Calculating the information density of an encoding system How do I calculate the information density of an encoding system?

This question came up as I was reading several papers about encoding data in DNA (I think I have gone through just about every major paper on encoding data in DNA after Church's). All of them mention the information density of their encoding system, which I understand to be given in units along the lines of bits or bytes per nucleotide. 

From looking around on the internet and asking around in the hbar (the Physics chat) I understand the absolute basics: that one creates a model that gives the probability distribution over the possible messages in your system (say, English, or ASCII) and then from there calculate the average information density of a message using that distribution. 

Unfortunately, as a complete beginner, I don't know how to do any of those things. I'd like to understand that process and in the end be able to do my own calculations. Any resources would be helpful, of course, but I was advised to ask this question here.

Tl;dr: how do you calculate the information density of a system?
When people talk about information density in this context, they probably mean that the information density is

$$\text{density} = {n \over m}$$

where $m$ is the number of nucleotides needed to encode $n$ bits.  Here the assumption is that the $n$ bits come from the uniform random distribution (i.e., all $2^n$ possibilities are equally likely).  Implicitly, we are taking $n$ to be very large, or taking the limiting value of the density as $n \to \infty$.

So, think of it this way.  Imagine we had $n = $ 1 million bits.  How many nucleotides would the encoding scheme need, to encode those 1 million bits, on average?  Call that $m$.  Now divide $n$ by $m$; that's your information density.

The way to figure out the average number of nucleotides needed to encode a one-million-bit input will depend on the particular encoding scheme.