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3D Visualization of Microbial Structures using GPU

Imagine seeing the invisible world of microbes in stunning 3D detail. The topic of GPU-based 3D visualization of microbial structures is fast evolving and can change how we perceive the microbiome.

It can be helpful to see a variety of microbiological structures, including simple communities and single cells.

Moreover, this knowledge can help research microbial interactions, create fresh medicines and vaccines, and even design novel substances.

The ability to view things we have never seen before is one of the fascinating aspects of GPU-based 3D visualization of microbiological structures.

For instance, researchers can examine the intricate structure of the billions of microorganisms that comprise the human microbiome using GPU-based 3D visualization.

This way, new information about the microbiome's function in human health and disease.

How can GPUs be useful for 3D visualization of microbial structures?

There are several ways that GPUs can be helpful to the 3D visualization of microbiological structures.

Volume Rendering Using GPUs

Rendering 3D data as a series of 2D slices is a volume rendering technique. Visualizing scientific data, such as fluid flow simulations and climate models. Moreover, medical images, such as CT and MRI scans, are frequently helpful.

Microbes are rendered in three dimensions: intracellular core, chromatin, and microbe on an isolated, dark background.

Volume rendering works by projecting rays through the 3D data volume. At each location along the beam, the data is sampled to determine the color and opacity of the voxel. The voxel's color and opacity then influence the final image's color.

We can visualize Medical images, including those from CT and MRI scans, using volume rendering. It can aid medical professionals in the diagnosis and planning of illnesses.

Moreover, we can also visualize Scientific data like fluid flow simulations and climate models using volume rendering. It can aid in grasping the understanding of complex phenomena by scientists.

Volume rendering shows engineering data, such as CAD models and 3D scans. It can help engineers to design and construct goods.

GPUs from both Nvidia and AMD are useful in volume rendering. Some popular GPUs used in volume rendering include:

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Here is more explanation of the volume rendering process

Prepare the data 

Converting data to an appropriate format is the first step in preparing it for volume rendering. So, we can mainly perform Volume rendering in VTK (Visualisation Toolkit) format.

Several tools for processing and displaying scientific data are available through the free and open-source VTK software package.

You can use the VTK library directly to convert data to VTK format, or you can use one of the many third-party solutions that are readily accessible.

We can use it once we transform data into VTK format in any volume rendering program like VMD or ChimerX.

Resampling the data to a regular grid is the next stage in preparing it for volume rendering. Hence, It is essential since most volume rendering methods presume the data is on a regular grid.

You can use one of the numerous resampling methods to resample the data to a regular grid. The VTK library offers resampling methods, including trilinear and closest neighbor interpolation.

The volume rendering software package you are using should be compatible with the data type of the data.

For instance, specific software programs for volume rendering only allow floating-point data. It is necessary to normalize the data set.

In other words, the data values need to be scaled to a particular range, like [0, 1] or [-1, 1].

So in this way, we can display data more accurately. A high-quality image we can get out of it if the data resolution is sufficient. The data resolution shouldn't be so excellent that it slows the volume rendering procedure.

Set up the camera.

Setting up the camera is the subsequent stage. So, choosing the camera's position, orientation, and field of view. The camera positioning within the 3D data volume is the camera position.

Therefore, three coordinates (x, y, and z) can help specify the camera's position. The camera's orientation is set by where it is pointed. Pitch, yaw, and roll are the three angles that can help specify the camera orientation.

The angle at which the camera sees is the field of view. Degrees can help determine the field of view. The data volume, for instance, will be visible from the side if the camera turns 90 degrees.

The image will take on different looks depending on the camera's position, orientation, and range of view. For instance, putting the camera nearer the data volume will result in a closer-cropped image.

It is crucial to take the following factors into account when positioning the camera for volume rendering:

What do you expect the rendered image to show? Do you want to view the complete volume of data or just a portion of it?

What angle do you want to take? You like to view the data volume from the top, the side, or another perspective.

Do you wish to display how much of the total data volume? Do you want to display all or only a portion of the data?

Cast rays through the data volume

The core operation of volume rendering is casting rays through the data volume.

A ray is cast through the data volume traces from the camera to the image plane for each output pixel—also, the data samples at each point along the ray.

The data at each sample point determines the pixel's color and opacity. So they are then composited to create the final image.

The coordinates of the pixel in the final image are the camera coordinates. The coordinates of the point in the 3D data volume corresponding to the pixel are the world coordinates.

A ray tracing algorithm follows the ray as it moves through the data volume.

Ray tracing algorithm may impact the performance and quality of the volume rendering process.

The Whitted and Pathtracer algorithms are two popular ray tracing techniques.

Compute the color and opacity of the voxel at each sample point.

A transfer function and the data at each point along the ray that was cast through the data volume help determine the color and opacity of a pixel in a volume-rendered image.

The transfer function maps color and opacity values to the data values.

The transfer function is capable of helping highlight particular features in the data volume or produce a variety of other visual effects.

For example, the transfer function can make high-density voxels appear brighter and more opaque than low-density voxels.

