Science of Discworld

THE DESCENT OF DARWIN

THE WIZARDS MET THE GOD OF EVOLUTION in The Last Continent. He made things the way a god ought to:

"'Amazin' piece of work," said Ridcully, emerging from the elephant. "Very good wheels. You paint these bits before assembly, do you?"'

The God of Evolution builds creatures piece by piece, like a butcher in reverse. He likes worms and snakes because they're very easy, you can roll them out like a child with modelling clay. But once the God of Evolution has made a species, can it change? It does on Discworld, because the God runs around making hurried adjustments... but how does it work without such divine interven­tion?

All societies that have domestic animals, be they hunting dogs or edible pigs, know that living creatures can undergo gradual changes in form from one generation to the next. Human intervention, in the form of 'unnatural selection', can breed long thin dogs to go down holes and big fat pigs that provide more bacon per trotter. The wizards know this, and so did the Victorians. Until the nine­teenth century, though, nobody seems to have realized that a very similar process might explain the remarkable diversity of life on Earth, from bacteria to bactrians, from oranges to orangutans.

They didn't appreciate that possibility for two reasons. When you bred dogs, what you got was a different kind of dog, not a banana or a fish. And breeding animals was the purest kind of magic: if a human being wanted a long thin dog, and if they started from short fat ones, and if they knew how the trick worked (if, so to speak, they cast the right 'spells') then they would get a long thin dog. Bananas, long and thin though they might be, were not a good starting point. Organisms couldn't change species, and they only changed form within their own species because people wanted them to.

Around 1850, two people independently began to wonder whether nature might play a similar game, but on a much longer timescale and in a much grander manner, and without any sense of purpose or goal (which had been the flaw in previous musings along similar lines). They considered a self-propelled magic: 'natural' selection as opposed to selection by people. One of them was Alfred Wallace; the other, far better known today, was Charles Darwin. Darwin spent years travelling the world. From 1831 to 1836 he was hired as ship's naturalist aboard HMS Beagle, and his job was to observe plants and animals and note down what he saw. In a letter of 1877 he says that while on the Beagle he believed in 'the permanence of species', but on his return home in 1836 he began to think about the deeper meaning of what he had seen, and realized that 'many facts indicated the common descent of species'. By this he meant that species that are different now probably came from ancestors that once belonged to the same species. Species must be able to change. That wasn't an entirely new idea, but he also came up with an effective mechanism for such changes, and that was new. Meanwhile Wallace was studying the flora and fauna of Brazil and the East Indies, and comparing what he saw in the two regions, and was coming to similar conclusions, and much the same expla­nation. By 1858 Darwin was still mulling over his ideas, contemplating a grand publication of everything he wanted to say about the subject, while Wallace was getting ready to publish a short article containing the main idea. Being a true English gentleman, Wallace warned Darwin of his intentions so that Darwin could pub­lish something first, and Darwin rapidly penned a short paper for the Linnaean Society, followed a year later by a book, The Origin of Species, a big book, but still not on the majestic scale that Darwin had originally intended. Wallace's paper appeared in the same jour­nal shortly afterwards, but both papers were officially 'presented' to the Society at the same meeting.

What was the initial reaction to these two Earth-shattering arti­cles? In his annual report for that year, the President of the Society, Thomas Bell, wrote that 'The year has not, indeed, been marked by any of those striking discoveries which at once revolutionize, so to speak, the department of science in which they occur.' However, this perception quickly changed as the sheer enormity of Darwin's and Wallace's theory began to sink in, and they took a lot of stick from Mustrum Ridcully's spiritual brethren for daring to come up with a plausible alternative to Biblical creation. What was this epoch-making alternative? An idea so simple that everybody else had missed it. Thomas Huxley is said to have remarked, on reading Origin: 'How extremely stupid not to have thought of that.'

