or protein transport
  • the mathematical paradigm used may be different from simulating processes such as signal transduction, programmed cell death, and cell cycle.
  • a visualization engine can be constructed.
  • the visualization engine analyzes each biochemical reaction module in each signal pathway unit to obtain the relationship between each biochemical process, and obtain each biochemical process as a biochemical process. Relationships between processes and biochemical components as component nodes. By outputting or displaying these relationships, the intracellular signaling network simulated by the digital cell model can be output or displayed.
  • the relationship between the process nodes and the relationship between the process nodes and the component nodes in the cells simulated by the digital cell model can be presented in the form of a graph according to the visualization engine, especially can be presented as a cell signaling network diagram.
  • the visualization engine can also dynamically present the changing status of at least some of the biochemical components.
  • a component node i.e., a biochemical component
  • a component node can be displayed in different colors when the concentration increases or decreases; for example, if the concentration of a component node increases, the node in the cell signaling network diagram will be displayed in different colors. Component nodes appear in red; if the concentration of a component node decreases, the component node appears in green in the cell signaling network diagram.
  • the component node can be displayed as a different volume according to the increase or decrease in concentration of the component node (ie, a biochemical component).
  • the greater the change in the concentration of a component node the larger the volume of the point presented by the component node; the smaller the change in concentration of the component node, the smaller the volume of the point presented by the component node.
  • concentration of the component node decreases slightly during the iteration.
  • the visualization engine can also present the changes in the concentration of single or multiple biochemical components during the iterative simulation process, such as displaying the real-time concentration of the selected biochemical component, or drawing the concentration change curve of the selected biochemical component, etc. .
  • part of the signal pathways simulated by the digital cell model are parallel, that is, there is no obvious sequence or time dependence between the two.
  • these parallel signal paths can be treated as a signal path group.
  • the signaling pathway units describing these parallel signaling pathways are regarded as parallel signaling pathway units in the digital cell model and are labeled with the same unit process sequence.
  • these signal path units with the same unit process sequence can be regarded as a signal path unit group, and signal path unit groups with different unit process sequences can be cascaded in sequence.
  • each signaling pathway unit can be marked with a process sequence, and a set of biochemical process equations with the same biochemical process sequence can form a signaling pathway unit group.
  • the digital cell model can logically include multiple cascaded signal pathway unit groups, namely signal pathway unit group A, signal pathway unit group B... signal pathway unit Group M.
  • the signal path unit group M is used to represent the last stage signal path unit group.
  • each signal pathway unit group is solved in sequence to simulate the overall orderliness and directionality of signal transmission in the biochemical processes of biological cells.
  • the signal path unit group A in this example has one signal path unit, including, for example, a signal path unit a1, a signal path unit a2, and a signal path unit a3.
  • this exemplary signal path unit group B has one signal path unit, that is, signal path unit b1.
  • the signal path units may be out of order.
  • the order in which individual signal path elements are solved can be randomly determined. In this way, the parallel nature of biochemical processes in biological cells can be simulated.
  • each signal path unit group ie, each signal path unit
  • each signal path unit is executed once as a simulation of the digital cell model, and the results after each simulation are used as the basis for the next simulation.
  • each signal path unit can be solved sequentially according to the process sequence.
  • the digital cell model includes a large number of biochemical process equations, these biochemical process equations are not solved at the same time, but are solved sequentially according to the process order of the signal pathway unit.
  • each signal pathway unit is executed in unit process order.
  • the biochemical reaction modules in the signaling pathway unit have a modular process sequence. When any one of the signaling pathway units is executed, each biochemical reaction module in the signaling pathway unit is executed in accordance with the module process sequence.
  • the biochemical process equation set in the biochemical reaction module can be solved.
  • the biochemical process equation set references the current concentration of the biochemical component in the biochemical component pool; after completing any of the biochemical reaction modules, the biochemical process equation set refers to the current concentration of the biochemical component in the biochemical component pool.
  • the information of each biochemical component in the biochemical component pool is updated according to the solution results, especially the concentration of each biochemical component is updated.
  • each signal path unit when solving signal path units, if multiple signal path units have the same unit process order, these signal path units may be solved in parallel as a whole. Specifically, in one example, in each simulation, the execution order among the signal path units with the same unit process order is random; in a large number of iterations, each signal path unit can be regarded as parallel of. This can simulate the parallel characteristics between signal pathway units in the same signal pathway unit group and improve the robustness of the digital cell model.
  • At least one signaling pathway unit includes a plurality of signaling pathway subunits, and any one of the signaling pathway subunits includes a biochemical reaction module or a plurality of biochemical reaction modules with a process sequence.
  • the solution order of the multiple signal path sub-units can be randomly determined, and the multiple signal path sub-units can be solved sequentially according to the solution order.
  • each signal pathway sub-unit can simulate a part of the signal pathway, and this part of the signal pathway can include a biochemical process, or It includes multiple biochemical processes cascaded in sequence, and each biochemical process is simulated through a set of biochemical process equations.
  • each signal path subunit may also have a lower-level submodule.
  • each biochemical process shares the biochemical component pool, that is, the biochemical process equation set corresponding to each biochemical process needs to be solved based on the biochemical component pool and the solution results need to be reflected in the biochemical component pool.
  • concentration of each biochemical component in the biochemical component pool needs to be used; then, the biochemical groups involved in the biochemical process equation set in the biochemical component pool need to be updated based on the calculation results. concentration; this can simulate the consumption, increase, and dependence of biochemical processes on surrounding biochemical components.
  • the solution results and the biochemical component pool before updating can be used to determine the update The final pool of biochemical components.
  • the biochemical component pool is updated according to the solution results of each biochemical process equation set to simulate the dynamic change process of the biochemical components of the digital cell model during cell activities.
  • the data of the biochemical component pool at a specific stage of each simulation can be recorded, for example, the data of the biochemical component pool after the end of each simulation (ie, the concentration of each biochemical component).
  • these data can be used as historical data of the biochemical component pool for evaluation of the iterative simulation process of the digital cell model.
  • the historical data of the biochemical component pool also includes the number of times each data was generated during the simulation process.
  • the historical data of the biochemical component pool may include the simulation times of the digital cell model and the biochemical component pool data corresponding to the simulation times.
  • the digital cell model can be evaluated during the iterative simulation process of the digital cell model to determine whether the digital cell model has reached a steady state during the iterative simulation process.
  • the initial digital cell model can be updated and the iterative simulation and evaluation performed again; this cycle continues until the digital cell model is evaluated during the iterative simulation process is a steady state.
  • the digital cell model was evaluated as a steady state during the iterative simulation process, indicating that the current initial digital cell model has been able to reflect some cell activity patterns of biological cells during the iterative simulation.
  • the current initial digital cell model has The limited initial biochemical component pool and each signaling pathway unit can effectively simulate the biochemical component distribution and cell activity process rules of biological cells to a certain extent.
  • the target digital cell model determined based on the current initial digital cell model can more effectively simulate at least part of the cellular activities of biological cells.
  • the construction method of the digital cell model of the present disclosure it is possible to judge whether the biochemical component pool and each signaling pathway unit of the initial digital cell model are suitable based on the evaluation results of the initial digital cell model during the iterative simulation process; when When the initial digital cell model is evaluated as a failure during the iterative simulation process, it can be judged that the current initial digital cell model is inappropriate for the types and concentrations of biochemical components, the simulation of various biochemical processes, etc., and then it can be The initial digital cell model is reacquired by changing at least one of the biochemical component pool and the signal pathway unit, that is, the initial digital cell model is updated by updating at least one of the biochemical component pool and the signal pathway unit.
  • the digital cell model construction method of the present disclosure can overcome the current lack of accurate cell biology knowledge and determine more appropriate biochemical groups through the cyclic process of updating the initial digital cell model - iterative simulation and process evaluation. Separate cells and signal pathway units to obtain a digital cell model that can achieve a steady state to achieve effective simulation of at least part of the cell activities.
  • the biochemical information based on it includes at least one of signaling pathway information, protein network information, gene network information, biomarker information, and information related to biochemical processes. one.
  • digital cell models can also be constructed based on more biochemical information. Further, at least part of this biochemical information is obtained through knowledge extraction from the public literature.
  • the initial digital cell model is updated when the digital cell model reaches a failure state.
  • the initial biochemical component pool and at least one of the multiple signal pathway units can be updated, so that the biochemical component pool and multiple signal pathway units of the new initial digital cell model are in The overall difference from the initial digital cell model before the update shall prevail.
  • the initial digital cell model can be updated by updating the initial biochemical component pool (the biochemical component pool of the initial digital cell model before any iterative simulation is performed), For example, you can add one or more new biochemical component information to the initial biochemical component pool before the update, or delete one or more biochemical component information from the initial biochemical component pool before the update, or change the original biochemical component information.
  • a new initial biochemical component pool can also be generated (that is, the initial biochemical component pool is regenerated), and the new initial biochemical component pool is determined to be the same as the one before the update. After the initial biochemical component pool is different, use the new initial biochemical component pool to replace the initial biochemical component pool before updating.
