Translational Research vs. Basic Science: Comparing Apples to Upside-down Apples

“Translational research” has become an extremely popular buzzword in the world of biomedical research lately.  Basically translational research aims to “translate” existing knowledge about biology into techniques and tools for treating human disease: from bench to bedside.  Beyond that, the definition gets a little fuzzy, but it’s basically just applied research for the medical world, and like everything in medicine it requires its own obscure name.  When I was making the transition from undergrad to grad school, I was positive that I had to find a lab that did “translational research,” even though I was still foggy on the details of what exactly that meant.  I just knew that I wanted my work to improve lives; I didn’t care how exactly I would accomplish that but I knew that I wanted it to happen in as direct a way as possible.  I didn’t think I was patient enough for basic science.   I’ve since (mostly) figured out what translational research entails, and it isn’t nearly as different from basic science as you might think.

Drs. Ferric Fang and Arturo Casadevall wrote a great editorial on translational research back in 2010.  If you haven’t had a chance to read this yet, it’s available for free at http://iai.asm.org/content/78/2/563.full.  In it they described the meteoric rise of translational research, starting in the 1980s when universities were first allowed to patent discoveries made with federal funding and applied research suddenly became a major priority in academia.  The primary drive of the article is to express concern that the public might be forgetting about good old Basic Science in the rush to fund translational projects.  After all, they argue, basic and applied research are complementary, with basic science uncovering promising new ideas for use in applied research and applied research raising new questions for basic scientists to answer.  It seems as though they view the two as completely separate entities, with basic science as a generator of basic ideas on one end and applied research as a conveyor belt to the market on the opposite end.

From my perspective as a first year grad student in biomedical engineering, applied research and basic science are much less clear-cut than that.  There is a spectrum between pure applied research and pure basic science, and the motivation behind an experiment makes all the difference.  If your motivation is knowledge for knowledge’s sake, then you’re doing basic science.  If your motivation is to get results that can be used to make a better vaccine, lighter engineering material, faster network architecture, etc., then it’s applied research.  In this way applied research is a top-down style of scientific inquiry, while basic science is bottom-up.  If two researchers look at the same signaling pathway in human cartilage cells, a biomedical engineer would look for ways to exploit it in tissue engineering and a molecular biologist would simply be exploring the unknown.  Both researchers would advance knowledge and find the same information, but the engineer is doing applied research and the biologist is doing more basic science.  Even the molecular biologist will likely have an eye towards medical applications, making her research somewhere in the middle of the spectrum.  This is why it’s so hard to nail down a clear-cut definition of translational research.

“What I cannot build, I cannot understand.”  That’s a quote that synthetic biologist Craig Venter, coded into the genome of the world’s first synthetic organism.  He misquoted Richard Feynman, but Venter’s version works much better as a mantra for applied research.  Attempting to apply the knowledge we already have is a great way to find out what we still don’t know, and that can make purely-applied fields like synthetic biology very interesting to basic scientists.  When experiments don’t work out quite as expected, it signals an opportunity to fill gaps in our basic knowledge.  Surprises that arise in applied research can actually help us answer questions that would traditionally be left to basic scientists, as in this case where underestimating the influence of molecular crowding in a synthetic application led to research that would be relevant to the molecular biology of natural cells:  http://www.nature.com/nnano/journal/v8/n8/full/nnano.2013.132.html.

I’m currently working in the Baar lab at UC Davis, in a sweet spot at the crossroads of basic science and translational research.  We use engineered ligaments grown in vitro to answer basic biological questions, like “is movement critical to the development and repair of connective tissue?” and “why are women at such an elevated risk of tendon and ligament tears?”  We can also turn right around and use those answers in a translational way:  not only do certain results suggest ways in which we can grow functionally better ligaments in the future, but the results from our engineered ligament models can be used immediately to suggest better clinical treatments and physical therapy regimens for tendon and ligament reconstructions.  It turns out you can have your cake and eat it too.

Applied research and basic science aren’t competitors, they’re just different styles of approaching the same problem.  Is one style better than the other?  Fang and Casadevall didn’t think so, but as an engineer I admit I may be slightly biased toward applied research.  I prefer to work towards a stated need, solving small problems incrementally as I go.  After all, I have almost exclusively been exposed to applied research thus far.  In the end though, both styles have their own strengths, and we’ll all have our own preferences depending on our backgrounds.  The findings of basic science are sometimes shelved without an obvious immediate use, but exploring uncharted areas of knowledge is the sort of thing that can create revolutions in science.  The fact that applied research has a goal in mind from the very beginning makes it easier for results to be used outside of academia, so it’s not hard to see why applied research has become so much more attractive to funding agencies.  But in the end they’re really not that different; despite their separate goals the types of results they yield are often the same.

Michael SelepMichael Selep is a first year graduate student in biomedical engineering at UC Davis, with a B.S. in mechanical engineering from the University of Notre Dame.  When he’s not in the lab, in class, or sleeping; you can find him skiing or daydreaming about skiing, depending on the season.

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4 Responses to Translational Research vs. Basic Science: Comparing Apples to Upside-down Apples

  1. There is a spectrum between pure applied research and pure basic science, and the motivation behind an experiment makes all the difference.

    When I moved my lab from Technical University Berlin to Humboldt University Berlin, I also changed its name to “Translational Computational Neuroscience“. Ironically, one would expect translational science being better placed at our Technical University rather than at Humboldt in Berlin. Humboldt has a medical school, however, and, as you say, everything in medicine requires its own name. Is it obscure? Anyway, you caught me in the act.

    Motivation behind the scientific approach makes all the difference. Again, I think, you are completely right here. On the long term, however, this label can actually change the way you go forward quite a bit.

    The name “applied” is unfortunately in my area, computational neuroscience, often mistaken for doing in silico experiments, which is not the road to bedside. So it’s not so much the difference between pure applied research and pure basic science but the objective and, yes, motivation.

    Having my lab name in front of me, I recontextualize my scientific approach in accordance to a top-down style every single day. With that, more and more I might actually achieve what has been initially — I admit it — a rhetorical purpose to get funding. And all that by simple turning the apple up side down. Isn’t it great?

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  3. Jenny says:

    Sydney Brenner had some interesting things to say about the role for model systems vs. the long-sought ability to actually do human experiments in a talk he gave at Columbia several weeks ago (which I will not presume to paraphrase). I don’t know if he’s written it up anywhere, but it was insightful.

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