|From:||Ali Hashemi <ali.hashemi+ontolog@xxxxxxxxxxx>|
|Date:||Tue, 29 Sep 2009 21:42:21 -0400|
Anyone hear of this? Does anyone know if any ontologists were included on the project? Or how well are the semantics of the terms defined?|
Le Novère and 38 scientists from across five continents have developed a standardized language for biological diagrams called the Systems Biology Graphical Notation, or SBGN. With the first phase of the four-year effort completed, the scientific community must now assess the functionality and design of SBGN and decide whether or not to adopt the new language.
In 2005, Le Novère and his colleagues, Michael Hucka at Caltech and Hiroaki Kitano at the Systems Biology Institute in Tokyo, recruited computer scientists, biochemists, and modelers working in systems biology to begin developing SBGN. They approached the project with a simple philosophy: Design a biological language that is basic, clear, and can be processed by a computer.
The result was three languages to describe molecular processes, relationships, and the flow of activity through a system. Besides being complementary, the languages are also efficient; combined, they use only about 50 symbols.
“Once people learn the symbols and grammar they will be able to share biological pathways in the same way musicians share music,” Le Novère says. “An American pianist, a European pianist, and a Chinese pianist can all read and interpret the same sheet of Mozart.”
King sees the efforts of the SBGN team as indicative of a larger trend within biology. “There is a movement right now to increasingly formalize biological knowledge,” he says. “Experiments are getting so complicated and there are many things being done and recorded that we want to reuse it in efficient ways.”
Appropriately, the growth of systems biology has required scientists to step outside the traditional bounds of their research to reconsider how they communicate with one another. It remains to be seen whether or not SGBN will become the standard language for biology, but it seems clear that the efforts of its developers were not in vain. Their work represents a significant advancement in our understanding of how to articulate, formalize, and present the complex biological ideas that emerge from the unprecedented amounts of data researchers produce—and then must confront—today.
Follow link for full article..
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