Recorded: 02 Jun 2003
Oh, sure. Anything this interesting will keep evolving in many ways. We're not there yet. I think that we have five years or so where it's important to shepherd it through still. I think it's obviously a much richer community, much broader community, a very, very good community. But I still think we're not all the way there. We've got to get it to the point where it's just irreversible that everybody is able to make use of-now we have sequences out there but we don't know what it means, we don't have all the elements. We got-we need a tremendous amount of comparative sequence information to figure out what the elements are. We got-you know, there's still a lot of heavy lifting to do to bring this all the way to the Promised Land. We're sort of close to the Promised Land. We can see the Promised Land. But the next five years, there's still enough heavy lifting that we've got to make sure it happens.
If we get there, then it will all take care of itself. But, right now at least, I sort of still see a responsibility for the next five years to get it to that point. Together with now probably four or five dozen people who do that.
So, it's hard to predict. And I can't say I predicted all of what we have now. But by 1995, I think it was becoming clear. I wrote a piece in Science magazine called "The New Genomics: Global Views of Biology." And it was becoming clear then that what this is going to let you do is look at all the pieces simultaneously and that there were going to be new ways of analysis that would let you extract things from the pattern of thirty thousand genes across hundreds of tissues. That you'd begin to put together connections. I didn't anticipate all of the details of how you'd put it together. But this world of learning things by pattern recognition by intersecting data sets, I think by '95 in that article it was already clear that that was going to be here within another decade. That we had to collect much more than just the sequences and the genome. We needed large expression data sets and SNP data sets and this one and that one. Where is it all going to go, but it's pretty clear that biology will be so totally different. The big difference is that in the past the work you would do in your laboratory at your own laboratory bench constituted ninety percent of the crucial data for a paper that you were writing. Now almost every really good paper will be something where the work you did at your bench will be one percent of the data that's in the paper interpreted in the light of a hundred times more data off the web from many, many other things.
It makes an interconnectedness in biology. It says that nature has done all these experiments already. It has done it in nature and evolution. It's done it in cancer. And nature is a more much patient experimentalist than we are. It's just collected all this stuff. What we should do is we should do a few little experiments at our bench and use them really insightfully in the context of this massive information and it's nothing like what biology was. Biology was your experiment, your bench. And now we are all connected to each other. Somebody who follows that model of doing it all by themselves in their lab, I'm sure great things will come out of that from time to time. But I think it will soon become the minority of biologists who aren't setting everything they do in this vast, you know, context of a knowledge base.
Eric Lander earned his A.B. in mathematics from Princeton University (1978) and D.Phil. in mathematics as a Rhodes Scholar at Oxford University (1981).
He first came to the Whitehead Institute as a Whitehead Fellow in 1986, while still an assistant professor of managerial economics at the Harvard Business School and is currently Director of the Whitehead Center for Genome Research and Professor of Biology at MIT. As director of the Whitehead Center for Genome Research, Dr Lander has been one of the principal leaders of the Human Genome Project, contributing 30 percent of the total sequence of the human genome and developing and making freely available many of the key tools used in modern mammalian genomics.
He is a member of the National Academy of Sciences and the American Academy of Arts & Sciences and has been awarded the Beckman Prize for Lab Automation, the Chiron Prize for Biotechnology, and the Gairdner Award for his outstanding contribution to genomic research.
Lander has attended every human genome meeting at CSHL. At the request of Jim Watson, Lander gave his first lecture at the 1986 CSHL symposium on the Molecular Biology of Homo Sapiens.