Biology is the study of life, and informatics is the science of processing information with computers. Thus, bioinformatics is the science of using computers to answer biological questions.

Computers today are capable of designing new vaccines, tracing the ancestry of one person thousands of years back, and identifying a criminal with nearly 100% accuracy from just a speck of blood. None of these tasks would be possible without advances in bioinformatics.

Let’s look at a simple example to understand how a biological problem can be solved using bioinformatics.

Putting Two And Two Together

All bioinformatics solutions can be broken down into four parts – (1) data, (2) a model (like a big math equation), (3) visualization of results (graphing), and (4) inference (what did we learn).

Assume that you are a biologist observing a small population of red and white mice. There are 8 white mice and 2 red mice (1 data). You are interested in knowing approximately how many generations it would take for the mice population to be entirely red or entirely white.

A traditional biologist will have to wait for every new generation and count the mice.. Even for mice, this could take a very, very long time.

Well, we already know a fair bit about how mouse color is passed from parents to babies. Therefore, we also know that the number of red mice in the next generation will depend on the number of red mice in the previous generation. In other words, if there are more red mom and dad mice now, it is more likely that their babies will also be red.  But how can we demonstrate this?

Bioinformatics To The Rescue

That is when the bioinformatician will “simulate” this situation using a computer. I did exactly this using a simple computer program that I wrote which tracks the percentage of red mice over time (part 2, model).

I ran this same experiment on a computer five times, and within seconds, an interesting pattern began to emerge, which can be seen in the graph below (part 3, visualization). So if the graphed line goes up from left to right, it indicates that the number of red mice is growing; if it goes down, it shows that the number of red mice is falling.

What do you see? In four out of five cases, the number of red mice went to 0 within a span of a few generations (here, an average of about 10 generations). Only in one case (shown by the red line), did the number of red mice go up to 10 (shown here by a ratio R/N = 1.0).  

There are several conclusions we can make (part 4, inference). First, it is likely that in less than 20 generations, the mice will likely be either all white or all red. Secondly, I would interpret this to mean that there is an 80% chance that the population will become all white. 

Indeed, this phenomenon is very common in nature. We call it “genetic drift”, or the random change in frequency of an observation (here color of mice) in a population. As we have shown in this simple example, it only depends on the frequency in the previous generation. Touché, mouse biologist!