The Science of Discworld

by Terry Pratchett

  Many biologists think that this is a minor objection — after all, Egg Donor and Surrogate Mum work the way they do because their DNA contains the information that makes them do it. But things that aren't in any organism's DNA may be essential for the reproductive cycle. A good example occurs in yeast, a plant that can turn sugar into alcohol and give off carbon dioxide. The entire DNA code for one species of yeast is now known. Thousands of experimentalists have played genetic games with yeast, then spun the beasties in a centrifuge to separate the DNA, from which they can work out the code. When you do this, you leave a scummy residue in the bottom of the test tube, but since it's not DNA, you know it can't be important for genetics, and you throw it away. And so they all did, until in 1997 one geneticist asked a stupid question. If it's not DNA, what's it for? What's in that scummy residue, anyway?

  The answer was simple, and baffling. Prions. Lots and lots of them.

  A prion is a smallish protein molecule that can act as a catalyst for the formation of more protein molecules just like itself. Unlike DNA, it doesn't do this by replication. Instead, it needs a supply of proteins that are almost like itself, but not quite — the right atoms, in the right order, but folded into the wrong shape. The prion attaches itself to such a protein, jiggles it around a bit, and nudges it into the same shape as the prion. So now you've got more prions, and the process speeds up.

  Prions are molecular preachers: they make more of themselves by converting the heathen, not by splitting into identical twins. The most notorious prion is the one that is believed to be the cause of BSE, 'mad cow disease'. The protein that gets converted happens to be a key component of the cow's brain, which is why infected cows lose coordination, stagger around, foam at the mouth, and look crazy. What does yeast want prions for? Without prions, yeast can't reproduce. The protein-making instructions in its DNA sometimes make a protein that is folded into the wrong shape. When a yeast cell divides, it copies its DNA to each half, but it shares the prions (which can be topped up by converting other proteins). So here's a case where, even on the molecular level, an organism's DNA does not specify 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 realize that it's silly to use genuine circuits. Instead, you can simulate the circuits on a computer (since you know exactly how a circuit behaves) and do the whole job more quickly and more cheaply in simulation. Thompson mistrusted this line of argument, though: maybe real circuits 'knew' something that a simulation would miss.

  He decided on a task: to distinguish between two input signals of different frequencies, 1 kilohertz and 10 kilohertz — that is, signals that made 1000 vibrations per second and 10,000 vibrations per second. Think of them as sound: a low tone and a high tone. The circuit should accept the tone as input signal, process it in some manner to be determined by its eventual structure, and produce an output signal. For the high tone, the circuit should output a steady zero volts — that is, no output at all — and for the low tone, the circuit should output a steady five volts. (Actually, these properties were not specified at the start: any two different steady signals would have been acceptable. But that's how it ended up.)

  It would take forever to build thousands of trial circuits by hand, so he employed a 'field-programmable gate array'. This is a microchip that contains a number of very tiny transistorized 'logic cells' — mildly intelligent switches, so to speak — whose connections can be changed by loading new instructions into the chip's configuration memory.

  Those instructions are analogous to an organism's DNA code, and can be cross-bred. That's what Thompson did. He started with an array of one hundred logic cells, and used a computer to randomly generate a population of fifty instruction codes. The computer loaded each set into the array, fed in the two tones, looked at the outputs, and tried to find some feature that might help in evolving a decent circuit. To begin with, that feature was anything that didn't look totally random. The 'fittest' individual in the first generation produced a steady five-volt output no matter which tone it heard. The least fit instruction cod
es were then killed off (deleted), the fit ones were bred (copied and recombined), and the process was repeated.

  What's most interesting about the experiment is not the details, but how the system homed in on a solution — and the remarkable nature of that solution. By the 220th generation, the fittest circuit produced outputs that were pretty much the same as the inputs, two waveforms of different frequencies. The same effect could have been obtained with no circuit at all, just a bare wire! The desired steady output signals were not yet in prospect.