It can emphasize high-density features in the data volume, such as bones or malignancies.

Applying various colors to distinct data values is also possible using the transfer function.

It helps to differentiate between several feature categories within the data. An example of coloration might be muscles blue, tumors red, and bones white.

Surface Rendering Using GPU

The Beauty Of Bacteria 3D Surface Rendering Of Blue Bacterium On Red Surface In Detailed Microscopic View Background.


One method for visualizing a 3D object's surface is surface rendering. Visualizing scientific data, such as fluid flow simulations and climate models, as well as medical pictures, like CT and MRI scans, is a typical use.

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The surface rendering process involves creating a triangular mesh of the object's surface. Therefore, A graphics processing unit is then helpful in rendering the triangular mesh (GPU).

GPUs are computers that are exceptionally ideal for surface rendering. Moreover, GPUs can quickly and efficiently produce and draw massive triangular models.

Here are some examples of how surface rendering visualizes microbial structures:

  • Visualizing the structure of viruses: Surface rendering can be helpful to visualize the structure of viruses, such as HIV and the SARS-CoV-2 virus. This can help scientists to understand how viruses work and develop new drugs and vaccines.
  • Visualizing the structure of bacteria: Surface rendering can be helpful to visualize the structure of bacteria, such as E. coli and Salmonella. So, It can help scientists understand how bacteria interact with each other and their environment.


Here is an explanation of the steps of the surface rendering process:

Generate a triangular mesh of the surface of the object.

Using marching cubes and isosurface extraction, we can produce a triangular mesh of an object's surface from a volumetric data set.

An algorithm marching cubes divides the volumetric data set into a grid of cubes.

Moreover, the program determines if a cube contains the object's surface for each cube. The algorithm creates a triangular mesh of the surface inside the cube if the surface exists.

An algorithm isosurface extraction locates a scalar field's isosurface to function. Collecting all scalar field points with the same value is an isosurface.

Before creating a triangular mesh, the program finds every voxel in the volumetric data set on the isosurface. The program creates a triangular surface mesh after identifying the voxels on the isosurface.

Triangular mesh generation may be achieved quickly and efficiently using marching cubes and isosurface extraction algorithms.

On the other hand, Marching cubes are more versatile. Whereas isosurface extraction is more meant explicitly for extracting isosurfaces from volumetric data sets.

After the generation of the triangular mesh, a central processing unit (CPU) or graphics processing unit (GPU) can help render it.

Although we can render triangular meshes by CPUs on devices without a GPU, GPUs are usually faster than CPUs.

Transfer the triangular mesh to the GPU.

An OpenGL or Vulkan-style graphics API moves the triangular mesh to the GPU. These APIs support vertex and index buffer creation and transfer to the GPU.

A vertex buffer is a buffer to hold the triangular mesh's vertex data. Moreover, each vertex's position, normal, and color are included in the vertex data.

The vertices' indices comprising each triangle in the mesh are stored in an index buffer.

To transfer the triangular mesh to the GPU, the following steps are performed:

  1. Create a vertex buffer on the GPU.
  2. Copy the CPU's vertex data to the GPU's vertex buffer.
  3. Create an index buffer on the GPU.
  4. Copy the CPU index data to the GPU's index buffer.

Here is an example of how to transfer a triangular mesh to the GPU using OpenGL:

// Create a vertex buffer object. GLuint vertexBuffer; glGenBuffers(1, &vertexBuffer); glBindBuffer(GL_ARRAY_BUFFER, vertexBuffer);

// Copy the vertex data from the CPU to the vertex buffer object. glBufferData(GL_ARRAY_BUFFER, sizeof(vertices), vertices, GL_STATIC_DRAW); 

// Create an index buffer object. GLuint indexBuffer; glGenBuffers(1, &indexBuffer); glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, indexBuffer);

// Copy the index data from the CPU to the index buffer object. glBufferData(GL_ELEMENT_ARRAY_BUFFER, sizeof(indices), indices, GL_STATIC_DRAW);

This is merely a simple illustration of using OpenGL to move a triangular mesh to the GPU.

Hence, more intricate instances could entail employing other graphics APIs or numerous vertex or index buffers.

Render the triangular mesh on the GPU.

The triangles appear on the screen in a particular order throughout the rendering process.

Therefore, the triangle rasterization order refers to the arrangement of the triangles.

Also, the triangles' depth dictates the triangle rasterization sequence. First to be drawn are the triangles nearest to the camera.

The coordinate system to find the 3D object is world space. The coordinate system used to display the rendered image is screen space.

The screen region where the rendered image is visible is the viewport. Removing any triangle segments outside the viewport is clipping.

The triangle turns from a geometric primitive into a collection of pixels on the screen through rasterization.
Finding the color of each triangle's pixel is a process of shading.

Display the rendered image.

The GPU employs a technique of frame buffering to display the produced image on the screen via a monitor.

The technique of saving the image onscreen in a frame buffer—a memory buffer frame buffering. Hence, the monitor reads the frame buffer and shows it on the screen.