  • the initial digital cell model can be updated by updating one or more signaling pathway units. For example, you can adjust the signal path unit to which the signal path unit belongs, add a new signal path unit to the current signal path unit, delete the signal path unit from the current signal path unit, and update the current signal path unit. At least one of the following strategies: the type or number of biochemical process equation sets in the pathway unit, updating the values of constants of any biochemical process equation set in the current signaling pathway unit, and other strategies.
  • a new signal path unit can also be generated (that is, the signal path unit is regenerated), and after it is determined that the new signal path unit is different from the signal path unit before the update, the new signal path unit is used.
  • the signal path unit replaces the signal path unit before the update.
  • each time the initial digital cell model is updated the strategy of updating the initial pool of biochemical components can be adopted alone, or the strategy of updating one or more signaling pathway units can be adopted alone, or the updating strategy can be adopted at the same time.
  • Strategies for initial pooling of biochemical components and strategies for updating one or more signaling pathway units are used to reacquire the initial digital cell model any two times.
  • the strategy of changing the concentration of biochemical components can be used alone to update the initial digital cell model; in the subsequent update of the initial digital cell model, the strategy of adding There are four strategies to update the initial digital cell model: new biochemical component information, deletion of biochemical component information, addition of new biochemical process equation set, and deletion of biochemical process equation set.
  • biochemical component information is added to the biochemical component pool, it is often necessary to add a set of biochemical process equations related to the newly added biochemical component information, which will lead to the adjustment of at least one signaling pathway unit.
  • biochemical process equation set related to the deleted biochemical component information, which will lead to the adjustment of at least one signaling pathway unit.
  • the strategy used each time the initial digital cell model is updated can be determined according to preset rules, such as using the same strategy multiple times in a row, or looping according to a preset strategy sequence.
  • the sequence records the strategy used each time the initial digital cell model is updated within a strategy cycle.
  • the method used to update the initial digital cell model can be determined based on the reasons why the digital cell model is evaluated as a failure state during the iterative simulation process or the specific status of the digital cell model when it is evaluated as a failure state. For example, an expert system can be introduced to improve the accuracy of updating the initial digital cell model, or technical experts can intervene to provide a more appropriate update strategy. Of course, in other embodiments of the present disclosure, other methods can be used to determine the strategy used each time the initial digital cell model is updated, so as to enable the initial digital cell model to be updated.
  • a parameter adjustment module and a structure adjustment module can be constructed, and the current initial digital cell model can be adjusted with the help of the parameter adjustment module and the structure adjustment module to form a new initial digital cell model.
  • the biochemical component concentration in the biochemical component information and the values of the constants of the biochemical process equations can be adjusted through the parameter adjustment module; the biochemical component information in the biochemical component pool can be increased or decreased through the structure adjustment module. , adjust the biochemical process equation set included in the signal pathway unit or adjust the signal pathway unit included in the signal pathway unit.
  • the parameter adjustment module will not cause changes in the types and quantities of biochemical components in the digital cell model, nor will it cause changes in the types, quantities, and positions of the biochemical process equations in each signaling pathway unit. It only changes the Adjustment of concentrations of biochemical components or parameters of biochemical process equations.
  • This adjustment method can generally have a smaller adjustment range and better adjustment accuracy, which is conducive to fine-tuning the digital cell model.
  • the structure adjustment module will adjust the structural characteristics of the digital cell model such as the type and quantity of biochemical components in the biochemical component pool, the type, quantity and location of the biochemical process equations in the signaling pathway unit. Through this adjustment method, the architecture of the digital cell model can be changed to a large extent, which is conducive to obtaining the possible feasible architecture of the digital cell model through rough adjustment.
  • a structure adjustment module may be first used to update the initial digital cell model until a feasible architecture of a digital cell model with good prospects is obtained. Then, the initial digital cell model after structural adjustment is updated through the parameter adjustment module to obtain a digital cell model that can simulate some functions and processes of biochemical cells under this feasible architecture.
  • the structure adjustment module and the structure adjustment module can also be used alternately to update the initial digital cell model.
  • the current initial digital cell model is updated based on at least one of the following information:
  • Information derived from medical literature information derived from biological literature, information derived from high-throughput cell experiments, information derived from high-throughput sequencing, information predicted based on literature information, experimental information or sequencing information.
  • key parameters required for constructing a digital cell model can be determined through searching and analyzing published documents. At least some of these key parameters, such as parameters that are easily verified or obtained through high-throughput cell experiments or high-throughput sequencing technology, or parameters that have large differences in different literatures, should be verified through experiments or through The experiments are obtained by themselves to ensure the accuracy and effectiveness of these key parameters, thereby improving the acquisition efficiency of the digital cell model and improving the closeness of the digital cell model to biological cell simulation.
  • the method for constructing a digital cell model may further include: obtaining the biochemical component pool of the initial digital cell model (i.e., the initial biochemical component pool) according to the biochemical component database. ); the biochemical component database includes a plurality of biochemical component setting information, and the biochemical component setting information includes the concentration range of the biochemical component.
  • multiple biochemical component setting information can be selected from the biochemical component database, and the biochemical component information is determined based on the selected biochemical component setting information, and each biochemical component information forms an initial biochemical group. Divide the pool.
  • the biochemical component setting information includes a concentration range of the biochemical component
  • the biochemical component information includes the concentration of the same biochemical component
  • the concentration in the biochemical component information is within the concentration range of the biochemical component setting information.
  • the types of biochemical components involved in the initial biochemical component pool do not exceed the types of biochemical components in the biochemical component database
  • the concentration of biochemical components in the initial biochemical component pool is within the range of the biochemical components in the biochemical component database. within the concentration range of the component.
  • the biochemical component database can be used as the basis and restriction for generating the initial biochemical component pool; of course, it can also be used as the basis and restriction for updating the initial biochemical component pool.
  • the biochemical component database includes N1 (N1 is a positive integer) biochemical component setting information, that is, it involves N1 biochemical components.
  • N1 is a positive integer
  • biochemical component setting information can be extracted from the biochemical component database and based on the N2 biochemical components
  • the corresponding N2 biochemical component information is generated based on the setting information, and the concentration of the biochemical component of each biochemical component information is within the concentration range of the biochemical component of the corresponding biochemical component setting information; the N2 biochemical component information
  • An initial pool of biochemical components can be formed.
  • the biochemical component database may also be acquired first.
  • the existing biochemical component database can be directly obtained, the required biochemical component database can also be obtained by modifying and supplementing the existing database, or the biochemical component database can be constructed from scratch .
  • a biochemical component database can be constructed from scratch.
  • the data used to construct the biochemical component database can be at least partially derived from existing databases and published documents in the biological field (such as the field of cell biology). (such as journals, newspapers, conference proceedings, monographs, etc.), obtained through biological experiments and research, and in some cases, targeted cell biology research (such as high-throughput cell experiments) has been obtained.
  • Data which may include components in biological cells and the concentrations of these components. Of course, it is understandable that there may be differences in the components and their concentrations in biological cells based on data from different sources.
  • biochemical component database at least some of the biochemical components and their related data can be found from existing databases and/or published documents in the biological field, and these data can be corrected or not corrected to form a biochemical composition. Set information and add it to the biochemical component database.
  • the concentration of a specific biochemical component if multiple data sources provide the concentration of a specific biochemical component and the concentrations of biochemical components from different data sources are relatively close, for example, the biochemical components from each data source If the concentrations of the components all fluctuate within the same order of magnitude, it can be considered that the concentration of the specific biochemical component has a high degree of certainty, and then the concentration range of the biochemical component is formed into the biochemical component setting information without correction and Added to the biochemical component database.
  • biochemical component setting information of the new biochemical component can be added to the biochemical component database; in this way, it can be continuously improved through
  • the method of biochemical component database provides a knowledge base for the construction of more accurate digital cell models, allowing the constructed digital cell models to more effectively simulate the biochemical processes of biological cells.
  • a machine learning algorithm can be used to predict the concentration of some biochemical components, and biochemical component setting information is formed based on the prediction results and added to the biochemical component database. For example, when the concentration of a specific biochemical component is difficult to directly measure, a biochemical network model related to the biochemical component can be constructed based on existing data, and then a machine learning algorithm can be used to predict the concentration or concentration range of the biochemical component.
  • the biochemical component At in order to calculate the concentration of a specific biochemical component At, can be used as a node to construct a biochemical network model.
  • the biochemical network model also includes node A1, node A2, node A3 and node A4. ; Node A1, node A2, and node A3 represent the biochemical process that generates the specific biochemical component At, and node A4 represents the biochemical process in which the specific biochemical component At participates.
  • new biochemical components and new biochemical processes can be predicted based on known biochemical networks with the help of link prediction techniques.
  • concentration ranges of these predicted biochemical components can be directly verified (such as biological verification, especially through high-throughput cell experiments) or indirectly verified (such as through different biochemical network models) to form a biochemical component design.
  • the information is determined and added to the biochemical component database, thereby improving the biochemical component database, so that the digital cell model built based on the biochemical component database can be closer to real cells.
