What? As someone who can write code in several different languages and who has also made de novo designer modifications to proteins at the low level (biological equivalent to assembler, if you will: http://ityonemo.github.io/acs2013/#/, if you're curious) I can attest to the metaphor between the two. It's not totally simple and not one-to-one, but in many situations it has helped me. Hopefully it keeps helping me as I move into my next stint (http://indysci.org/projectmarilyn)

If your idea of "biology" is "going into bioinformatics", well then no wonder your perception of the metaphor is skewed. Bioinformatics is more like "big data on biology"; what is more analogous to computer programming is Synthetic Biology.

Now, I would happen to disagree with many of the metaphors in the OP (and have, as a practitioner of both, better ones in mind that I use in my work), but that there are no connections is, I think, misguided.

One can understand this better by looking for example at the difficulties encountered by people who do actually base computations on the DNA:

http://en.wikipedia.org/wiki/DNA_code_construction

It's not clear to me what your link about the DNA code construction really means.

Consider instead:

1) people can encode terabytes of digital information in DNA and retrieve it using molecular techniques. To do so accurately enough requires coding.

2) Crick, who helped figure out many of the basic information coding mechanisms, widely acknowledged being influenced by the theory of information as proposed by Shannon.

3) both computer scientists and biologists frequently use the theory of control mechanisms (feedback, etc, also known as 'cybernetics' when I studied under David Huffman) to understand how their systems work, and engineer new ones.

4) you can make a computer out of DNA< and you can put computers into biological systems; they interact according to the laws of information theory, entropy, thermodynamics, etc.

The relationship between computing theory- as practiced today- and what biology has managed to spontaneously developed- are intimately related at the most basic levels of physics. Understanding that is KEY to being a productive biologist or computer scientist, and escpeially so for bioinformaticians.

I'm not defending the original poster's use of analogy between introns and comments. Those are naive, simple analogies.

What I am responding to is your categorical statement that there are no legitimate analogies between CS and biology, and that's patently false.

I'm certainly not claiming - without evidence - that biological systems perform computation. I strongly suspect that biological systems carry out computation- quorum sensing being a canonical example- and in some sense, any sufficiently complex biological system can be considered an analog computer of some sort, with goal-seeking computational behavior.

One of my interests in some time has been building a digital circuit in DNA; one that can compute, using state, a taylor series approximation of an interesting constant, such as pi, using feedback circuits, error correction, and other "digital" approaches. None of this is impossible, it's just technology. People have already started doing this. That the underlying systems are capable of being turned into computing systems is just an outcome of the fact that biological systems are sufficiently complex that they can be used to instantiate simpler digital computing systems.

On the other hand, I think there are lots of CS people who apply blindly oversimplistic models to complex biological situations without understanding the biology, and that's what I was trying to protest against.

That's the kind of attitude that pushes competent programmers away from bioinformatics. End result? Almost all bioinformatics software is a steaming pile of crap.

So it's pretty damn good code, then? (Having worked with plenty of coders from the former soviet bloc: they have a pretty low tolerance of crap code)

The "computational" models that have been successfully used to model the DNA have nothing to do with treating it as a description of a computation, they are only computational in the sense that they use mathematics and computing for making predictions about biological phenomena. Those are mostly probabilistic models that treat the DNA as a string of discrete symbols, and encode the physical and chemical complexities associated with this approach in various ways, one good example is Hidden Markov Models, where the probabilities might encode physical or chemical knowledge:

http://en.wikipedia.org/wiki/Hidden_Markov_model

http://www.biostat.wisc.edu/~craven/hunter.pdf

is actually the first chapter of "Artificial Intelligence and Molecular Biology":

http://mitpress.mit.edu/books/artificial-intelligence-and-mo...

You can't conceive nature as an engineer and talk about how sloppy and random its designs are. You have to bear in mind that nature has made things that we can scarcely comprehend, let alone engineer, so if you're critiquing its designs as if you can read its mind and see what it was trying to do, you're probably missing the point. It pulls off incredible things, so nature is the master and we're the student.

Why not? We can talk about how sloppy something is from a given perspective; we do it all the time, and then try to improve things (there will be no progress otherwise), and if we succeed/fail, learn more ...

>>as if you can read its mind

Nature/evolution is not a sentient being, thus has no mind. Do you have evidence to the contrary ?

https://news.ycombinator.com/item?id=6896779

And then there's RNA, which can fold and form structures, sort of like proteins.

Anybody who has ever seriously dealt with things produced by evolution knows that evolution "cheats" and optimizes when constrained by resources, producing structure upon structure upon structure, crazy indirection and non-obvious side-effects, as long as it helps the goal (survival and reproduction usually).

That's why I don't think human brains will be understood any time soon. We might figure out all the layers, but to figure out all the hidden structures would take a superhuman AI.

EDIT:

Example of circuit design by evolutionary algorithms:

http://hforsten.com/evolutionary-algorithms-and-analog-elect...

So, I don't want to rain on anyone's parade, but we didn't "just" find this out. We've known for quite a long time about secondary (and even tertiary!) "codes" in DNA. That HN article was the result of a press-release about something that was interesting, but certainly not earth-shattering new theory. The reason it was in Science was because they did an extremely large-scale test of a whole bunch of different codons on thousands of different gene promoters, and directly quantified the impact of rare codons. That was an impressive way to settle an open debate.

Anyway, it's good to know that there are higher-order interactions encoded in DNA than just DNA -> RNA -> Protein, but you should realize that this is an old/deep area of research. This is pretty much what bioinformatics is about, actually: deducing the higher-order structures in DNA, RNA and proteins.