CN116913528A - Modeling and simulation method and system for whole-cell multi-element substance - Google Patents

Modeling and simulation method and system for whole-cell multi-element substance Download PDF

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CN116913528A
CN116913528A CN202310270621.0A CN202310270621A CN116913528A CN 116913528 A CN116913528 A CN 116913528A CN 202310270621 A CN202310270621 A CN 202310270621A CN 116913528 A CN116913528 A CN 116913528A
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陈广勇
田淙宇
廖祥云
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Zhejiang Lab
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Abstract

The invention discloses a modeling and simulation method and system for whole-cell multi-element substances. The method comprises the following steps: acquiring medical image data, and extracting a cell structure by using a deep learning model; reconstructing a three-dimensional cell model based on a moving cube algorithm for the extracted cell structures, wherein the three-dimensional cell model characterizes the spatial position of each cell structure by a three-dimensional grid; for constituent substances in the extracted cell structure, cell dynamic simulation was performed based on smooth particle fluid dynamics. The invention can simulate the cell structure and the substance movement therein, and ensures the real-time performance of the simulation on the premise of ensuring the authenticity.

Description

Modeling and simulation method and system for whole-cell multi-element substance
Technical Field
The invention relates to the technical field of graphic image processing, in particular to a whole-cell multi-element substance modeling and simulation method and system.
Background
Currently, computers are increasingly being combined with other industries. For example, most industries have a need for simulation and visualization using computers, seeking more intuitive presentation and predictive experimental results. In the field of cell research in biomedicine, computers have become an indispensable tool, and as early as 1999, students have tried to build virtual cell models using computers. In recent years, the visual layer of cells has had work of the ivan viola team, who completed the complete presentation of static cells and rendered details. On the cell simulation level, zaida Luthen-Schulten et al created the most complete and life-moving computer simulation of cells so far, accelerating the search of biologists for the course of cell operation.
The work of the ivan viola team completely displays the structure of the cell and renders detailed parts, but lacks the display of the dynamic activity process of each structure in the cell, and only displays the visual display of the cell structure including organelles. The whole cell model constructed by Zaida Lutheny-Schulten team is constructed from the gene level, and quantitatively analyzes the life rule to complete the simulation of basic vital activity of the cell, so that the bottom-up method can carefully describe the cell operation process. However, in the simulation of the organelle hierarchy, this solution appears to be exceptionally redundant, and the enormous computational effort required to do so does not satisfy the real-time visual requirements.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a whole-cell multi-element substance modeling and simulation method and system.
According to a first aspect of the present invention, a whole-cell multi-element modeling and simulation method is provided. The method comprises the following steps:
acquiring medical image data, and extracting a cell structure by using a deep learning model;
reconstructing a three-dimensional cell model based on a moving cube algorithm for the extracted cell structures, wherein the three-dimensional cell model characterizes the spatial position of each cell structure by a three-dimensional grid;
for constituent substances in the extracted cell structure, cell dynamic simulation was performed based on smooth particle fluid dynamics.
According to a second aspect of the present invention, a whole-cell multi-element modeling and simulation system is provided. The system comprises:
feature extraction unit: the method comprises the steps of obtaining medical image data and extracting cell structures by using a deep learning model;
a three-dimensional model reconstruction unit: reconstructing a three-dimensional cell model based on a moving cube algorithm for the extracted cell structures, wherein the three-dimensional cell model characterizes the spatial position of each cell structure by a three-dimensional grid;
simulation unit: for performing a cell dynamic simulation on constituent substances in the extracted cell structure based on smooth particle fluid dynamics.
Compared with the prior art, the invention has the advantages that a whole-cell multi-element substance modeling and simulation system framework is provided, a cell model is rebuilt based on deep learning, and the structure of the multi-element substance of the cell is dynamically simulated based on SPH (smooth particle fluid dynamics), so that the whole process from data to visual dynamic simulation of the cell is completed.
Other features of the present invention and its advantages will become apparent from the following detailed description of exemplary embodiments of the invention, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart of a whole cell multi-component modeling and simulation method according to one embodiment of the invention;
FIG. 2 is an overall flow chart of a whole cell multi-component modeling and simulation method according to one embodiment of the invention;
FIG. 3 is a schematic diagram of a U-Net structure according to one embodiment of the invention;
FIG. 4 is a schematic illustration of a protein simulation demonstration in accordance with one embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
In brief, the invention provides a whole-cell multi-element substance modeling and simulation scheme, which reconstructs a cell model based on deep learning and dynamically simulates the structure of the multi-element substance of the cell based on SPH. The invention rebuilds the three-dimensional model of the multi-element substance structure of the cell, and utilizes the SPH-based simulation algorithm to dynamically simulate the multi-element substance structure in the cell, thereby simulating the overall dynamic effect of the whole cell internal environment.
Specifically, as shown in fig. 1 and 2, the provided modeling and simulation method for the whole-cell multi-element substance comprises the following steps:
step S110, obtaining medical image data, and performing cell structure segmentation and extraction by using a deep learning model.
For example, a deep learning model for medical image segmentation uses a U-Net network, as shown in FIG. 3. The U-shaped structure of the U-Net network is a coding-decoding structure, the compression channel is an encoder for extracting the characteristics of the image layer by layer, and the expansion channel is a decoder for restoring the position information of the image. Various structures can be segmented from the acquired primitive cell images of different levels by using the U-Net network.
