Processing cycles battling deadly diseases
October 6th, 2008Cancer, as many of you already know, is the unrestrained development of certain cells in the organism. The disease, according to its Wikipedia entry, takes millions of lives every year:
Cancer may affect people at all ages, even fetuses, but the risk for most varieties increases with age.[1] Cancer causes about 13% of all deaths.[2] According to the American Cancer Society, 7.6 million people died from cancer in the world during 2007.[3] Cancers can affect all animals.
AIDS (Acquired Immune Deficiency Syndrome) is another formidable human killer. Also from Wikipedia:
AIDS is now a pandemic.[4] In 2007, an estimated 33.2 million people lived with the disease worldwide, and it killed an estimated 2.1 million people, including 330,000 children.[5] Over three-quarters of these deaths occurred in sub-Saharan Africa,[5] retarding economic growth and destroying human capital.[6]
The human intellect has been unable to find a cure for these and other lethal maladies. Fortunately, there are lots of people working with unrelenting determination in order to find the solution that’s going to rid us from these illnesses. I’m happy to inform I’ve joined their ranks. Despite what you might think, I’m not studying medicine, nor have I relocated to any research lab or hospital in the US. Rather, I’ve chosen to fight cancer with my own computer. That’s right. Here’s how I’m trying to make a difference: I’m contributing to Standford’s folding@home project.
Here’s the definition of the undertaking, as read from the site’s FAQ section:
The Folding@home project (FAH) is dedicated to understanding protein folding, the diseases that result from protein misfolding and aggregation, and novel computational ways to develop new drugs in general.
Proteins are the basis of how biology gets things done. As enzymes, they are the driving force behind all of the biochemical reactions that make biology work. As structural elements, they are the main constituent of our bones, muscles, hair, skin and blood vessels. As antibodies, they recognize invading elements and allow the immune system to get rid of the unwanted invaders. For these reasons, scientists have sequenced the human genome — the blueprint for all of the proteins in biology — but how can we understand what these proteins do and how they work?
However, only knowing this sequence tells us little about what the protein does and how it does it. In order to carry out their function (e.g. as enzymes or antibodies), they must take on a particular shape, also known as a “fold.” Thus, proteins are truly amazing machines: before they do their work, they assemble themselves! This self-assembly is called “folding.”
Diseases such as Alzheimer’s disease, Huntington’s disease, cystic fibrosis, BSE (Mad Cow disease), an inherited form of emphysema, and even many cancers are believed to result from protein misfolding.
As you can clearly see, protein folding is a big deal. But since these simulations require vast computing resources, it’d be impractical by today’s standards to have a single computer, irrespective of its technical specifications, to crunch all these complicated calculations. Enter distributed computing. The folks at Standford fittingly though of a divide-and-conquer approach to the problem: fragment the computation required for a given protein, disperse it among thousands of computers around the world and aggregate the results as they become available.
Number of active CPUs since the project’s genesis.
Source: folding@home
Of course, this is an overly simplistic idea of what folding@home actually is and how things work (the technical details of how to accomplish this are actually incredibly intricate). Yet, it’s a nice, short introduction for you to get a broad sense of the project.
If you leave your PC, laptop or Playstation 3 powered on for extended periods of time without your direct use, consider donating those idle processing cycles to folding@home. It’ll be for a good cause. If you still need an incentive, please refer to the project’s results page.