There are usually two framebuffers on a GPU: a frontbuffer and a backbuffer. The framebuffer that is now visible on the screen is the frontbuffer.

The framebuffer that the GPU is now rendering to is the backbuffer.

Therefore, Upon rendering the triangular mesh, the GPU switches the front and rear buffers. The rendered image appears on the screen as a result.

The rendered image appears on the screen by repeatedly running the framebuffering procedure.

The most popular software  for GPU-based 3D visualization of microbial structures

VMD (Visual Molecular Dynamics)

VMD molecular scenes with depth-of-field focal blur,
ambient occlusion lighting and shadows, rendered with the
interactive ray tracing engines built into VMD.

A free and open-source software program, VMD (Visual Molecular Dynamics), can visualize various molecular and biological data.

Moreover, it also includes microbiological structures in three dimensions. VMD supports surface rendering and volume rendering.

VMD employs several methods to render volume, such as isosurface extraction, marching cubes, and ray casting.

In addition, VMD offers other capabilities, such as clipping planes and transfer functions, for managing the appearance of volume-produced images.

VMD performs surface rendering using various methods, such as point clouds and triangle meshes.

In addition, VMD has several options for adjusting surface-produced image appearance, including lighting, shading, and texturing.

It is an effective and adaptable biological and molecular data 3D visualization tool.

Researchers and scientists use it in various domains, such as molecular biology, chemistry, and biophysics.


The ChimeraX window.

For 3D visualization and analysis of macromolecular structures, such as proteins and nucleic acids, ChimeraX is a free and open-source software program.

Moreover, surface rendering and volume rendering are available by ChimeraX. ChimeraX is an effective and adaptable macromolecular structure investigation and 3D visualization tool.

Therefore, ChimeraX is a valuable tool for viewing the structure of proteins, including antibodies and enzymes. Understanding how proteins function can aid scientists in developing novel medications and treatments.

Nucleic acid structures, like DNA and RNA, can be seen using ChimeraX. It can help understand how nucleic acid scientists store and transmit genetic information.

Macromolecular complexes are collections of two or more macromolecules that interact.

ChimeraX can visualize the structure of these complexes. Moreover, It can aid in understanding how macromolecules cooperate to carry out biological functions by scientists.


A commercial software program, PyMOL, is available for 3D molecular structure investigation and visualization. PyMOL supports surface rendering and volume rendering.

Therefore, we can visualize structurally Protein-ligand complexes, collections of two or more molecules interacting using PyMOL. This can aid in understanding how medications bind to proteins.

Moreover, it also aids in developing novel, less hazardous, and more potent drugs. VMD and ChimeraX are free, open-source software programs with a more constrained feature set than PyMOL.

It is a commercial software package with more features. Your ideal tool will rely on your spending limit and particular requirements.


AVIZO was especially for biological data visualization in three dimensions. Various biological data can be visualized using this robust and adaptable software program.

The structure of tissues and organs, including the liver, heart, and brain, can be seen with AVIZO. This can aid in understanding the growth and operation of tissues and organs by scientists.

With AVIZO, one can see the architecture of cellular components such as the Golgi apparatus, mitochondria, and nucleus. Scientists may use this to understand better how cells function and create novel treatment approaches for illnesses.

A robust and adaptable tool for visualizing biological data is AVIZO. Therefore, Researchers and scientists in various disciplines, such as neuroscience, immunology, cancer, and microbiology, employ it.

Future of GPU-based 3D visualization of microbial structures

The subject of GPU-based 3D visualization of microbiological structures is expanding quickly and has a promising future.

Due to their rising power and affordability, a larger spectrum of consumers may now afford and use GPUs.

Furthermore, new software tools that facilitate the usage of GPUs for 3D visualization are under development. Developing and testing novel medications targeting microbial structures may be possible using GPU-based 3D visualization.

Therefore, It may produce fresh, more potent remedies for viral illnesses. Developing and evaluating novel vaccinations that defend against specific microbial structures using GPU-based 3D visualization may be possible.

It may result in the creation of vaccinations against infectious diseases that are more effective.

The study of the interactions between microbial populations and their surroundings may be accomplished through GPU-based 3D visualization.

Therefore, it may contribute to a deeper comprehension of the roles played by microbial communities and how illnesses and health influence people.


GPU-based 3D visualization is a valuable tool for visualizing microbiological structures. It offers a variety of advantages over typical CPU-based 3D visualization, including performance, scalability, and realism.

Numerous disciplines, including microbiology, immunology, cancer, and neurology, use GPU-based 3D visualization.

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Moreover, Microbial structures at various scales, from individual cells to whole communities, can be seen using it.

The topic of GPU-based 3D visualization is expanding quickly and has excellent prospects.

More users can use GPU-based 3D visualization as new software tools are developed, and GPUs grow in power and affordability.

GPU-based 3D visualization is a solid and adaptable tool for seeing microbiological structures.

It could completely change how we investigate microbial systems and result in novel neuroscience, cancer, immunology, and microbiology findings.

If you want to know more about GPU applications in other sectors of the pharmacology and microbial field, then visit the following articles.

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