This is the idea. You don't need a human being to push animals into new forms; they can do it to themselves, more precisely: to each other. This was the mechanism of natural selection. Herbert Spencer, who did the important journalistic job of interpreting Darwin's theory to the masses, coined the phrase, 'survival of the fittest' to describe it. The phrase had the advantage of convincing everybody that they understood what Darwin was saying, and it had the disadvantage of convincing everybody that they understood what Darwin was saying. It was a classic lie-to-children, and it deceives many critics of evolution to this day, causing them to aim at a long-disowned target, besides giving a spurious 'scientific' background to some extremely stupid and unpleasant political the­ories.

Starting from an enormous range of observations of many species of plants and animals, Darwin had become convinced that organisms could change of their own accord, so much so that they could even, over very long periods, change so much that they gave rise to new species.

Imagine a lot of creatures of the same species. They are in com­petition for resources, such as food, competing with each other, and with animals of other species. Now suppose that by random chance, one or more of these animals has offspring that are better at winning the competition. Then those animals are more likely to survive for long enough to produce the next generation, and the next generation is also better at winning. In contrast, if one or more of these animals has offspring that are worse at winning the com­petition, then those animals are less likely to produce a succeeding generation, and even if they somehow do, that next generation is still worse at winning. Qearly even a tiny advantage will, over many generations, lead to a population composed almost entirely of the new high-powered winners. In fact, the effect of any advantage grows like compound interest, so it doesn't take all that long. Natural selection sounds like a very straightforward idea, but words like 'competition' and 'win' are loaded. It's easy to get the wrong impression of just how subtle evolution must be. When a baby bird falls out of the nest and gets gobbled up by a passing cat, it is easy to see the battle for survival as being fought between bird and cat. But if that is the competition, then cats are clear winners, so why haven't birds evolved away altogether? Why aren't there just cats?

Because cats and birds long ago came, unwittingly, to a mutual accommodation in which both can survive. If birds could breed unchecked, there would soon be far too many birds for their food supply to support them. A female starling, for instance, lays about 16 eggs in her life. If they all survived, and this continued, the star­ling population would multiply by eight every generation, 16 babies for every two parents. Such 'exponential' growth is amaz­ingly rapid: by the 70th generation a sphere the size of the solar system would be occupied entirely by starlings (instead of by pigeons, which appears to be its natural destiny).

The only 'growth rate' for the population that works is for each breeding pair of adult starlings to produce, on average, exactly one breeding pair of adult starlings. Replacement, but no more, and no less. Anything more than replacement, and the population explodes; anything less, and it eventually dies out. So of those 16 eggs, an average of 14 must not survive to breed. And that's where the cat comes in, along with all the other things that make it tough to be a bird, especially a young one. In a way, the cats are doing the birds a favour, collectively, though maybe not as individuals. (It depends if you're one of the two that survive to breed or the 14 that don't.)

Rather more obviously, the birds are doing the cats a favour, cat food literally drops out of the skies, manna from heaven. So what stops it getting out of hand is that if a group of greedy cats happens to evolve somewhere, they rapidly eat themselves out of existence again. The more restrained cats next door survive to breed, and quickly take over the vacated territory. So those cats that eat just enough birds to maintain their food supply will win a competition against the greedy cats. Cats and birds aren't competing because they're not playing the same game. The real competitions are between cats and other cats, and between birds and other birds. This may seem a wasteful process, but it isn't. A female starling has no trouble laying her 16 eggs. Life is reproductive, it makes rea­sonably close, though not exact, copies of itself, in quantity, and 'cheaply'. Evolution can easily 'try out' many different possibilities, and discard those that don't work. And that's an astonishingly effective way to home in on what does work.

As Huxley said, it's such an obvious idea. It caused so much trouble from religionists because it takes the gloss off one of their favourite arguments, the argument from design. Living creatures seem so perfectly put together that surely they must have been designed, and if so, there must have been a Designer. Darwinism made it clear that a process of random, purposeless variation trimmed by self-induced selection can achieve equally impressive results, so there can be the semblance of design without any Designer.

