Comments on: Petabyte Age Wiredesque lesson on what science can learn from Google

By: Who could make sense out of mountains of scientific data ? | brainhealthhacks.com

Mon, 23 Jun 2008 17:23:35 +0000

[...] at PIMM, Attila in his blog discusses a feature article in Wired magazine and offers a potential solution to this data [...]

By: Ward

Ward — Mon, 23 Jun 2008 14:10:28 +0000

Attila,

very interesting post. Ever since reading your first post regarding the potential Google has in the science/health field I agreed with you – because I had similar thoughts. The data housing and mining of this data with Google’s infrastructure and brain power could lead to valuable insights and lead to a whole new field of science (even a paradigm shift).

I will have to run out and get the new Wired magazine.

ps GlaxoSmithKline just released a huge data base of cancer cell line data (http://blog.wired.com/wiredscience/2008/06/massive-cancer.html)

brainhealthhacks.com

By: Jim H

Jim H — Mon, 23 Jun 2008 11:40:05 +0000

I find it interesting that this concept appears diametrically opposed to the DIYbio movement: generation of petabytes of computational power will require enormous wealth. I understand the idea of pulling this power through pooled resources, but the necessity of a massive, centralized processor seems insurmountable.

Am I missing something?

By: Jon Rowley

Jon Rowley — Mon, 23 Jun 2008 10:44:00 +0000

I haven’t read my Wired yet, but I loved the cover and have been thinking about it since I got it – of course with respect to biotechnology. There has definitely been a trend in biotechnology towards screening-type experiments and away from individual hypothesis driven experiments. There are arguments both ways that high throughput screening has led to the Demise of Big Pharma, but there are some cool ways to use the technology. I was involved in the first group to apply screening with application towards ‘discovering’ environments to drive stem cell fate which is currently being used internally for BDs cell therapy initiative and for external commercialization(http://www.bd.com/technologies/discovery_platform/screening.asp). While the platform allows for screening hundreds of conditions, there are still a lot of hypotheses being asked per experiment. BD had a 10+ person informatics team to customize the informatics for the 30+ strong biology group – so it was very obvious the importance of computing power to push that forward. I could totally see how that or other platforms could be extended and with infinite resources (~$300M/year?) you could just screen everything and look for what works. While that isn’t happening today, I can see how 10-30 years down the line (where Chris Anderson & Wired tries to ‘predict’) that non-hypothesis driven work will dramatically increase the ‘market share’ of even academic science. With that, there is the potential of a snowball effect, fewer scientists learning how to shape and test hypotheses, more screening/informatics approaches, towards the ‘end of science’ – that is the story that has developed in my head since seeing Wired’s cover. So while the End of Science is far from a reality, it isn’t so far fetched that it won’t happen in this milenium.

By: Deepak

Deepak — Mon, 23 Jun 2008 05:35:13 +0000

I’ll add that there are multiple avenues for Google to be an enabling platform (e.g. android, palimpset, google code, google scholar, etc).

By: Deepak

Deepak — Mon, 23 Jun 2008 05:12:58 +0000

Attila, there is a lot more to biological data than just managing it. What are the biological problems you are trying to solve? What questions are you trying to answer? Do you know how to present the information to different groups in your organization? These are problems that a non-scientist cannot answer. So what we need is a marriage of the two minds. I doubt Google has any interest in being a scientific company per se. It’s hard work and they’ll essentially have to be two companies. IBM has done some good science in its day (some brilliant science actually), but that’s still not it’s core business and shouldn’t be.

What we need is the mindset, and the realization that we need to think about new ways of managing data and making the results available to scientists and decision makers. We need to start thinking about distributed computing paradigms, but those are thing science has always learned from the tech industry. We’ve just been too slow doing that.