  • a database parser can be constructed, and the database parser can parse existing data materials to generate biochemical component setting information, for example, generate biochemical component setting information based on an existing database. Or generate biochemical component setting information based on published literature. Further, at least part of the biochemical component setting information constructed by the database parser can be used as the initial biochemical component setting information; the initial biochemical component setting information can be considered as modified or corrected in other ways, as the final available biochemical component setting information. The biochemical component setting information is used to build or update the initial biochemical component pool.
  • the database parser can analyze existing literature to obtain the concentration of a specific biochemical component in different literatures, and collect these concentrations and related information from the literature as the initial biochemical component setting information.
  • Technical experts can make corrections to the concentrations involved in the initial biochemical component setting information, such as deleting some obviously erroneous concentrations or setting a concentration range based on the collected concentrations, to obtain the final usable biochemical component setting information.
  • the current initial biochemical component pool of the initial digital cell model is updated according to the biochemical component database.
  • the type of biochemical component information in the initial biochemical component pool, the concentration of the biochemical component in the biochemical component information can be adjusted according to the biochemical component database, or the biochemical components in the initial biochemical component pool can be adjusted at the same time.
  • the type of information and the concentration of the biochemical component of at least one biochemical component information are adjusted to obtain a new initial biochemical component pool.
  • a new initial biochemical component pool can also be regenerated based on the initial biochemical component pool, and the new initial biochemical component pool can be combined with the initial biochemical component pool before updating.
  • the initial biochemical component pool before updating is replaced with the new initial biochemical component pool, thereby completing the update of the initial biochemical component pool.
  • the biochemical component setting information may also include the concentration search step size of each biochemical component.
  • the biochemical component setting information at least records the concentration range and concentration search step of each biochemical component.
  • the concentration range of the biochemical component of at least one biochemical component setting information can be multiple discrete concentration values; when updating the biochemical component in the initial biochemical component pool When selecting the biochemical component information in the biochemical component, you can select a new concentration value from the concentration range of the biochemical component setting information of the biochemical component.
  • the concentration of the biochemical component in at least one biochemical component setting information is a specific concentration (ie, a point value)
  • the concentration range of the biochemical component is the specific concentration.
  • changes in the concentration of some biochemical components have little impact on the biochemical processes of cells, or their concentration or content is relatively stable.
  • These biochemical components are in the biochemical component database.
  • the concentration range of the biochemical component can be set to a specific concentration, that is, the concentration range of the biochemical component setting information can be set to a specific concentration, so as to reflect some of the biological laws of biological cells and reduce the difficulty of obtaining digital cell models.
  • each biochemical component setting information in the biochemical component database is also marked with a credibility parameter (such as a credibility level), and the credibility parameter is used to characterize the concentration of the biochemical component. reliability.
  • a credibility parameter such as a credibility level
  • the biochemical component setting information has a larger concentration range
  • the concentration of the biochemical component is less reliable
  • the biochemical component setting information has a smaller concentration range
  • the concentration range of the biochemical component is lower. Concentrations of biochemical components are more reliable.
  • the concentration range of the biochemical component setting information comes from highly credible data, such as from biological experimental data, the credibility of the concentration of the biochemical component is higher; when the biochemical component When the concentration range of the component setting information is derived from less reliable data, such as from non-authoritative journals, newspapers, or only predicted values, the credibility of the concentration of the biochemical component is low.
  • high-confidence biochemical component setting information may be given priority, or at least partially based on the credibility of the biochemical component setting information. Extract biochemical component setting information from the biochemical component database.
  • the biochemical component information corresponding to the low-confidence biochemical component setting information can be adjusted first, for example, deleting these biochemical component information ( At least part of the biochemical component information corresponding to the low-confidence biochemical component setting information) or the biochemical component that changes to at least part of the biochemical component information (corresponding to the low-confidence biochemical component setting information) concentration.
  • the method of constructing a digital cell model further includes generating or updating an initial digital cell model based on the signaling pathway database; specifically, generating or updating the initial digital cell model based on the signaling pathway database. signal pathway unit in .
  • the following method can be used to generate or update the signaling pathway units in the initial digital cell model according to the signaling pathway database:
  • each signaling pathway unit of the initial digital cell model is obtained.
  • the signal pathway database includes a plurality of signal pathway information and a biochemical process paradigm pool;
  • the biochemical process paradigm pool includes a plurality of biochemical process paradigms used to describe biochemical process rules;
  • the signal pathway information includes a pool used to describe the signal pathway
  • the biochemical process information of each biochemical process is marked with the process sequence;
  • each biochemical process information includes the biochemical process paradigm referenced by the biochemical process, the meaning of the variables in the referenced biochemical process paradigm, and the referenced biochemical process The value range of the constants in the paradigm and the order in the signal pathway information;
  • the initial signal pathway information includes multiple signal pathway information obtained from the signal pathway database, and the biochemical processes in the initial signal pathway information Constants in the biochemical process paradigm to which the information refers are limited to point values.
  • each signaling pathway unit in the initial digital cell model can be obtained according to the signaling pathway database.
  • the signaling pathway units in the initial digital cell model may be updated according to the signaling pathway database.
  • each signaling pathway can be constructed based on existing knowledge, such as knowledge about intracellular signaling pathways in biological or medical literature, that is, each signaling pathway information can be constructed; each biochemical process information in the signaling pathway information By referencing the pool of biochemical process paradigms, it can be transformed into the desired signaling pathway unit. According to the process sequence of each signal path information, the combination method of each signal path unit can be determined, and then each signal path unit can be constructed.
  • the biochemical process paradigm has variables (including independent variables and dependent variables) and constants, as well as mathematical descriptions of mathematical relationships between variables and constants.
  • individual variables and constants are not assigned specific meanings, and constants are not assigned values.
  • the biochemical process paradigm is used only to represent mathematical laws.
  • the meaning of the variables in the biochemical process paradigm referenced by the biochemical process information may refer to the biochemical component information referenced by each variable in the biochemical process information, especially The concentration of the biochemical component in the referenced biochemical component information.
  • biochemical process mathematical models can be generated according to the biochemical process paradigm.
  • the biochemical process mathematical model can simulate the biochemical process defined by the biochemical process information.
  • each variable in the biochemical process paradigm can be replaced by the concentration of each biochemical component defined by the biochemical process information, and a constant can be determined as a constant based on the definition of the value range of the constant in the biochemical process information.
  • the biochemical process paradigm pool and signal pathway information in the signal pathway database by setting up a biochemical process paradigm pool and signal pathway information in the signal pathway database, on the one hand, it can simplify the construction of mathematical models of each biochemical process and reduce the complexity of the signal pathway database; on the other hand, the constructed signal
  • the pathway database can meet the needs of both knowledge expression and computational expression.
  • the description of the meaning of the variables in the biochemical process information and the labels or names of the biochemical process information can use abbreviations commonly used in the field or customized expression rules, thereby making the biochemical process information easier to use. Build and be more easily updated or modified.
  • a biochemical process paradigm of type "mm” includes the following two equations:
  • activators.v_max activators.k_cat*activators.concentration;
  • This biochemical process paradigm can reflect a kinase-catalyzed multimerization process, in which activators, substrate, etc. do not represent specific biochemical components, and k_m, k_cat, etc. do not refer to specific constants.
  • Figure 4 illustrates a biochemical process information and the biochemical process mathematical model that can be generated when the biochemical process information calls the biochemical process paradigm represented by "mm".
  • the mathematical model of the biochemical process describes the reaction rate of the multimerization process catalyzed by the kinase, which is specifically reflected in the concentration change rate v_t of the product. According to the reaction rate of the multimerization process catalyzed by the kinase, combined with the time step, the concentration change of each biochemical component involved after a time step can be determined.
  • initial signal pathway information can be generated based on the signal pathway information.
  • the initial signal pathway information includes a plurality of biochemical process information extracted from the signal pathway information, and the constants in the biochemical process information are in the initial signal pathway information. is a point value rather than a range value; the value of the constant in the biochemical process information in the initial signal pathway information satisfies the range in the signal pathway information.
  • the initial signaling pathway information can be transformed into multiple specific biochemical process mathematical models by calling the biochemical process paradigm pool.
  • This initial signaling pathway information is used to construct each signaling pathway unit of the digital cell model. When the initial digital cell model needs to be updated, the signal pathway unit of the digital cell model can be updated by updating the initial signal pathway information.
  • the biochemical process information in the initial signal pathway information can directly generate the biochemical process equation set by calling the biochemical process paradigm in the biochemical process paradigm pool, and determine the biochemical process equation set to which it belongs based on the process order of the initial signal pathway information. Signal path unit.
  • a biochemical process paradigm may include a set of paradigm equations composed of multiple paradigm equations.
  • Each paradigm equation is used to simulate the change of a variable after a time step, for example, simulating the change of a variable after a time step.
  • Increase or decrease the amount can reflect the role played by the variable; generally, when a variable increases after a time step, the variable is the dependent variable, which is expressed in the biochemical process.
  • the concentration of the reaction product; when a variable decreases after a time step, the variable is an independent variable, that is, it represents the concentration of the reaction substrate in the biochemical process; when a variable remains unchanged after a time step, it represents This variable represents components such as enzymes.
