Pathways to the new biology
By Mark Wanner
What does biological research look like?
For some, the first mental image may be of Jim Fowler wrestling a large animal better left alone on “Wild Kingdom.” Or perhaps a lab bench with test tubes, pipettes and Petri dishes comes to mind.
If so, the lab setup used by many researchers today would be a surprise: a workstation with a powerful computer and a large monitor. Oh, and probably a large white board covered with a mix of symbols and letters impossible for non-specialists to decipher.
These are the labs of bioinformatics and computational biology researchers, a growing legion who work in “dry” labs to analyze vast amounts of data, much of it produced by scientists in more traditional (“wet”) labs. Every time a mammalian genome—all the DNA present in each cell—is sequenced, the output contains roughly 3 billion or more data points. Not only that, but to fully understand it, scientists have to look far beyond linear sequences to as many as ten dimensions of interactions: between genes and proteins, genes and genes, genes and the environment, epigenetic changes, and so on. Making sense of it all takes skills far removed from anything “Wild Kingdom” ever dished up.
It’s also vitally important to medical progress. Much of the research planned for The Jackson Laboratory for Genomic Medicine in Connecticut will take place on computers, as researchers there look for information in our genomes that will help doctors better treat complex human diseases.
Computational biology and bioinformatics are such a new scientific disciplines that many of today’s researchers pretty much made it up as they went along. Handling and analyzing such vast datasets puts unprecedented stresses on both equipment infrastructure and intellectual creativity. So how did these pioneers enter and develop the nascent fields? And what about today’s students? Math, computer science and physics students may find themselves drawn to solving biological problems in ways they’d never imagined.
If so, they’ll find a wealth of opportunity, both in demand for their skills and in the numbers of problems remaining to be solved. There’s a term for them—bioinformagicians—that is usually said in jest but that nonetheless reflects both their value and the demands placed upon them. Translating “big” data into understanding and actionable knowledge is one of the most potentially valuable and most difficult tasks facing biological science in the next decade.