The Science of Discworld Revised Edition

everything about that organism.
    There’s a lot about the DNA code system that we don’t understand, but one part that we
do
is the ‘genetic code’. Some segments of DNA are recipes for proteins. In fact, they come very close to being exact blueprints for proteins, because they list the precise components of the protein and they list them in exactly the right order. Proteins are made from a catalogue of fairly tiny molecules known as amino acids. For most organisms, humans included, the catalogue contains exactly 22 amino acids. If you string lots of amino acids together in a row, and let them fold up into a relatively compact tangle, you get a protein. The one thing the DNA doesn’t list is
how
to fold the resulting molecule up, but usually it folds the right way of its own accord. Occasionally, when it doesn’t, there are more servant molecules to nudge it into the right shape. Just such a servant molecule, rejoicing in the name HSP90, is turning molecular genetics upside down even as we write. HSP90 ‘insists’ that proteins fold into the orthodox shape, even if there are a few mutations in the DNA that codes for those proteins. When the organism is ‘stressed’, diverting HSP90 to other functions, these cryptic mutations suddenly get expressed – the proteins acquire the unorthodox shape that goes along with their mutated DNA codes. In effect, this says that you can trigger a genetic change by non-genetic means.
    Segments of DNA that code for working proteins are called genes. Segments that don’t rejoice in a variety of names. Some of them code for proteins that control when a given gene ‘switches on’, that is, starts to make proteins: these are known as regulatory (or homeotic) genes. Some bits are colloquially called ‘junk DNA’, a scientific term meaning ‘we don’t know what these bits are for’. Some literally minded scientists read this as ‘they’re not
for
anything’, thereby getting the horse of nature neatly aligned with the rear end of the cart of human understanding. Most likely they are a mix of different things: DNA that used to have some function way back in evolution but currently does not (and might possibly be revived if, say, an ancient parasite reappeared), DNA that controls how genes switch their protein manufacturing on and off, DNA that controls
those
, and so on. Some may actually be genuine junk. And some (so the joke goes) may encode a message like ‘It was me, I’m God, I existed all along, ha ha.’
    Evolutionary processes do not always direct themselves along paths that are neatly comprehensible to humans. This doesn’t mean Darwin was wrong: it means that even when he’s right, there may be a surprising absence of narrativium, so that a ‘story’ that makes perfect sense to evolution may not make sense to humans. We suspect that a lot of what you find in living organisms is like that – offering a small advantage at every stage of its evolution, but an advantage in such a complex game is that we can’t tell a convincing story about
why
it’s an advantage. To show just how bizarre evolutionary processes can be, even in comparatively simple circumstances, we must look not to animals or plants, but to electronic circuits.
    Since 1993 an engineer named Adrian Thompson has been evolving circuits. The basic technique, known as ‘genetic algorithms’, is quite widely used in computer science. An algorithm is a specific program, or recipe, to solve a given problem. One way to find algorithms for really tough problems is to ‘cross-breed’ them and apply natural selection. By ‘cross breed’ we mean ‘mix parts of one algorithm with parts of the other’. Biologists call this ‘recombination’ and each sexual organism – like you – recombines its parents’ chromosomes in just this manner. Such a technique, or its result, is called a genetic algorithm. When the method works, it works brilliantly; its main disadvantage is that you can’t always give a sensible explanation of how the resulting algorithm accomplishes whatever it does. More of that in a moment: first we must discuss the electronics.
    Thompson wondered what would happen if you used the genetic algorithm approach on an electronic circuit. Decide on some task, randomly cross-breed circuits that might or might not solve it, keep the ones that do better than the rest, and repeat for as many generations as it takes.
    Most electronic engineers, thinking about such a project, will quickly