  By the 650th generation, the output for the low tone was steady, but the high tone still produced a variable output signal. It took until generation 2800 for the circuit to give approximately steady, and different, signals for the two tones; only by generation 4100 did the odd glitch get ironed out, after which point little further evolution occurred.

  The strangest thing about the eventual solution was its structure. No human engineer would ever have invented it. Indeed no human engineer would have been able to find a solution with a mere 100 logic cells. The human engineer's solution, though, would have been comprehensible — we would be able to tell a convincing 'story' about why it worked. For example, it would include a 'clock' — a circuit that ticks at a constant rate. That would give a baseline to compare the other frequencies against. But you can't make a clock with 100 logic cells. The evolutionary solution didn't bother with a clock. Instead, it routed the input signal through a complicated series of loops. These presumably generated time-delayed and otherwise processed versions of the signals, which eventually were combined to produce the steady outputs. Presumably. Thompson described how it functioned like this: 'Really, I don't have the faintest idea how it works.'

  Amazingly, further study of the final solution showed that only 32 of its 100 logic cells were actually needed. The rest could be removed from the circuit without affecting its behaviour. At first it looked as if five other logic cells could be removed — they were not connected electrically to the rest, nor to the input or output. However, if these were removed, the circuit ceased to work. Presumably these cells reacted to physical properties of the rest of the circuit other than electrical current, magnetic fields, say. Whatever the reason, Thompson's hunch that a real silicon circuit would have more tricks up its sleeve than a computer simulation turned out to be absolutely right.

  The technological justification for Thompson's work is the possibility of evolving highly efficient circuits. But the message for basic evolutionary theory is also important. In effect, it tells us that evolution has no need for narrativium. An evolved solution may 'work' without it being at all clear how it does whatever it does. It may not follow any 'design principle' that makes sense to human beings. Instead, it can follow the emergent logic of Ant Country, which can't be captured in a simple story.

  Of course, evolution may sometimes hit on 'designed' solutions, as happens for the eye. Sometimes it hits on solutions that do have a narrative, but we fail to appreciate the story. Stick insects look like sticks, and their eggs look like seeds. There is a kind of Discworld logic to this, since seeds are the 'eggs' of sticks, and prior to the theory of evolution taking hold the Victorians approved of this 'logic' because it looked like God being consistent. The early evolutionists didn't see it that way, and they worried about it; but they worried a lot more when they found that some stick insect eggs looked like little snails. It seemed silly for anything to resemble the favourite food of nearly everything else. In fact, it seemed to be a flat contradiction to the evolutionary story. The puzzle was solved only in 1994, after forest fires in Australia. When new plant shoots came up out of the ashes, they were covered in baby stick insects. Ants had carried the 'seeds', and the 'baby snails', down into their subterranean nests, thinking they were the real thing. Being safely underground, the stick insect eggs escaped the fires. In fact, baby stick insects look, and run, just like ants: this should have been a clue, but nobody made the connection.

  And sometimes evolution's solution has no narrative structure. To test Darwin's theories thoroughly, we should be looking for evolved systems that don't conform to a simple narrative description, as well as for ones that do. Many of the brain's sensory systems may well be like this. The first few layers of the visual cortex, for example, perform generalized functions like detecting edges, but we have no idea how lower layers work, and that may well be because they don't conform to any design principles that we currently can recognize. Our sense of smell seems to be 'organized' along very strange lines, not at all as clearly structured as the visual cortex, and it too may be lacking any element of design.

  More importantly, genes may well be like this. Biologists habitually talk of 'the function of a gene' — what it does. The unspoken assumption is that it does only one thing, or a small list of things. This is pure magic: the gene as a spell. It is conceived as being a spell in the same sense that 'Cold Start' in a car is. But a lot of genes may not do anything that can be summed up in a simple story. The job they evolved to do is 'build an organism', and they evolved as a team, like Thompson's circuits. When evolution turns up solutions of this kind, conventional reductionism is not much help in understanding those solutions. You can list neural connections till the cows come home, but you won't understand how the cows' visual systems distinguish a cowshed from a bull.

