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 a disruption in health care, in which people will shift their thinking toward wellness. Instead of roughly saying eat less, exercise more, people will be able to quantitatively measure over time how they are doing in far greater detail than anything you can get by stepping on a bathroom scale every day.
“It will force every sector of the healthcare industry to fundamentally change their biz plan over next 10 years,” Hood said.
This change can’t happen soon enough, Lazowska said. He talked about how he rode his bicycle over to the OVP summit at the Four Seasons Hotel in downtown Seattle, and it bugs him that he can’t really get a good measurable sense of how that activity affects his health compared with, say, driving there. In other words, medicine has a long way to go before IT can start driving exponential growth like it has for other fields, Lazowska said.
“It bothers me a lot that my car is a much better instrument than I am. You can go to a mechanic and he’ll plug a diagnostic tool in, and it will tell you everything that happened in the last six months,” Lazowska said. “On the rare occasion when I visit a doctor, it starts with something like ‘Where does it hurt?”
The movement toward evidence-based medicine needs to start helping physicians translate all this data into actionable knowledge. It’s like “machine learning for medicine, telling you what works and what doesn’t work.” The way entrepreneurs could think of this, Lazowska said, is by coming up with tools to provide “cognitive assistance for physicians. There’s been surprisingly little progress in helping doctors with what they should do in a situation. There’s a huge amount of work to do there.”
Smarr used a personal anecdote to talk about the opportunity he sees in businesses that can enable a revolution in wellness. He is personally keeping track of 30-40 parameters in his blood on a regular basis, sharing the information with his doctor, and using that information to adjust the type of food he eats and exercise he does, to stay healthy. This takes a lot of time and effort now. But when nanotech advances enough to make it possible to capture that sort of data in a fingerprick of blood on a handheld device, look out. The way Smarr talks, this will be way more attractive than just asking everybody to pop the latest weight loss pill.
“The counter-revolution to obesity is centered there. People will be able to tune their bodies,” Smarr said.
By posting a comment, you agree to our terms and conditions.