  • the biochemical process information also includes the value range of the constants in the biochemical process paradigm.
  • the value range can be a fixed value (that is, the value range is a point value), or Can be a range value.
  • the certainty of the constant in the biochemical process information when the certainty of the constant in the biochemical process information is very high or very clear, it can be a point value; for example, when the constant in the biochemical process information represents the Michaelis-Menten constant, it can be a point value.
  • the certainty of the constant in the biochemical process information is not very high, it can be configured as a range value.
  • At least one biochemical process is determined based on at least one of medical literature, biological literature, high-throughput cell experiments, machine learning algorithms, and link prediction algorithms, and a biochemical process is generated based on the biochemical process
  • the information is added to the signaling pathway information in the signaling pathway database. In this way, by continuously improving the signaling pathway database, the accuracy of digital cell models can be improved.
  • constants in some biochemical processes can be determined through high-throughput biological experimental technology (such as high-throughput cell experimental technology or high-throughput sequencing technology), and the constants in the biochemical process can be used as The basis for determining the value range of constants in biochemical process information.
  • a machine learning algorithm can be used to determine the constants of some biochemical processes, and the value range of the constants of the corresponding biochemical process information can be determined based on the constants of the biochemical process.
  • the specific biochemical process Bt can be used as a node to construct a biochemical network model.
  • the biochemical network model also includes node B1, node B2, node B3 and node B4; node B1, node B2, and node B3 represent the biochemical component information participating in the biochemical process Bt, and node B4 represents the biochemical component information generated by a specific biochemical process Bt.
  • the concentration data of node B1, node B2, node B3 and node B4 can be determined through machine learning.
  • the mathematical model of the biochemical process of Bt is used to determine the constants of the biochemical process based on the mathematical model of the biochemical process.
  • the type and quantity of biochemical components included in the biochemical component pool are consistent with the type and quantity of biochemical component setting information in the biochemical component database; or, in the signaling pathway database,
  • the biochemical process information or signaling pathway information involved is reflected in the digital cell model.
  • This method can maximize the closeness of digital cell models to biological cells.
  • it is understandable that due to limitations in simulation methods and cognitive knowledge of biological cells, it may be difficult for digital cell models to completely simulate the various functions of biological cells at some specific stages. For example, it may be difficult to simulate the actual functions of biological cells. Multiple biochemical processes performed in sequence are simulated as one biochemical process, in order to simulate as much as possible the final results of these processes rather than the specific processes of each biochemical process.
  • the digital cell model is constructed based on the biochemical component database and the signaling pathway database, but it is not pursued that the digital cell model fully and fully embodies the requirements of the biochemical component database and the signaling pathway database. It collects all kinds of knowledge and information, but pursues the simulation of biological cells with a certain accuracy and function. Of course, as more and more knowledge and information are collected in the biochemical component database and signaling pathway database, the description of the intracellular signaling network becomes more and more refined. Versions of digital cell models will simulate biological cells with increasing accuracy. In other words, in the embodiments of the present disclosure, the biochemical component database and signaling pathway database can be continuously improved, and a more complete digital cell model can be constructed using a more complete biochemical component database and signaling pathway database according to needs.
  • the obtained target digital cell model can be saved as a biochemical component pool and each signaling pathway unit to facilitate direct application of the target digital cell model.
  • the target digital cell model can be saved as a model database, which can include a component subdatabase, a process subdatabase, and a paradigm subdatabase.
  • the molecular database is used to save the information of each biochemical component in the biochemical component pool of the target digital cell model.
  • the process sub-database is used to save various parameters of each biochemical process equation set in the signal pathway unit, that is, the biochemical process equation set is saved in the form of biochemical process information.
  • the paradigm sub-database is used to save each biochemical process paradigm involved in each set of biochemical process equations.
  • the PM of the signaling pathway database can be quoted or copied.
  • the biochemical component information in the molecular database and the biochemical process information in the process sub-database can be directly modified, and then based on the modified molecular database, modified process Subdatabases and paradigm subdatabases construct new digital cell models to simulate new cells.
  • the modified molecular database can be used to construct the biochemical component pool of the new digital cell model; the modified process sub-database can be used to construct each signaling pathway unit of the new digital cell model by referencing the paradigm sub-database.
  • parameters of the signal pathway units may be adjusted first to obtain each target signal pathway unit. Then adjust the parameters of the related target signal pathway units as a whole, for example, adjust the parameters of multiple signal pathway units on the same signal path as a related signal pathway model group, and obtain the target signal pathway model group. . Then, an initial digital cell model is obtained based on the target signaling pathway model set.
  • the method for constructing a digital cell model further includes acquiring a plurality of target signal pathway units; when acquiring an initial digital cell model, determining the initial digital cell model based on the multiple target signal pathway units.
  • Signaling pathway units in digital cell models In other words, the parameters of each signal pathway unit can be adjusted first, and then the parameters can be adjusted at the overall level of the digital cell model based on the parameter adjustment results of the signal pathway unit.
  • Obtaining any target signal pathway unit includes:
  • the initial signaling pathway model includes an initial signaling pathway unit and biochemical component information involved in the initial signaling pathway unit, and the biochemical component information includes the concentration of the biochemical component;
  • the screening conditions including the concentration change trend of one or more biochemical components as markers
  • the initial signal pathway model is updated until the filtering conditions are met;
  • the initial signal pathway model is determined to be the target signal pathway model.
  • the method for constructing a digital cell model further includes acquiring multiple target signal pathway model groups; each of the target signal pathway model groups includes multiple signal pathway units; after obtaining the initial digital
  • the signal pathway units in the initial digital cell model are determined based on the signal pathway units in the target signal pathway model group.
  • multiple related (for example, serially connected in signal conduction) signal pathway units can be adjusted as a whole first, and then the overall parameters can be adjusted at the digital cell model level after the parameters are adjusted. In this way, the efficiency of parameter adjustment of the digital cell model can be improved as a whole, and the digital cell model can simulate biological cells more effectively.
  • any target signaling pathway model group including:
  • an initial signal pathway model set which includes a plurality of target signal pathway units and biochemical component information involved in each target signal pathway unit, and the biochemical component information includes the concentration of the biochemical component;
  • the screening conditions including the concentration change trend of one or more biochemical components as markers
  • the initial signal pathway model group is updated until the filtering conditions are met; updating the initial signal pathway model group includes updating the relevant signal pathway model groups. at least one signaling pathway unit;
  • the initial signal pathway model group is determined to be the target signal pathway model group.
  • step S110 after obtaining the initial digital cell model, the legality of the initial digital cell model can also be judged; when the initial digital cell model meets the legality requirements, then Enter step S120 to iteratively simulate the digital cell model.
  • a whitelist strategy or a blacklist strategy can be used to determine the legality of the initial digital cell model.
  • the whitelist strategy refers to setting at least one legality rule in advance; as long as the initial digital cell model satisfies any legality rule, it will be judged that the initial digital cell model meets the legality requirements, otherwise it will be judged that the initial digital cell model does not meet the legality requirements.
  • Legality requirements refers to setting at least one illegality rule in advance; as long as the initial digital cell model satisfies any illegality rule, it will be judged that the initial digital cell model does not meet the legality rules, otherwise it will be judged that the initial digital cell model meets the legality rules. Require.
  • the digital cell model construction method provided by the present disclosure may also include, between step S110 and step S120, after obtaining the initial digital cell model, determining whether the initial digital cell model satisfies any illegality in the illegality rule base. rules; when the initial digital cell model satisfies any illegality rule, it is determined that the initial digital cell model does not meet the legality conditions, and returns to step S110 to reacquire the initial digital cell model; when the initial digital cell model does not satisfy any of the legality conditions When there is an illegality rule, it is determined that the initial digital cell model meets the legality requirements, and step S120 is entered.
  • the illegality rules may include but are not limited to the following rules: at least one biochemical component information in the biochemical component pool is not called by any biochemical process equation group; at least one biochemical process equation group calls the biochemical group The component information is not included in the biochemical component pool; the concentration of at least one biochemical component in the biochemical component pool does not meet the requirements of the biochemical process equation set for the concentration of the biochemical component.
  • the method for constructing a digital cell model provided by the present disclosure may further include constructing an illegality rule library, in which one or more illegality rules are recorded.
  • step S130 the iterative simulation process of the digital cell model can be evaluated to determine the status of the digital cell model during the iterative simulation process, and whether the end condition is reached according to the evaluation result.
  • the status of the digital cell model can be evaluated according to the process evaluation model library, to determine whether the digital cell model has reached a steady state or a failure state, and then to determine whether the iterative simulation process of the digital cell model has ended. condition.
  • the method for constructing a digital cell model of the present disclosure may further include acquiring a process evaluation model library, for example, constructing a process evaluation model library.
  • the concentration of the biochemical component as a marker needs to be monitored or data processed. It is understood that in order to achieve different purposes, the markers in different processes can be different.
  • the biochemical components used as markers in each process can be obtained based on existing knowledge, for example, by searching biological literature or medical literature to determine which biochemical components can be used as markers reflecting cell apoptosis, and which biochemical indicators can As a marker reflecting cell division.