Step S120, reconstructing a three-dimensional model of the cell based on the moving cube algorithm for the extracted cell structure.
The mobile cube algorithm (marchangcube) is an algorithm for reconstructing medical images, and divides a space into a plurality of hexahedral meshes, checks the field value of each vertex in the space, constructs an isosurface formed by triangular patches according to the vertex state of the hexahedron, then splices all the triangular patches according to the vertex coordinates and normal vectors, and finally obtains a reconstructed three-dimensional mesh. In the invention, the MarchingCube algorithm is used for extracting the cell structure obtained in the previous step, reconstructing a three-dimensional grid of the structure for each cell structure, and calculating the spatial position of the three-dimensional grid.
Step S130, performing cell dynamic simulation based on the smooth particle fluid dynamics.
Smooth particle fluid dynamics (SPH) is a method widely used for solid mechanics and fluid mechanics, but it was originally used to simulate problems such as planetary explosions. Starting from the problem to be solved by the present invention, the SPH method is selected to dynamically simulate the constituent substances in cells.
First, the present invention classifies the cellular structures obtained by the above processes into two classes: static structures and dynamic structures, which are considered as a type of fixed rigid body for static structures that do not need to be operated, are modeled as boundary particles in the SPH method. For a dynamic structure that needs to simulate motion, it is considered a particle and modeled as a moving particle in SPH.
(1) Static structure
In one embodiment, triangular scanning sampling is performed on all three-dimensional grids of the static structure to obtain boundary particles. And corresponding density ρ is set according to the structure type b Coefficient of viscosity mu b And the like.
The density of each boundary particle is then corrected according to the classical approach of akini:
wherein i represents the index of the particle, ρ b Correction factors for the density of the structure to which the particles belongThen it can be obtained by:
where k represents the index of surrounding boundary particles of the same type, Σ k W ik I.e. the sum of the SPH kernel values of the boundary particle and the surrounding boundary particles of the same type, i.e. the density value of the boundary particle is corrected by using the surrounding boundary particles of the same type.
(2) Dynamic structure
And generating dynamic particles based on the dynamic structure space position information acquired in the process, and uniformly treating all dynamic structures as particle processing in the modeling process. For different types of structures, the corresponding particles are given different masses, densities, viscosity coefficients, and velocities, respectively noted asAnd calculating the specific density ρ of the particles at the beginning of each frame i ：
Where j is the index of the adjacent particles, W ij A smooth kernel function, W, representing the target particle i and the adjacent particle j ik Representing the smooth kernel function of the target particle i and the boundary particle k,represents the density of the boundary particles j after correction, +.>Representing the mass, Σ, of the target particle i j W ij I.e. the sum of the kernel function values of all adjacent particles.
(3) Motion simulation
In one embodiment, two types of forces for dynamic particles are calculated using the SPH method: the pressure at which the density is maintained and the viscous forces of the coupling between the particles.
For the pressure part, the pressure p of the particle is calculated, e.g. using the Tait state equation i ：
Where k represents the pressure constant, i represents the target particle index, ρ i Representing the current density of the target particles i,the static density of the target particle i is represented by γ, which is a set parameter, for example, k=1/7, and γ=7. While pressure can be expressed as:
when the particle j is a boundary particle,
and the viscous force is calculated as:
wherein mu i Represents the viscosity coefficient, mu, of the target particle i j Representing the viscosity coefficient, v, of the adjacent particles j i Representing the velocity, v, of the target particle i j Indicating the velocity, W, of the adjacent particles j ij Representing a smooth kernel function, ρ, between the target particle i and the adjacent particle j i Representing the current density, ρ, of the target particle i j Representing the current density of the adjacent particles j.
After all forces are calculated, the positions and velocities of all dynamic particles are updated according to the time step, and the behavior of the dynamic particles is different due to the different structures to which they belong.
Correspondingly, the invention also provides a whole-cell multi-element substance modeling and simulation system for realizing one or more aspects of the method. For example, the system includes: feature extraction unit: the method comprises the steps of obtaining medical image data and extracting cell structures by using a deep learning model; a three-dimensional model reconstruction unit: reconstructing a three-dimensional cell model based on a moving cube algorithm for the extracted cell structures, wherein the three-dimensional cell model characterizes the spatial position of each cell structure by a three-dimensional grid; simulation unit: for performing a cell dynamic simulation on constituent substances in the extracted cell structure based on smooth particle fluid dynamics. Wherein each unit may be implemented by an FPGA, a general-purpose processor, or a special-purpose processor.
To verify the feasibility of the present invention, the movement of intracellular proteins was simulated as shown in FIG. 4. It can be seen that the invention can effectively simulate the cell multi-element substance.
In summary, the invention provides a whole-cell multi-element substance modeling and simulation system framework, which reconstructs a cell model based on deep learning, carries out dynamic simulation on the structure of multi-element substances of cells based on SPH, simulates the structure/substance movement in the organelle/protein layer, and ensures the real-time performance of the simulation on the premise of ensuring the authenticity.
The present invention may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of the present invention may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++, python, and the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer readable program instructions, which can execute the computer readable program instructions.
Various aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvements in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims (10)

