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Imagine how terrible driving would be if every city in the United States had its own traffic signs, designed with no consideration for those in other cities. New York might use orange squares for stop signs while Boston opts for green triangles. From there it isn’t hard to envision some poor Bostonian weekending in Manhattan, digging his fingernails into his steering wheel as he haplessly runs stop signs and weaves his way through streams of oncoming taxi cabs and delivery trucks.
Luckily, traffic officials understand that standardized symbols go a long way in enabling drivers to effectively share information with one another: The familiar red-and-white octagon has been the only stop sign legally allowed in the US since 1966. But what holds true for complex systems like city streets also holds true for the metabolic pathways that govern the function of our cells. Thanks to efforts spearheaded by Nicolas Le Novère of the European Bioinformatics Institute, systems biology may get its first standard visual language.
Researchers in systems biology combine computer science and biochemistry to understand and model complex living systems, visually mapping the activity of metabolic pathways in order to better understand how they work. Currently, however, they have no set of standards with which to construct these maps, leaving them much like that unfortunate Bostonian careening through downtown Manhattan.
“I’ve been a biologist for the past 20 years, and I was facing this situation all the time,” Le Novére says. “I would contact scientists and ask them to explain the diagrams in their papers, but they couldn’t because they themselves were confused by what they did.”
To address the problem, 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.
It might seem surprising that there has never been a standardized visual language for systems biology, but only relatively recently have standards been needed. The field is at most about 40 years old. It’s hard to determine exactly when it was first established in it’s own right, but historians point to 1968, when Yugoslavian scientist Mihajlo Mesarovic published Systems Theory and Biology, as arguably the earliest possible date.
Since the turn of the century, advancements in computing and molecular biology have enabled researchers to study systems of greater size and complexity than ever before. Tackling these more complex systems requires scientists to form large-scale collaborations with one another—and as a result, they need to begin speaking the same language. “We started being pushed by the rise of new fields of biotechnology such as synthetic biology and by the development of high-throughput technologies that deliver more data every day,” Le Novère says.
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 development of SBG took four years, and there are good reasons for this,” Le Novère says. “We systematically tested all the solutions proposed and hunted down the errors that came about.” The team attempted to negate every potential source of confusion and miscommunication when designing SBGN. For instance, many scientists incorporated colors, shades, and patterns into their diagrams. The team weeded out all of these properties, explaining that they can be hard to preserve when faxing diagrams or reproducing them by hand.
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.”
The next step will be to get the scientific community to accept SBGN. Doing so will require more than just the support of scientists; software designers must also be willing to create tools for working with the languages. The team has already made considerable progress reaching out to such designers: More than a dozen different tools have been developed for the language that describes molecular processes and more are currently in production. “I strongly believe that a standard is used when it is easier to use than not doing so,” Le Novère says.
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