  • a process evaluation model library includes a first process evaluation model that includes legal concentration ranges for a plurality of biochemical components.
  • step S120 during the iterative simulation process of the digital cell model, the biochemical component pool is evaluated using the first process evaluation model.
  • the evaluation finds that the concentration of one of the biochemical components in the pool or some biochemical components used as markers exceeds the legal concentration range of the biochemical components, the digital cell model is determined to be in a failed state.
  • the first process evaluation model defines that the concentration of a specific biochemical component is not less than 0; if the concentration of the specific biochemical component in a certain updated biochemical component pool is a negative value, then determine the number The cell model is in a failed state.
  • the first process evaluation model can be used to evaluate each updated biochemical component pool, so that when the concentration of the biochemical component in the updated biochemical component pool does not meet the legal concentration range, a timely determination can be made.
  • the advancement of the digital cell model is terminated due to failure.
  • the initial digital cell model is updated in a timely manner to reduce the computational load of obtaining the digital cell model and improve the efficiency of obtaining the digital cell model.
  • sampling evaluation can also be performed on each updated biochemical component pool, for example, an evaluation is performed every 3 to 10 times when the biochemical component pool is updated.
  • the legal concentration range of the biochemical component may be different from the concentration of the biochemical component in the initial biochemical component pool, or may be different from the biochemical component defined in the biochemical component database.
  • the concentration range is different.
  • the limits on the concentration of biochemical components in the initial biochemical component pool and biochemical component database are limits on the starting conditions of the initial digital cell model, rather than on the biochemical components in the iterative simulation process of the digital cell model. Limitation of concentration changes.
  • the legal concentration range of biochemical components is to limit the concentration changes of biochemical components during the iterative simulation process of the digital cell model, so that when the concentration of biochemical components is obviously inconsistent with biological knowledge, Under certain circumstances (such as the occurrence of negative values, such as the occurrence of extreme high concentrations), it is judged that the current iterative simulation process of the digital cell model is obviously inconsistent with biological laws, and then the current iterative simulation of the digital cell model is terminated and the current initial digital cell is abandoned. Model.
  • the process evaluation model library includes a second process evaluation model, and the second process evaluation model includes an upper limit of the number of iterations of the digital cell model iterative simulation.
  • the number of iterative simulations of the digital cell model can be evaluated according to the second process evaluation model; when the number of iterative simulations of the digital cell model reaches the upper limit of the number of iterations, it is determined that the digital cell model is in a failed state.
  • the second process evaluation model limits the upper limit of the number of iterations of the digital cell model iterative simulation to 30,000 times; when the number of iterative simulations of the digital cell model reaches 30,000 times, the digital cell model is determined to be in a failed state.
  • the second process evaluation model can be used to evaluate the current number of iterations.
  • the evaluation can also be performed before each start of solving the first signal path unit, or the evaluation can be performed at other times.
  • the process evaluation model library includes a third process evaluation model
  • the third process evaluation model includes a concentration change trend of at least one biochemical component as a marker during an iterative simulation process, For example, the concentration gradually increases, the concentration gradually decreases, the concentration fluctuates, and the concentration stabilizes at the plateau stage after rising.
  • Assessing the status of digital cell models against the process assessment model library includes:
  • the historical data of the biochemical component pool is evaluated according to the third process evaluation model; if the historical data of the biochemical component pool does not satisfy the third process evaluation model, the digital cell The model reaches a failed state.
  • the third process evaluation model can also detect whether there is a mutation (sudden change) in the concentration change process of the biochemical component as a marker. Specifically, it detects one or more selected historical data in the biochemical component pool. Whether there is a concentration mutation that exceeds the mutation threshold for each biochemical component, and the concentration mutation that exceeds the preset threshold is regarded as a sufficient condition for not passing the screening evaluation (reaching a failure state). In this way, the initial digital cell model that passes the screening has overall continuity during the iterative simulation process.
  • a mutation den change
  • the process evaluation model library includes a fourth process evaluation model
  • the fourth process evaluation model includes changes in the concentration relationship between a plurality of biochemical components as markers during the iterative simulation process.
  • Trend for example, the concentration of some markers gradually increases while the concentration of some markers gradually decreases.
  • Assessing the status of digital cell models against the process assessment model library includes:
  • the historical data of the biochemical component pool is evaluated according to the fourth process evaluation model; if the historical data of the biochemical component pool does not satisfy the fourth process evaluation model, the digital cell The model reaches a failed state.
  • the process evaluation model library includes a fifth process evaluation model, the fifth process evaluation model includes at least one cell type model, each of the cell type models includes a component related to a concentration of a biochemical component.
  • the reference range of cell phenotypic index is included in the process evaluation model library.
  • core phenotypes can be extracted based on published literature. Specifically, cell phenotype parameters corresponding to these core phenotypes are obtained. Illustratively, these cell phenotype parameter reports are not limited to survival phenotype parameters, proliferation phenotype parameters, apoptosis phenotype parameters, migration phenotype parameters, invasion phenotype parameters, clonogenic phenotype parameters, and autophagy phenotype parameters. , angiogenesis phenotypic parameters, epithelial cell to mesenchymal transition phenotypic parameters, etc. Based on the combination of these cell phenotypic parameters, cell phenotypic parameters for characterizing the cell type can be obtained.
  • Assessing the status of digital cell models against the process assessment model library includes:
  • the digital cell model evaluates whether the digital cell model satisfies at least one cell type model after each simulation according to the fifth process evaluation model; if the digital cell model satisfies at least one cell type model during multiple consecutive iterative simulations, If the same cell type model is satisfied, the digital cell model is in a steady state.
  • evaluating whether the digital cell model satisfies at least one cell type model after any simulation includes: determining the simulated cell phenotype index based on the simulated biochemical component pool; based on the cell phenotype index and each cell type model The cell phenotypic index reference range is determined to determine the cell type model that the cell phenotypic index satisfies.
  • the cell phenotype index may include multiple cell phenotype parameters, any one of which is related to the concentration of a biochemical component as a marker (for example, related to an increase or decrease in concentration).
  • the reference range of any one of the cell phenotype indexes includes the reference ranges of multiple cell phenotype parameters.
  • the cell phenotype parameters include survival phenotype parameters, proliferation phenotype parameters, apoptosis phenotype parameters, migration phenotype parameters, invasion phenotype parameters, clonogenic phenotype parameters, autophagy phenotype parameters, blood vessels Generate a plurality of phenotypic parameters and epithelial-to-mesenchymal transition phenotypic parameters.
  • the markers used for each cell phenotype parameter can be obtained from medical or biological literature. It can be understood that with the accumulation of biological knowledge and the deepening of the understanding of cells, new cell phenotype parameters can also be constructed to be applied to the cell phenotype index of the embodiments of the present disclosure.
  • the fifth process evaluation model may include only one cell type model having a cell phenotype index reference range of the target cell type.
  • the digital cell model can represent the target cell type.
  • the digital cell model presents the target cell type in multiple iterations, for example, when the cell phenotypic index in multiple consecutive iterations is basically the same and meets the cell phenotypic index reference range, the digital cell model reaches a steady state.
  • the fifth process evaluation model includes a cell type model for simulating normal cells.
  • the cell phenotypic index of the biochemical component pool when the digital cell model reaches steady state can meet the above reference range of cell phenotypic index, then the digital cell model will appear as normal cells, specifically wild type, when it reaches steady state. (wildtype) normal surviving cells.
  • the fifth process evaluation model includes a plurality of different cell type models, and the different cell type models have cell phenotype index reference ranges of different cell phenotypes.
  • the digital cell model can satisfy any one cell type model in consecutive iterations, the digital cell model reaches a steady state. In other words, the evaluation process is goalless. In this way, the initial digital cell model screened by the cell type model can present or achieve one of the cell types during the iterative simulation process. In subsequent applications, the application scenario of the obtained digital cell model can be determined based on the specific cell type model passed by the initial digital cell model.
  • the fifth process evaluation model may include a plurality of different cell type models, and the different cell type models have different cell phenotype index reference ranges; wherein a specific Cell type models are marked as target cell type models.
  • each cell type model is used to evaluate the biochemical component pool after each simulation separately.
  • the digital cell model satisfies the target cell type model during multiple consecutive iterations of simulation, the digital cell model is in a steady state.
  • the digital cell model satisfies the same cell type model other than the target cell type model during multiple consecutive iterations of simulation, a cell type label is added to the current initial digital cell model and can be saved as a backup digital cell model.
  • the application scenario of the backup digital cell model can be determined based on the cell types that the backup digital cell model can achieve.
  • the digital cell model construction method of the present disclosure can not only obtain the target digital cell model, but also obtain backup digital cell models suitable for other application scenarios, or can speed up the construction speed of digital cell models suitable for other application scenarios. .
  • determining the target digital cell model based on the current initial digital cell model includes:
  • the current initial digital cell model is determined as the target digital cell model.
