Making invisible, visible

Associate Professor of Computer Science Ivan Viola.

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Early in Ivan Viola’s career he was fascinated by how we are able to model and visualize incredibly complex systems, bigger than the mind can imagine or smaller than the eyes can see.

“I realized that biology presented perhaps the biggest challenge for visualization,” Viola said.

At the outset, computer-generated biological models don’t offer immediate solutions like new drug targets or therapeutic approaches to diseases. But they offer something else, a near-universal benefit: the language of understanding what we see.

Visualization and understanding

As bioscience and medicine progress, we are now able to live with diseases, which were once a death sentence. And we are rapidly discovering new interactions at the microscopic level that give us insight into the engines of life. However, as research gets more complicated, the layperson gets left further and further behind. Even those who trust the work of scientists can feel overwhelmed when attempting to read the latest research.

Take the SARS-CoV-2 virus as the most recent example. The “spike” protein, a structure that funnels the virus’ RNA into host cells, became a headline target as a possible point to cut off the function and spread of the virus. But the average person likely can’t conceptualize how this process of transmission would actually look, from capsid to host cell. And a biomedical researcher, who would grasp the concept more easily, wouldn’t be able to see the entire process at a molecular level.

“Existing models may only show a part of the picture: the spike protein, or the capsid enclosing the RNA, or the N-protein that bundles the RNA,” said Viola. “Without a complete visualization, it can be difficult to conceptualize how the SARS-CoV-2 virus is structured and how it works.”

Nanovisualization Research Group

That’s why the KAUST Nanovisualization Research Group (NANOVIS) partnered with Scripps Research, TU Wien, and Nanographics to build a model to visualize the first-ever complete SARS-CoV-2 visualization, accurate to the molecular level.

The group’s model stands on the shoulders of immense volumes of research and data from sources worldwide, and in close collaboration with KAUST partners. Even though it still presents a grave threat to humankind, the novel coronavirus is rapidly becoming one of the best-understood viruses of today.

“What’s novel about the platform is its speed of delivery,” Viola said. “If the research is strong and the biology is well understood, we could theoretically model a biological object in an afternoon. We can achieve this by making rules for how chemicals behave in the structure, then replicating those rules to create a model that can render much quicker than using conventional 3D modeling methods.”

“Nature is entropic, but it’s also redundant. If we know a combination of chemicals forms a surface, we can write a set of rules to render how they are structured and generate a complete surface through repetition,” Viola said.

The limits of understanding

Modeling SARS-CoV-2 wasn’t the first project at NANOVIS. The group initially set out to build a model of the mitochondrion, the energy factory of the eukaryotic cell. But as the pandemic swept across the earth, the team knew they had to pivot.

“Just as we’ve modeled the virus, we will model the mitochondrion, and we’ll explore new structures to model – other organelles, or even whole cells,” Viola continued.

Viola and his team are able to work quickly and accurately because the building blocks of their visualizations can be replicated for future work. They can copy an entire strand of DNA, for example, and then re-sequence the nucleotides to match the appropriate genetics. In this way, pre-built organelles can be copied into models of many other cells in the body.

A new age of biological knowledge is coming, and it will be more visual than ever. Our technology is up to the task. “And I look forward to being able to model life again, rather than threats to it,” said Viola.

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/KAUST Release. View in full here.