The Worn Grooves of Disciplinary Research
Is pathbreaking science the product of interdisciplinary groups or the interdisciplinary thinking of foresighted individuals? In a commentary in PLoS Computational Biology, Sean Eddy, a Howard Hughes investigator, argues that “roadmap” thinking from the National Institutes of Health for building teams of specialists to tackle complex problems in modern research is flawed, because it encourages work in the worn grooves of existing, and perhaps outmoded, disciplines.
Rather, Eddy looks back to the birth of molecular genetics, as scientists stepped out of their research comfort zones and did the work necessary to understand fundamental problems in the study of cell biology. “We think of Watson and Crick as molecular biologists, not as an ornithologist and a physicist,” he writes, “The first molecular biologists were a motley crew of misfits and revolutionaries with no particularly relevant training, many of them ex-physicists.”
Those historical projects cut across the grain of existing disciplines, rather than operate as a choreographed dance that brought specialists together to each ply their own trades. The problem Eddy identifies with the team approach is that it encourages separation rather than synthesis:
Focusing on interdisciplinary teams instead of interdisciplinary people reinforces standard disciplinary boundaries rather than breaking them down. An interdisciplinary team is a committee in which members identify themselves as an expert in something else besides the actual scientific problem at hand, and abdicate responsibility for the majority of the work because it’s not their field. Expecting a team of disciplinary scientists to develop a new field is like sending a team of monolingual diplomats to the United Nations.
Eddy’s argument in favor of “antedisciplinary science” instead of “interdisciplinary” science is an exciting way of thinking about research and education. The increasing popularity of interdisciplinary programs for undergraduates, which train students in skills across departments, might be anecdotal evidence of the appeal and effectiveness of his approach.
One the other hand, there’s also research indicating that collaboration among a diverse set of problem solvers yields more potential approaches to tackling complex problems.
But his own personal narrative—training in molecular biology, followed by work in computer science, software development, and statistics, which led him to become what we now call a “computational biologist”—is a testament to the power of technology to allow individuals to synthesize increasing amounts of information. Advancing that capability is likely to encourage more scientists to carve their own mark across the disciplinary grain.
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