A Computer Scientist's guide to cell biology (2007) [pdf]
cs.cmu.edu
cs.cmu.edu
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.
I think that in order to actually understand truly how things work people are going to have to simulate many different types of cells from first principles (i.e. the physics) and even though the compute time will be 99.99999% wasted checking all kinds of interactions that turn out to be unimportant, that's what will find all the rare interactions that really matter.
The combinatorics that go on inside cells is truly staggering.
As an armchair biologist (among other things,) this paper is a great next-level-of detail from the pop-sci knowledge that "DNA is the Program." Indeed, Wikipedia's illustration of the workings of a ribosome takes steps towards your point, @alexholehouse. It's jagged and sloppy, and while it appears clock-like in its machinations, one must immediately ask how that could be anything but an oversimplification. Is this the workings of the computer that interprets DNA's "program?"
https://en.wikipedia.org/wiki/Ribosome#/media/File:Protein_t...
A final point, it's sobering to watch this and think that every movement of these proteins represents at least one doctoral thesis' worth of work. Although we have amazing ways of seeing these microscopic actions at play, we don't exactly have debuggers, REPLs or profilers that let us observe cells unaffected. Messy stuff.
Materials science is already way ahead here copying nature left and right. Favorite example that I found just last night:
Still, I found alex's comment very informative, a good addition to the book on that topic.
>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.
Okay, so the risk is non-zero. That doesn't tell us anything though. Our scientific progress hasn't halted because of high school students being convinced that they know all there is to know because they scored high on a test of the simplified model of reality.
Every single formal method of instruction uses simplified models to ground students' understanding of these concepts. All exams are graded on students "knowing" something, that's much more complicated in reality.
>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.
Analogies are always about broad brushes. All analogies break down at some level. Because at that point, you would just describe the complex idea, rather than use the analogy.
It costs more but it's worth it. It's a deeply informative book that covers a large spectrum of topics that you can read without much background knowledge in biology. It's the same book your doctor or bioinformatics professional probably used in school learning about cellular biology.
I think having it focus on cell biology, rather than just the information systems, is worthwhile.