For the biopharmaceutical industry, big data is the abundant and ever-growing compendium of disease associations and genomic data that is (in many cases) publicly available. Instead of following the traditional pharma paradigm of systematically testing the effects of known compounds, computational biologists can now mine petabytes of data to identify linkages between genotypes and phenotypes, and genes and diseases to identify potential therapeutic candidates that corroborate drug screening data.
Indeed, I do this all the time! I’m frequently mining and integrating data, building molecular interaction networks, and evaluating targets or indications before I ever need to “dig in” to specific drug data. From this process, I often know which types of drug data I need to focus on.
The Science Careers article highlights the growing demand for computational biologists:
As the pharmaceutical industry’s blockbuster drugs fall off the patent cliff, with precious few drugs in the pipeline to replace them, there are signs that big pharma could turn more of its attention to biologically derived medicines. If that happens, computational biologists will likely play a leading role in their discovery, [says Russ Altman, a professor of bioengineering, genetics, and medicine and director of the biomedical informatics training program at Stanford University in Palo Alto, California].
Altman says that according to his own observations, demand for computational biologists far outstrips supply. ‘I was just talking to a colleague the other day from a major drug company who came in with a piece of paper with 15 bioinformatics jobs that they’re ready to hire tomorrow,’ he says.
If there truly is a shortage of computational biologists in pharma and biotech, Altman says, the federal government should be investing more heavily in training. Three institutes at the National Institutes of Health (NIH)–the National Institute of General Medical Sciences, the National Cancer Institute, and the National Library of Medicine —–fund bioinformatics training programs, he says, but that’s not enough. Altman’s training grant from NIH was renewed recently, but its funding was reduced, so he’ll have to trim three or four training slots. ‘I do think it’s worrisome that a field that’s exploding is seeing a reduced amount of support for training.’
A recently announced Obama Administration project could reverse or offset [the] trend [of reduced funding for computational biologists]. The Big Data Research and Development Initiative pledges $200 million for NIH, the National Science Foundation, and other federal agencies to support data collection, analysis, and dispersion. Some of that money will go toward training computational biologists. When the program comes online, it could result in some direct hiring within the agencies it supports.
NIH’s director, Francis Collins, seems to believe in the field’s importance. At a 29 March briefing announcing the initiative in Washington, D.C., Collins told Science Careers, ‘If I were a senior or first-year graduate student interested in biology, I would migrate as fast as I could into the field of computational biology. … There are vast quantities of high-quality data accessible to anybody who has the skills to find the nuggets of truth that are hiding in that information.’