Here's my (a computer scientist's) view on the matter:

Imagine that you have a relatively complex computer system written with object oriented principles. Now, imagine that you are looking at the binary representation of this system and trying to make sense out of the whole thing. Also, imagine that you have no knowledge of how computer systems work and how the layers between the computer program, programming language, possibly virtual machine, and native code work.

There are layers involved between these objects and their binary representation. I imagine that there are also layers between our genome (analogy to binary code) and the leveraged representation of ourselves (analogy to object oriented system).

I think that this is why it is hard to make much sense out of the genome, even though the human genome was sequenced.

I also imagine that this is why it is hard to make sense out of the brain by looking at the brain directly. An analogy would be that we are again looking at binary representation of information.

It would be far more useful to figure out how this stuff works. I am not sure how this is done at this time.

The codon used for translating DNA/RNA code to protein is well established. It's a three-base degenerate code, meaning that there are several three-base DNA sequences representing a given amino acid [1]. This code is very well understood. If your DNA/RNA sequence has any of the three bases combos for alanine, your protein gets an alanine in that position. It follows from this that different DNA sequences can code for exactly the same amino acid sequence in a protein. However, proteins with the same amino acid sequence are chemically and biologicalally identical (ignoring things like post-translational modification).

A few years ago, I read a paper [2] where the group hypothesized that in a specific case, a rare codon for for an amino acid in a specific protein caused the cellular machinery to stall at that position. They suggested that in the intervening time, the protein misfolded into a different 3D shape. The resulting protein therefore had different chemical properties despite having identical amino acid sequence. Basically shredding what is often known as the "central dogmal of molecular biology".

Now, this specific example probably needs to be confirmed, and might not be very frequent. But it makes total sense when you understand how all the pieces work. However this explanation would be very low on most biologist's lists of reasons why a certain protein isn't functioning properly. In fact, when people do genetic analysis looking for diseases, they routinely throw out all synonymous changes before doing the stats. It makes you wonder how often we miss this when looking for disease genes.

My larger point is that lots of biological science is a collection of edge cases. We know so little about the systems we're studying and have such crude tools to investigate them, that we get blindsided by things sitting in plain sight all the time.

[1] http://en.wikipedia.org/wiki/Genetic_code [2] http://www.sciencemag.org/content/315/5811/525.abstract

I'll do a little play on words here. Are you saying that biology isn't logical? Does it defy the laws of the universe, physics, and the axioms in math? Certainly not. I think what you are saying is that it isn't the same as a silicon chip and follows different rules. We just don't know enough about biology to peel away the layers, but there are definitely layers. There has to be at least a one-to-one mapping between the genome and a live being, but I would bet that the layers are far more complicated.

I read a study a while back which monitors the different areas developed inside a mouse's brain. The study concluded that a certain part of the brain gets more developed as the mouse tries to run threw a maze vs a mouse that does nothing at all. I gather that the study is trying to point out which area of the brain is responsible for memory, and perhaps certain type of memory.

I fail to see how this study could tell us something meaningful regarding our ability to take information from our senses and store it into our memory. How could we understand more about how this process works? Are we that far away from understanding the inner workings of the brain that we need to do studies like this? If so, then I tend to believe that we are far from the singularity.

"My larger point is that lots of biological science is a collection of edge cases. We know so little about the systems we're studying and have such crude tools to investigate them, that we get blindsided by things sitting in plain sight all the time."

I understand. From my previous example, I would probably come up with edge cases too if I was trying to look at binary code for the first time and trying to figure out how a complex computer system works. Perhaps I would poke the system and monitor which part of the file ended up with more 1's and 0's.

No, he's saying that biology isn't human-designed. Human designs have several characteristics that stem from our limited cognition and limited ability to hold things in our heads at one time. Our designs tend to be highly modular, with distinct parts interacting in distinct ways, and with the failure of one part generally capable of taking the whole system down. There will generally be some clear separation of layers, with each layer having a distinct and clear responsibility. There is generally one way to accomplish something, or failing that, some very small set of ways. Our designs have to be this way, we can't deal with unabstracted systems, even at relatively small sizes.

To the extent that you immediately think of some piece of software that violates all of this, you also will notice that software is also pretty much end-of-lifed. We can't build in any other way for very long.

Biology doesn't work that way. Yes, there are parts you can identify, but they aren't like human-designed parts, either. They freely run all the way across the "abstraction hierarchy", such as it is. If evolution made it so that this one function that you think ought to be done by the kidneys is actually done by blood vessels, so be it. Even drawing lines around "functions" is quite difficult when you get down to it, the deeper you go and the more precise you try to make the line, the more of the body you end up implicating. It's all just jumbled together, with redundant pathways for everything.

We know about the "functions" of things that we know about precisely because we are looking for them in the first place. We have a cognitive bias (in the machine learning sense of a bias induced by what we are capable of even expressing in our heads in the first place, not the usual English sense of bias) for these things, so that's what we find. But whereas in human designs these functions are real (we made them that way), in biology they are only approximations. To the extent you look at a system in the human body and see something with clear, well-defined parts, that's because you can't really perceive the full complexity of what's actually there, not because it isn't complex and quite tangled.

And I gotta tell you, having my brain scanned and converted into an approximation of its original state that will use an approximation of how brains work is a bit of an intimidating prospect.

Biological systems are 'designed' through stochastic processes, which means that from any point in the system we can only make a probabilistic guess as to what points are adjacent to it. This works well for solving small problems, like predicting the next word in a sentence (language of couse being a natural, not human designed process), but this requires us to have a large corpus of example data, and doesn't scale well to trying to extract an entire book from one word.

So trying to fully understand large biological/natural systems is much harder than trying to decode an equally complex human created system.

It's a fantastic read that I think would do a much better job of discussing this topic, especially in the realm of human cognition than I can.

When you ask a genetic algorithm to design a lego bridge, you get something that looks like this: http://www.thinkartificial.org/artificial-intelligence/evolv...

If our DNA was designed by the same process, it almost certainly looks the same way. There should be no abstraction beyond the strictly functional. If it's comprehensible at all, that would be a miracle.

A frightening analogy might be the Win32 API. So many applications rely on the bugs and side effects of the Win32 API's internal implementation that those behaviors become part of the implicit API contract that Microsoft must maintain.