The fabled convergence of information technology and biology has been going on for a long time, but merging these fields ain’t easy. Chad Waite, a managing director with OVP Venture Partners in Kirkland, WA, knows this all too well, even though he has a megahit on his resume with Rosetta Inpharmatics, a computational biology company that sold for more than $600 million almost a decade ago.
“My partners said, you gotta do more of those,” Waite said today on panel discussion at OVP’s annual tech summit in Seattle. “I said, you mean sell a company with no revenue for $600 million? OK, sure.”
Sounds pretty far-fetched in a post-downturn world, right? Nevertheless, anybody who can iron out some of the massive inefficiencies that exist in health IT will be on the road to riches, according to a stellar panel of scientists at OVP’s tech summit. And to hear them talk, the inefficiencies that entrepreneurs are up against are truly mountainous.
To Leroy Hood, the biotech pioneer and president of the Institute for Systems Biology, the U.S. “regulatory jungle,” with institutionalized review boards is a major barrier keeping people from getting ahold of samples for genomic analysis. To Ed Lazowska, a computer science professor at the University of Washington, crunching the genome for personalized medicine is being held back by the fragmentation of computing into individual labs. The lack of central corporate or university computing centers has created barriers to sharing, and wasteful overhead that needs to be solved by cloud computing, Lazowska said. And Larry Smarr, director of the California Institute of Information Technology and Telecommunications (Calit2) at UC San Diego, said biologists, physicians, and computer scientists rarely pool their brainpower in productive ways to tackle problems this hard. Even if you herd all those cats, universities often lack the optical fiber infrastructure that’s needed to move around massive datasets on genomes from one building to another at high speed.
Challenges this big are going to require governments to think about biology in terms of “Big Science” that require big collaborations, and that draw big checks, instead of the current model—in which a lone researcher gets a little money for a bright idea, Hood said.
“You can continue to act like independent Brownian particles, but it won’t get you anyplace,” Hood said.
This talk certainly generated a lot of food for thought for scientists and entrepreneurs pursuing what OVP likes to call “digital biology.”
The data challenge that’s coming, as OVP’s Mark Ashida put it later, is really too much to fathom at the moment. He’s started hearing the term Yottabyte, pronounced “Yoda-byte” which is essentially a term for incomprehensible piles of data points, “not a green creature from Star Wars,” Ashida said. This is the kind of data scientists will grapple with if you think about how each individual has billions of datapoints in a genome, with snapshots on how those genes are expressed every six months to track our wellness over time, multiplied by more than 300 million individuals in the U.S. Then add in data from the genome, the proteome, the metabolome, and other various-omes, and you start getting into that kind of Yottabyte territory Ashida mentioned.
The data explosion in biology is bringing about
