Historically and practically, biology as a field has often represented processes and events in an highly linearized and well defined fashion. This is extremely attractive for a number of reasons. As one key example, we (as humans) remember through narrative, and so the construction of a Rube Goldberg type description of a process is often a useful technique for easy recall of complicated information ("A hits B which winds up C and switches on D etc...").
The other reason is that many of the experiments would really imply a linear pathway if that were all one look for. There is often a clear-cut progression of information signals or metabolic intermediates moving from one state to another state through well defined intermediates, such that if that were what you were looking for, you'd find it.
In this representation, many biological processes are highly analogous to computer programs which perform some task - you have an input, and through functional manipulation generate an output.
The realty, as has been uncovered in the last 15-20 years, is that most of these processes and events are not linear pathways. They are wildly heterogenous networks that integrate information spanning a range of temporal and spatial scales. The non-equilibrium nature of the cell means that, to a certain extent, everything is coupled to everything else through interactions where the associated coupling coefficients are also dependent on everything else.
The reason I bring this up is that I think it's very temping to find analogies between CS and biology (DNA = hard drive, RNA = memory etc). The problem with this is that we (again, as humans) implemented the underlying computer architecture, while we're only scratching the surface of biological complexity. By prescribing that some mapping of CS-to-biology exists we risk convincing ourselves that we understand the biology better than we do, or making assumptions regarding how the biology may or may not work.
Clearly, this kind of description can be used early on, but its important to recognize that these analogies should be viewed as broad-brush stroke descriptors and not functional ones.