A recent groundbreaking study on the genetics of schizophrenia found a varied pattern of large scale "sledgehammer" (as in whack the genes) mutations in many persons with schizophrenia. These are thought to affect varying stages of brain development.
Curiously, the researchers found the same problems in a minority of the "control" group of "normal" people:
One in twenty seemingly normal people have big, ugly looking mutations that ought to be messing up their brain development. Yet they seem "normal"...
Now another big study explains this strange normality (emphasis mine) ...
.... In the latest issue of Nature, scientists reported an experiment in which they wreaked havoc with E. coli's network. They randomly added new links between the transcription factors at the top of the microbe's hierarchy. Now a transcription factor could turn on another one that it never had before. The scientists randomly rewired the network in 598 different ways and then stepped back to see what happened to the bacteria.
You might expect that they all died. After all, if you were to pop open the back of an iPod and start linking its components together in random ways, you'd expect it to crash. But that's not what happened.
About 95 percent of the rewired bacteria did just fine with their new networks. They went on with their lives, feeding, growing and dividing. Some even performed better than microbes with the original wiring, under some conditions.
The tolerance these bacteria showed reveals something important about how evolution works. Humans can randomly rewire cells, and so can mutations. There's something about gene networks that allow them to thrive despite these mutations, and, in some cases, to even gain an edge in the evolutionary race.
But scientists don't quite know why a network like the one in E. coli can handle this rewiring so well. The source of their strength lies not in a single molecule -- DNA -- but in a complicated web of relationships. The network itself is the mystery for biologists in the 21st century...
This is of a piece with the discovery that DNA control system have complex topological components, my June 2007 essay on evolved circuits. and reading I've done over the past year on bioinformatics (systems biology) and the modeling of interacting protein networks (interactome) (example).
The blueprint for an organism is emergent. It "appears" through the interaction of the storage elements in DNA and DNA associated packaging, but, like a holographic image, it can "appear" even when pieces of the storage structure are absent or reorganized. This is a shared characteristic of evolved systems on every scale, we see hints of this even in evolved mechanical systems such as the freight train pneumatic braking system. Bacteria, of course, are the most "evolved" of all systems -- far more evolved than mere humans.
That's why major "controllers" of brain development can be disrupted, but, in many cases, the brain can still develop -- differently perhaps. In some settings, the differences might even be advantageous.
How will we understand this emergent control system? We will not be able to do perceive it directly. We will need computational systems to discern the emergent controllers, and to be able to relate a network level "control element" to the set of physical manifestations of the abstract control element in real-world DNA.
Sigh. It all looked so simple in the days of 'one gene, one protein' ...
Update 4/19/08: There's an obvious metaphor for the type of emergence we see here. An example that makes the problem transparently obvious for all of us.Imagine that I want you to meet me by the science museum at 11:30 am. I could use English or French or draw a picture. In any spoken or written language I could use an enormous variety of words and word order and still communicate my meaning.
If we think of "the meaning" in cellular biology as that which arises from interacting protein networks, then by analogy we can understand that many different gene arrangements and even several somewhat different proteins could produce similar protein network interactions.