1. A modeling and simulation method of whole-cell multi-element substances comprises the following steps:
acquiring medical image data, and extracting a cell structure by using a deep learning model;
reconstructing a three-dimensional cell model based on a moving cube algorithm for the extracted cell structures, wherein the three-dimensional cell model characterizes the spatial position of each cell structure by a three-dimensional grid;
for constituent substances in the extracted cell structure, cell dynamic simulation was performed based on smooth particle fluid dynamics.
2. The method of claim 1, wherein the deep learning model is a U-Net mesh for segmenting cellular structures of different levels from input medical image data.
3. The method according to claim 1, wherein the cell dynamic simulation based on smooth particle fluid dynamics for constituent substances in the extracted cell structure comprises the steps of:
dividing the cell structure into a static structure and a dynamic structure, wherein the static structure is used as a fixed rigid body, modeling is performed as boundary particles in smooth particle fluid dynamics, the dynamic structure is used as particles, and modeling is performed as dynamic particles in smooth particle fluid dynamics;
triangular scanning sampling is carried out on the three-dimensional grid of the static structure to obtain boundary particles, corresponding density and viscosity coefficient are set according to the structure type, and the density of the static particles is corrected;
the dynamic particle information is updated every frame, including a dynamic particle adjacency list, a dynamic particle density, a dynamic particle pressure, a dynamic particle viscous force, a dynamic particle position, and a dynamic particle velocity.
4. A method according to claim 3, wherein the density of the boundary particles is expressed as:
wherein ρ is b Is the density of the structure to which the boundary particles belong,is a correction factor expressed as:
where i represents the index of the target particle, Σ k W ik Is the sum of the smooth particle hydrodynamic kernel values of the boundary particle and the surrounding type of boundary particle, and the subscript k represents the index of the surrounding type of boundary particle.
5. The method of claim 4, wherein at the beginning of each frame, the density ρ of the dynamic particles is calculated according to the following formula i ：
Wherein Σ is j W ij Is the sum of the kernel values of all adjacent particles, j is the index of the adjacent particles, W ij Representing a smooth kernel function, W, between a target particle i and an adjacent particle j ik Representing the smooth kernel function of the target particle i and the boundary particle k,representing the mass of the target particle i>Indicating the density of the boundary particles j after correction.
6. The method of claim 5, wherein the dynamic particle pressure p i Expressed as:
where k=1/7, γ=7, ρ j Representing the current density, ρ, of adjacent particles j 0i Representing the static density of the target particles i.
7. The method of claim 6, wherein the pressure of the dynamic particles is expressed as:
wherein, when the particle j is a boundary particle,
the viscosity of the dynamic particles is expressed as:
wherein mu i Represents the viscosity coefficient, mu, of the target particle i j Representing the viscosity coefficient, v, of the adjacent particles j i Representing the velocity, v, of the target particle i j Representing the velocity, ρ, of the adjacent particle j i Representing the current density, ρ, of the target particle i 0i Representing the static density, ρ, of the target particle i j Representing the current density of the adjacent particles j, delta represents the gradient.
8. The method of claim 6, wherein reconstructing a three-dimensional model of cells based on a moving cube algorithm for the extracted cell structures comprises the steps of:
dividing a space corresponding to the medical image data into a plurality of hexahedral meshes, and checking a field value of each vertex in the space;
constructing an isosurface formed by triangular patches according to the vertex states of the hexahedral meshes;
and splicing all triangular patches according to the vertex coordinates and the normal vectors to obtain a reconstructed three-dimensional grid, wherein for each cell structure, the three-dimensional grid of the cell structure is obtained by reconstruction, and the spatial position of the three-dimensional grid is calculated.
9. A whole-cell multi-element modeling and simulation system, comprising:
feature extraction unit: the method comprises the steps of obtaining medical image data and extracting cell structures by using a deep learning model;
a three-dimensional model reconstruction unit: reconstructing a three-dimensional cell model based on a moving cube algorithm for the extracted cell structures, wherein the three-dimensional cell model characterizes the spatial position of each cell structure by a three-dimensional grid;
simulation unit: for performing a cell dynamic simulation on constituent substances in the extracted cell structure based on smooth particle fluid dynamics.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor realizes the steps of the method according to any of claims 1 to 8.
CN202310270621.0A 2023-03-20 2023-03-20 Modeling and simulation method and system for whole-cell multi-element substance Pending CN116913528A (en)

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