SAN FRANCISCO, June 21 (GenomeWeb News) - Advances in genomic technologies have led many researchers, including those in pharma, to focus on generating data instead of trying to understand complex disease mechanisms, according to a panel discussion held here today at this year's Beyond Genome conference.
"I think one of the problems that we have as scientists is that, for better or worse, we tend to be seduced by technology," said panelist Mike Liebman, executive director of the Windber Research Institute in
"As a result we believe that a collection of a lot of data is going to lead to an answer, and what it leads to [instead] is a collection of a lot more data and not an answer to the question because we forgot to ask the question to start with," he said.
Liebman, who sat in for Ellen Feigal, director of Medical Devices and Imaging at the Critical Path Institute, said it's "always easier" to generate data rather than applying the "science and the sweat" of interpreting and analyzing them. "Data generation is not productivity," he said.
Panelist Francois Iris, president and chief scientific officer of Bio-Modeling Systems, a French systems biology company, agreed and cautioned that researchers must not "confuse" technological innovations with possible answers.
"We should be a little more humble," he said. "We thought [new technologies] would save us from thinking. Tough luck."
The panelists' comments come at a perennial crossroads for genomic technologies. A random sampling of Beyond Genome attendees interviewed by GenomeWeb News lamented that these tools have not had an effect on drug discovery or development. However, an interview last month with the author of a widely respected study exploring pharma's productivity found that genomic technologies helped drug makers increase by 52-percent the number of candidates that make it into the clinic.
Liebman, who stressed that his comments are not an indictment of new technologies or of technological advancement, said the problem is especially evident in systems biology, a particularly complex corner of genomic research.
Some scientists studying systems bio "tend to try to minimize what we look at in systems to try to make it simpler," he told GenomeWeb News following the session. "We have a tendency to try to make the system small enough so we can manage it, but the problem is that it interacts with things outside of [it]. When we don't take that into consideration it makes that model fail."
Asked to identify solutions to this epistemological problem, Liebman said that in the case of biology, researchers "must look at a bigger biological system," while in the case of drug development, researchers "don't look broadly enough at public acceptance, at medical practices."
He said academia could play a role in reversing this trend, though it would likely find it an uphill battle in part because the National Institutes of Health, its biggest funder, is entrenched in the existing data-generation model.
"The NIH system tends to favor a lot of technology-development" instead of enabling research that uses existing tools to shed light on disease pathways and mechanisms.
"It's a very deep-rooted problem in society," he said.