Calculating the information density of an encoding 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.