  • determining the target digital cell model based on the current initial digital cell model includes:
  • the current initial digital cell model is determined as the candidate digital cell model
  • the candidate digital cell model is verified and evaluated according to the verification model library; when the candidate digital cell model does not satisfy the verification model library, the initial digital cell model is updated.
  • the candidate digital cell model it can be judged whether the candidate digital cell model can achieve specific functions, or whether it meets the requirements in terms of simulation precision and accuracy, so that the candidate digital cell model has better performance. Apply effects.
  • the verification model library includes a mutation intervention model
  • the mutation intervention model includes at least one mutation intervention sub-model; any mutation intervention sub-model includes mutation information, exogenous information and Intervention result information;
  • the mutation information includes at least one component mutation information and at least one biochemical process change information;
  • the component mutation information is used to describe the changes in the biochemical component pool caused by mutations in the cell;
  • the biochemical process change information is used It is used to describe changes in biochemical processes caused by intracellular mutations;
  • the exogenous information includes at least one exogenous component-related information and at least one exogenous component-related process equation set;
  • the exogenous component-related information includes exogenous component-related information.
  • the concentration of the source component; the exogenous component related process equation set is used to simulate the concentration change of the biochemical component after a time step caused by the addition of the exogenous component;
  • the intervention result information includes classification labels and The reference range of at least one intervention assessment indicator that is related to changes in the concentration of a biochemical component of the marker;
  • Verification and evaluation of the candidate digital cell model according to the verification model library includes: verification and evaluation of the candidate digital cell model according to the mutation intervention model; verification and evaluation of the candidate digital cell model according to the mutation intervention model includes: At least one of the mutation intervention sub-models performs verification and evaluation on the candidate digital cell model; performing verification and evaluation on the candidate digital cell model according to any one of the mutation intervention sub-models includes:
  • the digital mutant cell model performs iterative simulation to obtain a biochemical component pool of the digital mutant cell model that reaches a steady state as mutation steady-state data, and uses the digital mutant cell model that reaches a steady state as a steady-state digital mutant cell model. ;
  • the mutation intervention digital cell model performs iterative simulation to obtain a biochemical component pool of the mutation intervention digital cell model that reaches a steady state as mutation intervention data;
  • the mutation intervention model can evaluate the ability of the candidate digital cell model to construct a digital mutant cell model and its ability to return to normal cell types in response to external intervention.
  • This ability reflects the pathogenesis and treatment process of some diseases (such as tumors), that is, normal cells mutate into tumor cells as mutated cells, and drugs are used to intervene in tumor cells to achieve treatment of tumors.
  • the candidate digital cell model can pass the evaluation of the mutation intervention model, the candidate digital cell model can construct a personalized digital mutation cell model from the patient's individualized tumor cells and conduct personalized anti-tumor drug evaluation, thereby improving treatment effects and speeds up the treatment process.
  • mutation information exogenous information and intervention result information corresponding to the disease for evaluation
  • the evaluated candidate digital cell models also have good application prospects in the treatment of other diseases.
  • the mutation information is constructed from actual mutated cells, for example from tumor cells.
  • mutation information can be constructed through multi-omics data of mutated cells.
  • the mutation information may be a multi-omics database of mutated cells.
  • the method for constructing a digital cell model further includes:
  • the biochemical component information in the specific type of mutant cells or the biochemical component information of the mutant cells involved in the clinical process is used to determine the component mutation information in the mutation information;
  • the in vitro drug susceptibility test The drug information of the drugs used or the drugs involved in the clinical process is used to determine the exogenous information in the intervention result information;
  • the results of the in vitro drug susceptibility test or the clinical process are used Classification labels (such as valid labels or invalid labels) used to determine the intervention result information.
  • the specific type of mutated cells are tumor cells; the drug information of the drug is drug information of targeted anti-tumor drugs.
  • the external information may be drug information.
  • drug information can be constructed from a digital drug library and actual samples (such as clinical drug treatment samples or in vitro drug susceptibility test samples).
  • the digital drug library includes data sets of different drugs; the data set of each drug is based on the drug target, approved indications, safe dosage range, metabolic kinetic parameters, activity parameters, and side effects. At least one of them is constructed.
  • a drug information can be formed based on the drugs used in actual samples and their concentrations or dosages, as well as the data sets of drugs in the digital drug library, as exogenous information for the mutation intervention sub-model.
  • the digital mutant cell model when the digital mutant cell model presents the same cell type in multiple consecutive simulations, the digital mutant cell model reaches a steady state, and the current digital mutant cell model The mutant cell model is used as the steady-state digital mutant cell model, and the current pool of biochemical components of the digital mutant cell model is used as the mutant steady-state data.
  • the mutation intervention digital cell model when the mutation intervention digital cell model presents the same cell type in multiple consecutive simulations, the mutation intervention digital cell model reaches a steady state, and the current The pool of biochemical components of the mutational intervention digital cell model is used as mutational intervention data.
  • constructing a mutation intervention digital cell model based on the steady-state digital mutation cell model and the exogenous information includes:
  • the external information may be drug sample information.
  • drug sample information can be constructed from a digital drug library and actual sample information.
  • the digital drug library includes data sets of different drugs; the data set of each drug is based on the drug target, approved indications, safe dosage range, metabolic kinetic parameters, activity parameters, and side effects. At least one of them is constructed.
  • a drug sample information can be formed based on the drugs used in actual samples and their concentrations or dosages, as well as the data sets of drugs in the digital drug library, as exogenous information for the mutation intervention sub-model.
  • the intervention evaluation index includes a phenotypic reversal score
  • determining the intervention evaluation indicators includes:
  • the candidate digital cell model fails the validation evaluation of the mutation intervention sub-model. In this way, the candidate digital cell model evaluated by the mutation intervention sub-model has a high degree of fit when simulating mutant cells and the response of mutant cells to drugs.
  • obtaining a digital mutant cell model based on multi-omics data of mutant cells and a pre-constructed digital normal cell model includes:
  • the pre-built digital normal cell model is iteratively simulated until a steady state is reached;
  • the multi-omics data of mutant cells are mapped to the digital normal cell model that reaches steady state, and a digital mutant cell model is obtained.
  • the simulation iteration of the digital mutant cell model can be continued until the digital mutant cell model reaches a steady state.
  • the multi-omics data of mutant cells are mapped to a digital normal cell model that reaches a steady state.
  • Obtaining a digital mutant cell model includes:
  • the multi-omics database of the mutant cells is mapped to the digital normal cell model that reaches the steady state to obtain the digital mutant cell model.
  • mutant cells can be analyzed and sequenced through high-throughput sequencing technology or other technologies to obtain multi-omics data of the mutant cells.
  • constructing a multi-omics database of mutant cells based on the multi-omics data of mutant cells includes:
  • the gene variation parser constructs a digital gene variation library in the multi-omics database of mutant cells based on the acquired gene variation group data in the multi-omics data of mutant cells;
  • the epigenetic analyzer constructs a digital epigenetic library in the multi-omics database of mutant cells based on the epigenome data in the acquired multi-omics data of mutant cells;
  • the transcriptome parser constructs a digital transcript library in the multi-omics database of mutant cells based on the obtained transcriptome data in the multi-omics data of mutant cells;
  • the proteome parser constructs a digital protein library in the multi-omics database of mutant cells based on the proteome data in the acquired multi-omics data of mutant cells.
  • personalized multi-omics data of mutated cells can be obtained based on individual patient's tissues, organs or pathological samples.
  • the multi-omics data of these individualized mutated cells can include gene variant data, epigenome data, transcriptome data, and proteome data.
  • the multi-omics database of mutant cells is mapped to a digital normal cell model that reaches a steady state.
  • Obtaining the digital mutant cell model includes:
  • Information about proteins in a digital protein library is mapped to a pool of biochemical components of a digital normal cell model reaching steady-state conditions.
  • the genetic variation parser can obtain the genetic variation group data, and then classify the genetic variation according to type into single nucleotide variation, copy number variation, fusion variation, and indel variation.
  • Single nucleotide mutations are divided into nonsense mutations, missense mutations and splicing mutations; insertion and deletion mutations are divided into frameshift mutations and in-frame mutations.
  • Variants are functionally classified according to mutation classification and published literature, and are mainly divided into two categories: inactivation of function and gain of function, which can also include copy number increase, copy number decrease, in-frame gain of function, in-frame inactivation of function, etc.
  • white activity parameters or transcription level parameters can be determined and stored in a digital gene variation library.
  • the protein activity parameters or gene transcription level parameters related to the mutated genes in the digital gene variant library can be mapped to a digital normal cell model that reaches the steady state.
  • the signaling pathway unit or gene expression unit of the digital normal cell model In the signaling pathway unit or gene expression unit of the digital normal cell model.
  • the protein activity parameters related to the mutated gene include protein function inactivation parameters and protein function acquisition parameters; the gene transcription level parameters related to the mutated gene include transcription level increasing parameters and transcription level decreasing parameters.
  • the protein function inactivation parameter can be a complete inactivation of the protein function, or it can be a correction coefficient not greater than 1 for the biochemical process equation set involving the protein function to reflect the inactivation or reduction of the protein function; Or you can set the protein parameters directly.
