Insights: Computational biology and biological computation

Image of bees in a hive

Biology and computer science intersect in two fascinating ways that, while sharing confusingly similar names, are actually quite different.


The first is computational biology, which reproduces biological processes and structures in computers. Probably the best-known example is The Human Genome Project, which successfully completed a general map of our DNA in 2003. Among other benefits, such projects have allowed researchers to study (and treat) disease more thoroughly than could be possible through purely biological means.


Computational biology is the basis for my upcoming documentary, "Almost a Brain" which will focus on attempts to fully model the human brain in computers.


Biological computation — the study of how biological organisms perform calculations — is its lesser-known counterpart. I first became familiar with it through an interview (below) with the Salk Institute's Saket Navlakha, who studies communication among such critters as flocking birds and developing cells.


Biological computation could become more important to computer researchers in the coming years. Right now, computer models are based on traditional, digital computing, which ultimately breaks down complex structures into strings of ones and zeros. These strings can be incredibly long, so computers regularly have to handle billions, trillions, or quadrillions of binary digits at a time.


But some problems — including, perhaps, some in computational biology — are better handled using nature's own analog computer methods. (The field of neuromorphic computation is especially relevant to modeling brain processes.)


I've created some articles and videos about how computers and biology interact: Here's a taste.


Computational Biology in the 21st Century from CACM on Vimeo.


Distributed Information Processing in Biological and Computational Systems from CACM on Vimeo.


This post is the fourth in a series of four:

  1. Supercomputing and quantum computing
  2. Artificial intelligence
  3. Computer understanding of behavior
  4. Computational biology and biological computation (this post)