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The Volume 29 Number 1 January 2011 issue of nature biotechnology ( finally puts in print what I’ve been recommending all along. The Feature article on computational BIOLOGY, “Trends in computation biology – 2010” on page 45 states, “Interviews with leading scientists highlight several notable breakthroughs in computational biology from the past year and suggest areas where computation may drive biological discovery,”

The researchers were asked to nominate papers of particular interest published in the previous year that have influenced the direction of their research.

The article is good, but what was really interesting was Box 2 – Cross-functional individuals on page 49. To quote, “Our analysis…suggests that researchers of a particular type are driving much of cutting-edge computational biology. Read on to find out what characterizes them.”

I’m going to re-print Box 2 Cross-functional individuals in it’s entirety since it’s short and the message is so very important.

Box 2 Cross-functional individuals

In the courses of compiling this survey, several investigators remarked that it tends to be easier for computer scientists to learn biology that for biologists to learn computer science. Even so, it is hard to believe that learning the central dogma and the Krebs cycle will enable your typical programmer-turned-computational biologist to stumble upon a project that yields important novel biological insights. So what characterizes successful computational biologists?

George Church, whose laboratory at Harvard Medical School (Cambridge, MA USA) has a history of producing bleeding-edge research in many cross-disciplinary domains, including computational biology, say, “Individuals in my lab tend to be curious and somewhat dissatisfied with the way things are. They are comfortable in two domains simultaneously. This has allowed us to go after problems in the space between traditional research projects.”

A former Church lab member, Greg Porreca, articulates this idea further, “I’ve found that many advances in computational biology start with simple solutions written by cross-functional individuals to accomplish simple tasks. Bigger problems are hard to address with those rudimentary algorithms, so folks with classical training in computer science step in and devise highly optimized solutions that are faster and more flexible.”

An overarching theme that also emerges from this survey suggests that tools for computational analysis permeated biological research according to three states: first, a cross-functional individual sees a problem and devises a solution good enough to demonstrate the feasibility of a type of analysis; second, robust tools are created, often utilizing the specialized knowledge of formally trained computer scientists; and third, the tools reach biologists focused on understanding specific phenomena, who incorporate the tools into everyday use. These stages echo existing broader literature on disruptive innovations1 and technology-adoption life cycles2,3, which may suggest how breakthroughs in computational biology can be nurtured.

  1. Christiansen, C.M. & Bower, J.I., Disruptive technologies: catching the wave. Harvard Business Review (1995).

  2. Moore, G.A. Crossing the Chase: Marketing and Selling High-Tech Products to Mainstream Customers (Harvard Business, 1999)

  3. Rogers, E.M. Diffusion of Innovations (Free Press, 2003).

Biologists must become aware of what the disciplines of computer science and engineering can offer computational biology. Until this happens, forward progress in computational biological innovations and discovery will be unnecessarily hampered by a number of superfluous factors not the least of which is complacence.

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