  • the protein function acquisition parameter can be a correction coefficient greater than 1 to improve the biochemical reaction of the protein parameters, or can be one or more newly added biochemical processes to reflect the new biochemical processes caused by the protein function acquisition.
  • the transcription level increasing parameter and the transcription level decreasing parameter may be correction coefficients greater than 1 and less than 1 respectively, or may be parameters obtained based on literature or experiments.
  • the epigenetic parser can mainly divide the epigenetic group data into DNA methylation and histone acetylation according to the type of epigenetic inheritance. is the methylation of the gene coding region and the methylation of the gene promoter region. If the input is relative quantitative data, the numerical value (as part of the digital epigenetic library) of the relative quantitative data will be directly mapped to the corresponding digital cell model. On the scale of gene transcription; if the input is qualitative data, such as methylation of the gene promoter region, the digital cell model corresponding to high methylation can preset a certain degree of reduction in gene transcription scale (this reduction is used as a digital epigenetic library a part of).
  • the gene transcription scale parameters related to epigenetics include: an increase parameter of the gene transcription scale, a reduction parameter of the gene transcription scale, a unchanged gene transcription scale, or a value of the gene transcription scale.
  • the gene transcription scale parameters related to epigenetics include: an increase parameter of the gene transcription scale (for example, a correction coefficient greater than 1), a reduction parameter of the gene transcription scale (for example, a correction coefficient of less than 1), a gene transcription scale change parameter. Variation or the value of gene transcription scale.
  • the transcriptome parser can divide the RNA in the transcriptome data into non-coding RNA and mRNA according to whether it encodes a protein.
  • the correspondence to the digital cell model is based on published literature.
  • the reaction node that is, the regulatory node
  • it is reflected in the form of a reaction equation (as part of the transcriptome data)
  • mRNA it is directly mapped to the concentration of the corresponding mRNA in the digital cell model based on the quantitative data (the concentration is used as part of the transcriptome data) .
  • mapping the multi-omics database of mutant cells to the digital normal cell model that reaches the steady state includes: mapping the non-coding RNA-related biochemical process equations in the digital transcript library to the digital normal cells that reach the steady state.
  • the information of mRNA in the digital transcription library is mapped to the biochemical component pool of the digital normal cell model that reaches the steady state.
  • the mutated cells are tumor cells of a tumor patient.
  • the embodiment of the present disclosure also provides a device UB for constructing a digital mutant cell model.
  • the device UB for constructing a digital mutant cell model is configured to obtain based on the multi-omics data of the mutant cells and the pre-constructed digital normal cell model.
  • Digital mutant cell model wherein, the pre-constructed digital normal cell model includes a biochemical component pool and multiple signaling pathway units; the biochemical component pool includes multiple biochemical component information, and the biochemical component information includes biochemical component information.
  • Concentration and/or location information of components; the signal pathway unit is used to simulate the signal pathway of biological cells; the signal pathway unit includes at least one biochemical reaction module, and the biochemical reaction module is used to simulate all biological processes using a set of biochemical process equations. Describe the biochemical processes that occur in signaling pathway units.
  • the digital mutant cell model construction device UB includes:
  • the operation module UB1 is used to iteratively simulate the pre-built digital normal cell model until it reaches a steady state;
  • the data analysis module UB2 is used to obtain multi-omics data of mutant cells and construct a multi-omics database of mutant cells based on the multi-omics data of mutant cells;
  • the data mapping module UB3 is used to map the multi-omics database of the mutant cells to the digital normal cell model that has reached the steady state, and obtain the digital mutant cell model.
  • the embodiment of the present disclosure provides a drug efficacy prediction method. See Figure 9.
  • the drug efficacy prediction method includes:
  • Step S210 Construct a digital mutant cell model based on the pre-constructed digital normal cell model and multi-omics data of mutant cells;
  • Step S220 the digital normal cell model performs iterative simulation until it reaches a steady state, and obtains the cell phenotypic index of the digital normal cell model in the steady state as the normal cell phenotypic index;
  • Step S230 The digital mutant cell model performs iterative simulation until it reaches a steady state, and obtains the cell phenotypic index of the digital mutant cell model in the steady state as the mutant cell phenotypic index;
  • Step S240 construct a mutant cell model for digital drug intervention based on the digital mutant cell model and drug information in steady state
  • Step S250 perform an iterative simulation of the digital drug-intervention mutant cell model until it reaches a steady state, and obtain the post-intervention cell phenotype index of the digital drug-intervention mutant cell model in the steady state;
  • Step S260 Obtain drug efficacy prediction results based on the normal cell phenotypic index, the mutant cell phenotypic index and the post-intervention cell phenotypic index.
  • iterative simulation of the mutated cell model of digital drug intervention until a steady state is reached includes:
  • the mutated cell model of digital drug intervention is simulated iteratively until the cell phenotypic index of the mutated cell model of digital drug intervention remains unchanged in multiple consecutive iterations.
  • the cell phenotypic index in the multiple consecutive iterations is used as the post-intervention cell phenotype. Index; the cell phenotypic index includes a plurality of phenotypic parameters, each phenotypic parameter is related to the concentration of at least one biochemical component serving as a marker.
  • obtaining the drug efficacy prediction results based on the normal cell phenotypic index, the mutant cell phenotypic index and the post-intervention cell phenotypic index includes:
  • the mutant cell phenotypic index and the post-intervention cell phenotypic index the degree of reversal of the post-intervention cell phenotypic index to the normal cell phenotypic index is obtained.
  • obtaining the degree of reversal of the cell phenotypic index to the normal cell phenotypic index after intervention includes:
  • the phenotypic abnormality index is obtained;
  • the phenotype reversal index is obtained based on the difference between the phenotypic index of the mutant cells and the phenotypic index of the cells after intervention;
  • a phenotypic reversal score is obtained based on the phenotypic reversal index and the phenotypic abnormality index.
  • the obtaining of drug efficacy prediction results also includes:
  • the sensitivity prediction result of the mutant cell to the drug corresponding to the drug information is obtained; when the phenotypic reversal score is not less than the phenotypic reversal score threshold, the mutation The cells are sensitive to the drug corresponding to the drug information; when the phenotype reversal score is less than the phenotype reversal score threshold, the mutant cells are insensitive to the drug corresponding to the drug information.
  • the drug efficacy prediction method of the present disclosure can predict whether mutant cells are sensitive to the drug corresponding to the drug information, so as to facilitate the selection of suitable therapeutic drugs.
  • constructing a mutant cell model for digital drug intervention includes:
  • the drug information may include information on only one drug, or may include information on multiple drugs used in combination.
  • the drug efficacy prediction method can predict the therapeutic effect of the drug on the disease corresponding to the mutated cells.
  • the drug efficacy prediction method can predict the therapeutic effect of the multiple drugs (ie, combined drugs, drug combinations) on the disease corresponding to the mutated cells.
  • multiple different drug information can be obtained; the phenotypic reversal score corresponding to each drug information is determined respectively, and at least part of the phenotypic reversal score and its corresponding drug information are output according to the score.
  • the method for determining the phenotypic reversal score corresponding to any drug information is to map a drug information to a digital mutant cell model in the steady state, and obtain the digital drug-intervention mutant cells corresponding to the drug information. model, and based on this, the phenotype reversal score corresponding to the drug information is obtained.
  • At least two of the drug information are drug information of the same drug at different drug concentrations. In another example, among the plurality of different drug information, at least two of the drug information are drug information of different drugs. In another example, among the plurality of different drug information, at least two of the drug information are drug information of different drug combinations.
  • the drug efficacy prediction method also includes:
  • Obtain a digital drug library which includes data sets of different drugs; the data set of each drug is based on the drug target, approved indications, safe dosage range, metabolic kinetic parameters, activity parameters, and side effects. At least one to build;
  • the drug information of the drug is obtained; or, according to the data sets of multiple drugs, the drug information including the information of each drug is obtained, that is, the drug information is the information of the drug combination.
  • the digital drug library can automatically generate multiple different drug information based on the actual situation of the mutated cells according to preset rules, such as indications.
  • the drug efficacy prediction engine can combine the drug information to give drug treatment suggestions based on the drug efficacy prediction method of the present disclosure.
  • the drug information includes drug-related information and a drug-related process equation set.
  • the drug-related information is used to describe the dose or concentration of the drug.
  • the drug-related process equation set is used to simulate the addition of the drug. The resulting concentration change of a biochemical component after a time step.
  • the mutated cells are tumor mutated cells.
  • the drug corresponding to the drug information is a targeted drug.
  • a drug refers to a chemical component or combination of chemical substances that has a therapeutic effect or intervention effect on a disease.
  • drugs can have different classifications for different diseases.
  • drugs can include but are not limited to targeted anti-tumor drugs, cytotoxic drugs, cellular immune modulators, cell epigenetic modulators, etc. It can be understood that in the embodiments of the present disclosure, not all drugs are required to be administratively approved or have sufficient efficacy.
  • Some chemical components or compositions that have pharmaceutical potential or have the possibility of becoming drugs can be As drugs in embodiments of the present disclosure.
