Q: How did the universe form?
For thousands of years, humans have looked to the night sky and created myths to explain the origins of the planets and stars. The real answer could soon come from the elegant computer simulations conducted by Tom Abel, an associate professor of physics at Stanford.
Cosmologists face an ironic conundrum. By studying the current universe, we have gained a tremendous understanding of what occurred in the fractions of a second after the Big Bang, and how the first 400,000 years created the ingredients – gases, energy, etc. – that would eventually become the stars, planets and everything else. But we still don’t know what happened after those early years to create what we see in the night sky.
“It’s the perfect problem for a physicist, because we know the initial conditions very well,” says Abel, who is also director of the Kavli Institute for Particle Astrophysics and Cosmology at SLAC. “If you know the laws of physics correctly, you should be able to exactly calculate what will happen next.”
Easier said than done. Abel’s calculations must incorporate the laws of chemistry, atomic physics, gravity, how atoms and molecules radiate, gas and fluid dynamics and interactions, the forces associated with dark matter and so on. Those processes must then be simulated out over the course of hundreds of millions, and eventually billions, of years. Further complicating matters, a single galaxy holds one billion moving stars, and the simulation needs to consider their interactions in order to create an accurate prediction of how the universe came to be.
“Any of the advances we make will come from writing smarter algorithms,” Abel says. “The key point of the new facility is it will allow for rapid turnaround, which will allow us to constantly develop and refine and validate new algorithms. And this will help us understand how the very first things were formed in the universe.” —Bjorn Carey //
Q: How did we evolve?
The human genome is essentially a gigantic data set. Deep within each person’s six billion data points are minute variations that tell the story of human evolution, and provide clues to how scientists can combat modern-day diseases.
To better understand the causes and consequences of these genetic variations, Jonathan Pritchard, a professor of genetics and of biology, writes computer programs that can investigate those links. “Genetic variation affects how cells work, both in healthy variation and in response to disease,” Pritchard says. How that variation displays itself – in appearance or how cells work – and whether natural selection favors those changes within a population drives evolution.
Consider, for example, variation in the gene that codes for lactase, an enzyme that allows mammals to digest milk. Most mammals turn off the lactase gene after they’ve been weaned from their mother’s milk. In populations that have historically revolved around dairy farming, however, Pritchard’s algorithms have helped to elucidate signals of strong selection since the advent of agriculture to enable people to process milk active throughout life. There has been similarly strong selection on skin pigmentation in non-Africans that allow better synthesis of vitamin D in regions where people are exposed to less sunlight.
The algorithms and machine learning methods Pritchard used have the potential to yield powerful medical insights. Studying variations in how genes are regulated within a population could reveal how and where particular proteins bind to DNA, or which genes are turned on in different cell types – information that could help design novel therapies. These inquiries can generate hundreds of thousands of data sets and can only be parsed with up to tens of thousands of hours of computer work.
Pritchard is bracing for an even bigger explosion of data; as genome sequencing technologies become less expensive, he expects the number of individually sequenced genomes to jump by as much as a hundredfold in the next few years. “Storing and analyzing vast amounts of data is a fundamental challenge that all genomics groups are dealing with,” says Pritchard, who is a member of Stanford Bio-X. “Having access to SRCC will make our inquiries go easier and more quickly, and we can move on faster to making the next discovery.” —Bjorn Carey //
