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The 20th Century's hallmark inventions were qualitatively different from earlier achievements. The difference was complexity. No single human could possibly live long enough to design a moon rocket or an integrated circuit. By necessity, the work had to be done in teams. In some cases, the relevant science had been available since Victorian times. The big challenge was in finding ways to turn the labor of hundreds, and |
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then thousands, and finally hundreds of thousands of engineers into nearly perfect artifacts.
Anyone who's ever read Dilbert knows that it wasn't pretty. The new organizations were hierarchical and inefficient. The problem was, nobody could think of an alternative. It didn't really matter whether the aircraft designers were in Santa Barbara or Minsk. The long rows of drafting boards and (later) computer terminals looked pretty much the same. At first, nobody thought that large operating systems would be any different. After all, Microsoft created Windows the way Boeing designed airliners. Operating systems were just the latest in a long line of complex inventions.
The important thing about open source is that it showed that at least one complex invention -- computer software -- could be organized a second way.
(Maybe we shouldn't have been so surprised. As MIT Prof. Eric von Hippel points out in Democratizing Innovation, user groups shared inventions in industries ranging from machine tools to windsurfing throughout the Twentieth Century. Indeed, the process is as old as folklore. People say that The Odyssey is nearly perfect. That's only partly because of Homer. Audiences and bards spent hundreds of years tweaking Homer's epic before anyone thought to write it down. Sounds a lot like open source.)
The big question now is whether open source is limited to computing. It would be nice if the the alternative to Dilbert worked for something besides software. So far, the term "open source biology" has usually meant collaborations that either (a) write computer software, or (b) publish data without claiming intellectual property rights. However, the former is no different from other forms of open source software, while the latter is indistinguishable from biology as it was practiced down to the 1970s. Neither usage captures the essential feature of open source -- large, non-hierarchical groups working together to create specific products. All of which begs the question, "What would it look like?"
Perhaps the most natural place to use open source ideas is in computational biology. Since the 1940s, scientists have known that the DNA was a form of natural software. Prior to the Human Genome Project, this observation was more colorful than useful. Today, however, biologists can search for the proteins that controlled disease -- and even discover drug candidates -- while sitting at a computer terminal. The basic task -- finding tiny features hidden in an ocean of code -- is not much different from de-bugging a large operating system. It is therefore natural to think that the "many eyeballs" methods used to make products like LINUX could be effective in this environment also. ITHS is participating with collaborators at UC Berkeley, UCSF and Duke University to create the world's first open source drug discovery collaboration. The new entity -- called the Tropical Disease Initiative or "TDI" -- would focus on supply urgently needed genomic knowledge for diseases that afflict the developing world. The original proposal by Maurer, Rai & Sali(2004) has been widely discussed and will be the subject of a three-day workshop in May 2005. A pilot project is currently under construction in collaboration with Synaptic Leap.
A second place where open source biology might take root is chemistry. Unfortunately, chemistry experiments are expensive. This makes open source chemistry collaborations fundamentally different from groups like LINUX, where volunteers need little more than a computer terminal and access to the Internet. Maurer, Rai & Sali (2004) have pointed out that volunteers may be able to evade this problem by "scrounging" materials purchased under existing grants. Benkler (2004) has added the further suggestion that experiment results could be published using GPL licenses. The main point is that using scrounged materials would restore the economic environment of open source by making experiments effectively costless to volunteers. The extent to which funding agencies are willing to tolerate scrounging is, of course, a separate question. A more ambitious proposal might be for agencies to fund an entire community of open source researchers in much the same way that they currently fund Big Science experiments like atom smashers or the Alliance for Cell Signalling.
MIT professor Eric von Hippel argues that open source methods are an ideal way to collect and analyze patient data for "off-label" extensions of existing drugs. This is a natural environment for open source volunteers because the healthcare system pays for the research inputs (treating physicians, drugs) in the course of treating patients. Indeed, the use of volunteers may be more attractive than current systems which pay researchers to collect data, which creates incentives to fabricate "data" designed to please sponsors.
Because it is limited to post-approval drugs, von Hippel's idea is inherently limited. In the long run, however, it is worth asking whether large pharmaceutical companies could one day decide that it was in their interest to support open source clinical trials before FDA approval. Because open source methods tend to be unusually transparent, regulators could well decide that open source trials were inherently more persuasive than traditional programs.
Papers
S. Maurer & S. Scotchmer, "Open Source Software: The New Intellectual Property Paradigm," in T. Hendershott (ed.), Handbook on Information Systems (Elsevier: forthcoming 2006)
S. Maurer, A. Sali & A. Rai, “Finding Cures for Tropical Disease: Is Open Source the Answer?,” Public Library of Science: Medicine 1:56 (2004).
S. Maurer, “New
Institutions for Doing Science: From Databases to Open Source Biology,“
conference paper, University of Maastricht, November 2003.
Y. Benkler, Commons-Based Strategies and the Problem of Patents, Science 305:1110 (2004).
K.
Cukier "
An Open Source Shot in the Arm? " The Economist (June 10, 2004).
E. Von Hippel, Democratizing Innovation (MIT Press 2005)
A. Huang & C. Weber, “The Health of Nations: Open-Source Research and the Economics of Life and Death in the Developing World,” Berkeley Science Review 7:45 (Fall, 2004).
N.
Johnson, "Steal
This Genome," East Bay Express
Links
Duke University Workshop on Collective Computational Biology (May 23-25 2005)
The Synaptic Leap (Open Source Biology Project)
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