  • the drug used may be brand new and not clinically proven or administratively approved.
  • a potentially useful drug may be used.
  • specific verification is performed based on the digital cell model provided by the embodiment of the present disclosure to determine the possible efficacy of the compound in treating the specific disease, and then promote or accelerate the development or screening of therapeutic drugs for the disease. check.
  • the embodiment of the present disclosure also provides a drug efficacy prediction device UC, which includes:
  • the mutation model module UC1 is configured to construct a digital mutant cell model based on the pre-built digital normal cell model and the multi-omics data of the mutant cells;
  • the normal index module UC2 is configured as a digital normal cell model to perform iterative simulation until a steady state is reached, and obtains the cell phenotypic index of the digital normal cell model in the steady state as the normal cell phenotypic index;
  • the mutation index module UC3 is configured as a digital mutant cell model to perform iterative simulation until a steady state is reached, and obtains the cell phenotypic index of the digital mutant cell model in the steady state as the mutant cell phenotypic index;
  • the drug intervention module UC4 is configured to construct a mutated cell model for digital drug intervention based on the digital mutated cell model and drug information in steady state;
  • the intervention index module UC5 is configured to perform iterative simulation of the mutated cell model with digital drug intervention until it reaches a steady state, and obtain the post-intervention cell phenotype index of the mutated cell model with digital drug intervention in the steady state;
  • the efficacy prediction module UC6 is configured to obtain drug efficacy prediction results based on the normal cell phenotypic index, mutant cell phenotypic index, and post-intervention cell phenotypic index.
  • inventions of the present disclosure also provide a digital cell system.
  • the digital cell system includes the above-mentioned digital mutant cell model construction device UB, and also includes:
  • Construction engine MA used to build digital normal cell models based on biochemical information
  • Digital cell engine M1 used to run digital cell models
  • Digital drug library M3 used to provide drug information
  • Data mapping engine M4 used to map drug information in the digital drug library to the digital cell model
  • the drug efficacy analysis engine M5 is used to predict drug efficacy based on the running results of the digital cell engine.
  • the build engine MA can build a digital cell model, such as obtaining a normal digital cell model of normal cells.
  • the digital mutant cell model construction device UB can receive multi-omics data of mutant cells and normal digital cell models to construct a digital mutant cell model.
  • the data mapping engine M4 can map the drug information in the digital drug library M3 to the digital mutant cell model that has reached the steady state, and obtain the results of digital drug intervention.
  • the operation module UB1 of the digital mutant cell model construction device UB can be the same module as the digital cell engine M1.
  • the data mapping module UB3 of the digital mutant cell model construction device UB can be the same module as the data mapping engine M4.
  • an electronic device capable of implementing the above method of constructing a digital mutant cell model is also provided.
  • an electronic device capable of implementing the above drug efficacy prediction method is also provided.
  • FIG. 12 An electronic device 1000 according to this embodiment of the present disclosure is described below with reference to FIG. 12 .
  • the electronic device 1000 shown in FIG. 12 is only an example and should not bring any limitations to the functions and usage scope of the embodiments of the present disclosure.
  • electronic device 1000 is embodied in the form of a general computing device.
  • the components of the electronic device 1000 may include, but are not limited to: the above-mentioned at least one processing unit 1010, the above-mentioned at least one storage unit 1020, a bus 1030 connecting different system components (including the storage unit 1020 and the processing unit 1010), and the display unit 1040.
  • the storage unit stores program code, and the program code can be executed by the processing unit 1010, so that the processing unit 1010 performs various exemplary methods according to the present disclosure described in the "Example Method" section of this specification. Implementation steps.
  • the storage unit 1020 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 10201 and/or a cache storage unit 10202, and may further include a read-only storage unit (ROM) 10203.
  • RAM random access storage unit
  • ROM read-only storage unit
  • Storage unit 1020 may also include a program/utility 10204 having a set of (at least one) program modules 10205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, Each of these examples, or some combination, may include the implementation of a network environment.
  • program/utility 10204 having a set of (at least one) program modules 10205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, Each of these examples, or some combination, may include the implementation of a network environment.
  • Bus 1030 may be a local area representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or using any of a variety of bus structures. bus.
  • Electronic device 1000 may also communicate with one or more external devices 1100 (e.g., keyboard, pointing device, Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with electronic device 1000, and/or with Any device that enables the electronic device 1000 to communicate with one or more other computing devices (eg, router, modem, etc.). This communication may occur through input/output (I/O) interface 1050.
  • the electronic device 1000 may also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) through the network adapter 1060. As shown, network adapter 1060 communicates with other modules of electronic device 1000 via bus 1030.
  • network adapter 1060 communicates with other modules of electronic device 1000 via bus 1030.
  • the example embodiments described here can be implemented by software, or can be implemented by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure 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, U disk, mobile hard disk, etc.) or on the network , including several instructions to cause a computing device (which may be a personal computer, a server, a terminal device, a network device, etc.) to execute a method according to an embodiment of the present disclosure.
  • a computing device which may be a personal computer, a server, a terminal device, a network device, etc.
  • a computer-readable storage medium is also provided, on which a program product capable of implementing the method described above in this specification is stored.
  • a computer program stored on a computer-readable storage medium is executed by a processor, the above-mentioned method for constructing a digital mutant cell model can be implemented.
  • the computer program stored on the computer-readable storage medium is executed by the processor, the above-mentioned drug efficacy prediction method can be implemented.
  • various aspects of the present disclosure can also be implemented in the form of a program product, which includes program code.
  • program product When the program product is run on a terminal device, the program code is used to cause the The terminal device performs the steps according to various exemplary embodiments of the present disclosure described in the above "Example Method" section of this specification.
  • the program product for implementing the above method according to an embodiment of the present disclosure may adopt a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer.
  • a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.
  • the program product may take the form of 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, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave carrying readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.
  • a readable signal medium may also be any readable medium other than a readable storage medium that can send, propagate, or transport the program for use by or in connection with an instruction execution system, apparatus, or device.
  • Program code embodied on a readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical cable, RF, etc., or any suitable combination of the foregoing.
  • Program code for performing operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., as well as conventional procedural Programming language—such as "C" or a similar programming language.
  • the program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server execute on.
  • the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device, such as provided by an Internet service. (business comes via Internet connection).
  • LAN local area network
  • WAN wide area network

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Abstract

L'invention porte sur un procédé et un appareil de prédiction d'efficacité de médicament basés sur un modèle de cellule numérique, un support et un dispositif, se rapportant au domaine technique des modèles de cellules numériques. Le procédé de prédiction d'efficacité de médicament comprend : la construction d'un modèle de cellule mutante numérique selon un modèle de cellule normal numérique pré-construit et des données multi-omiques d'une cellule mutante (S210) ; la réalisation d'une simulation itérative sur le modèle de cellule normal numérique pré-construit jusqu'à ce qu'un état stable soit atteint, et l'acquisition d'un indice de phénotype de cellule du modèle de cellule normale numérique dans l'état stable en tant qu'indice de phénotype de cellule normal (S220) ; la réalisation d'une simulation itérative sur le modèle de cellule mutant numérique jusqu'à ce que l'état stable soit atteint, et l'acquisition d'un indice de phénotype de cellule du modèle de cellule mutant numérique dans l'état stable en tant qu'indice de phénotype de cellule mutant (S230) ; la construction d'un modèle de cellule mutant intervenu par médicament numérique selon le modèle de cellule mutante numérique dans l'état stable et des informations de médicament (S240) ; la réalisation d'une simulation itérative sur le modèle de cellule mutant intervenu par médicament numérique jusqu'à ce que l'état stable soit atteint, et l'acquisition d'un indice de phénotype de cellule intervenu du modèle de cellule mutant intervenu par médicament numérique dans l'état stable (S250) ; et l'acquisition d'un résultat de prédiction d'efficacité de médicament en fonction de l'indice de phénotype de cellule normal, de l'indice de phénotype de cellule mutant et de l'indice de phénotype de cellule intervenu (S260).
PCT/CN2022/115815 2022-05-31 2022-08-30 Procédé et appareil de prédiction d'efficacité de médicament basés sur un modèle de cellule numérique, support et dispositif WO2023231203A1 (fr)

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CN202210613704.0A CN117198387A (zh) 2022-05-31 2022-05-31 药物疗效预测方法及装置、介质、设备
CN202210613704.0 2022-05-31
CN202210613715.9A CN117198388A (zh) 2022-05-31 2022-05-31 数字突变细胞模型的构建方法及装置、介质、设备、系统

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CN107885976A (zh) * 2017-11-08 2018-04-06 吉林师范大学 表观遗传学药物凋亡诱导模型的构建方法
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WO2002088383A2 (fr) * 2001-04-27 2002-11-07 MAX-PLANCK-Gesellschaft zur Förderung der Wissenschaften e.V. Procede, systeme et leur utilisation pour l'identification de cibles, pour une intervention medicale efficace et/ou pour determiner les effets d'agents therapeutiques et/ou pour la detection a distance d'une signalisation cellulaire par cyclage nucleocytoplasmique
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