  The quantity of bacon per trotter is on average slightly more than one quarter of the amount per head.

  TWENTY-SEVEN

  WE NEED MORE BLOBS

  RINCEWIND WAS FINDING, now that he was back at what appeared to be his real size, that he was coming to enjoy this world after all. It was so marvellously dull.

  Every so often he'd be moved forward a few tens of millions of years. The sea levels would change. There seemed to be more land around, speckled with volcanoes. Sand was turning up on the edge of the sea. Yet the sheer vast ringing silence dominated everything. Oh, there'd be storms, and at night there were brilliant meteor showers that practically hissed across the sky, but these only underlined the absent symphony of life.

  He was rather pleased with 'symphony of life'.

  'Mr Stibbons?' he said.

  'Yes?' said Ponder's voice in his helmet.

  'There seem to be a lot of comets about.'

  'Yes, they seem to go with roundworld systems. Is this a problem?'

  'Aren't they going to crash into this world?'

  Rincewind heard the muted sounds of debate in the background, and then Ponder said: 'The Archchancellor says snowballs don't hurt.'

  'Oh. Good.'

  'We're going to move you on a few million years now. Ready?'

  'Millions and millions of years of dullness,' said the Senior Wrangler.

  'There are more blobs today,' said Ponder.

  'Oh, good. We need more blobs.'

  There was a yell from Rincewind. The wizards rushed to the omniscope.

  'Good heavens,' said the Dean. 'Is that a higher lifeform?'

  'I think,' said Ponder, 'that seat cushions have inherited the world.'

  They lay in the warm shallow water. They were dark green. They were reassuringly dull.

  But the other things weren't.

  Blobs drifted over the sea like giant eyeballs, black, purple, and green. The water itself was covered with them. A scum of them rolled in the surf. The aerial ones bobbed only a few inches above the waves, thick as fog, overshadowing one another in their fight for height.

  'Have you ever seen anything like that?' said the Senior Wranger.

  'Not legally,' said the Dean. A blob burst. Audio reception on the omniscope was not good, but the sound was, in short, phut. The stricken thing disappeared into the sea, and the floating blobs closed in over it.

  'Get Rincewind to try to communicate with them,' said Ridcully.

  'What have blobs got to talk about, sir?' said Ponder 'Besides, they're not making any noise. I don't think phut counts.'

  'They're various colours,' said the Lecturer in Recent Runes. 'Perhaps they communicate b
y changing colour? Like those sea creatures —’ He snapped his fingers as an aid to memory.

  'Lobsters?' the Dean supplied.

  'Really?' said the Senior Wrangler. 'I didn't know they did that.'

  'Oh yes,' said Ridcully. 'Red means "help!"'*

  'No, I think the Lecturer in Recent Runes is referring to squid,' said Ponder, who knew that this sort of thing could go on for a long time. He added hurriedly, 'I'll tell Rincewind to give it a try.'

  Rincewind, apparently knee deep in blobs, said: 'What do you mean?'

  'Well ... could you get embarrassed, perhaps?'

  'No, but I'm getting angry!'

  'That might work, if you get red enough. They'll think you want help.'

  'Do you know there's something else here besides blobs?'

  Some of the blobs trailed strands in the faint breeze blowing across the beach. When they tangled up on a blob gasbag, which put some stress on the line, the little blob on the end let go its grip on a rock, the line gradually shortened, and the gasbag bobbed onwards with its new passenger.

  Rincewind saw them on a number of blobs. The blobs did not look healthy.

  'Predators,' Ponder told him.

  'I'm on a beach with predators?'

  'If it really worries you, try not to look blobby. We'll keep an eye on them. Er ... the Faculty is of the opinion that intelligence is most likely to arise in creatures that eat lots of things.'