The human genome folds into a computer program for life

Software application developers have long sought to manipulate and harness the processing power at their disposal in order to get the most out of the applications and systems they seek to program. In this post-millennial era of “application enlightenment”, a huge percentage of us know that the “brain” of a computer is called the central processing unit or CPU for short. So when programmers need something faster and more powerful than a processor, where do they go?

Software developers working on geospatial intelligence systems, biomolecular research programs, scientific research, and (perhaps surprisingly) high-end computer games will often look to use a graphics processing unit (GPU) in addition to their CPU power.

How GPUs Speed ​​Up Computers

Sometimes referred to as High Performance Computing (HPC), this type of software development relies on the GPU’s ability to manipulate and change a computer’s calls to memory at extremely high speeds. This allows the machine to “speed up” the creation, delivery and presentation of image-related data that will eventually be displayed on the screen. Simply put, this means we can get computers to do some really cool and often really insightful things. So like what?

Mapping the structure of a human’s genome involves a lot more graphics power than Space Invaders ever did, so GPUs do the trick. The human “genetic instruction manual” contains approximately three billion base pairs of DNA. While the genome of every cell in the body is the same, each type of cell must be different to serve its specific purpose, such as growth, fighting disease, creating hormones, or one of hundreds of thousands of people. ‘jobs.

So how are the individual cells in your body programmed to do the right thing on any given day? Developers and researchers used Nvidia GPUs to determine what appears to be the answer.

The human genome folds into 10,000 loops

Researchers at Baylor College of Medicine, Rice University, MIT, and Harvard University used Nvidia Tesla GPUs to “plan over time” how the human genome folds up to form a set of computer instructions. from which our own body functions. They created the first high-resolution 3D maps of entire folded genomes and discovered that the human genome is folded into approximately 10,000 loops.

These form when two separate and distant pieces of DNA come into contact in the folded version of the genome in the nucleus of a cell. By folding the genome into different shapes, genes can be turned on or off, allowing cells to perform a wide range of functions.

“We were faced with a real challenge because we were asking ourselves, ‘How does each of the millions of pieces of DNA in the database interact with each of the other millions of pieces? “Said Miriam Huntley, a doctoral student at Harvard’s School of Engineering and Applied Sciences. “Most of the tools we used for this article were created from scratch, because the scale at which these experiments are performed is so unusual. The team soon realized that the processors weren’t powerful enough to do the job. “

Researcher Suhas Rao explained that ordinary computer processors are not well suited for the task of loop detection. To find the loops, the team had to use GPUs, processors typically used to produce computer graphics. This new information could provide new clues about how cells are functioning as well as new approaches to fight cancer and other complex diseases.

How to understand CPU vs GPU

Understanding how a processor works differently from a processor is pretty straightforward, according to Nvidia.

“An easy way to understand the difference between a CPU and a GPU is to compare the way they handle tasks. A CPU consists of a few cores optimized for sequential serial processing, while a GPU has a massively parallel architecture made up of thousands of smaller, more efficient cores designed to handle multiple tasks simultaneously.

Now you know how GPUs work, the folded DNA structures inside the human genome, and the really high-end versions of Space Invaders.

Gordon K. Morehouse