There are plenty of details to Darwinism that still aren't under­stood, as with all science, but most of the obvious ways of trying to shoot it down have been answered effectively. The classic example -still routinely trotted out by creationists and others even though Darwin himself had a pretty good answer, is the evolution of the eye. The human eye is a complex structure, and all of its compo­nents have to fit together to a high degree of accuracy, or it won't work. If we claim that such a complex structure has evolved, we must accept that it evolved gradually. It can't all have come into being at once. But if so, then at every stage along the evolutionary track the still-evolving proto-eye must offer some kind of survival advantage to the creature that possesses it. How can this happen? The question is often asked in the form 'What use is half an eye?', to which you are expected to conclude 'nothing', followed by a rapid conversion to some religion or other. 'Nothing' is a reasonable answer, but to the wrong question. There are lots of ways to get to an eye gradually that do not require it to be assembled piece by piece like a jigsaw puzzle. Evolution does not build creatures piece by piece like the God of Evolution in The Last Continent. Darwin himself pointed out that in creatures alive in his day you could find all kinds of light-sensitive organs, starting with patches of skin, then increasing in complexity, light-gathering power, and ability to detect fine detail, right up to structures as sophisticated as the human eye. There is a continuum of eyelike organs in the living world, and every creature gains an advantage by having its own type of light-sensing device, in comparison to similar creatures that have a slightly less effective device of a similar kind.

In 1994 Daniel Nilsson and Susanne Pelger used a computer to see what would happen to a mathematical model of a light-sensing surface if it was allowed to change in small, random, biologically feasible ways, with only those changes that improved its sensitivity to light being retained. They found that within 400,000 generations, an evolutionary blink of an eye, that flat surface gradually changed into a recognizable eye, complete with a lens. The lens even bent light differently in different places, just like our eye and unlike normal spectacle lenses. At every tiny step along the way, a creature with the improved 'eye' would be better than those with the old version.

At no stage was there ever 'half an eye'. There were just light-sensing things that got better at it.

Since the 1950s, we have been in possession of a new and central piece of the evolutionary jigsaw, one that Darwin would have given his right arm to know about. This is the physical, more precisely, chemical, nature of whatever it is that ensures that characteristics of organisms can change and be passed from one generation to the next.

You know the word: gene.

You know the molecule: DNA.

You even know how it works: DNA carries the genetic code, a kind of chemical 'blueprint' for an organism.

And, probably, a lot of what you know is lies-to-children.

Just as 'survival of the fittest' captured the imaginations of the Victorians, so 'DNA' has captured the imaginations of today's pub­lic. However, imaginations thrive best if they are left free to roam: they grow tired and feeble in captivity. Captive imaginations do breed quite effectively, because they are protected from the terrible predator known as Thought.

DNA has two striking properties, which play a significant role in the complex chemistry of life: it can encode information, and that information can be copied. (Other molecules process the DNA information, for example by making proteins according to recipes encoded in DNA.) From this point of view a living organism is a kind of molecular computer. Of course there's much more to life than that, but DNA is central to any discussion of life on Earth. DNA is life's most important molecular-level 'space elevator', a platform from which life can launch itself into higher realms.

The complexity of living creatures arises not because they are made from some special kind of matter- the now-discredited 'vitalist' theory, but because their matter is organized in an exceedingly intricate fashion. DNA does a lot of the routine 'bookkeeping' that keeps living creatures organized. Every cell of (nearly) every living organism contains its 'genome', a kind of code message written in DNA, which gives that organism a lot of hints about how to behave at the molecular level. (Exceptions are various viruses, on the boundary between life and non-life, which use a slightly different code.)

This is why it was possible to clone Dolly the Sheep, to take an ordinary cell from an adult sheep and make it grow into another sheep. The trick actually requires three adult sheep. First, there's the one from which you take the cell: call her 'Dolly's Mum'. Then you persuade the cell's nucleus to forget that it came from an adult and to think that it's back in the egg, and then you implant it into an egg from a second sheep ('Egg Donor'). Then you put the egg into the uterus of the third sheep ('Surrogate Mum') so that it can grow into a normal lamb.

Dolly is often said to be a perfect copy of Dolly's Mum, but that's not completely true. For a start, certain parts of Dolly's DNA come not from Dolly's Mum, but from Egg Donor. And even if that slight difference had been fixed, Dolly could still differ in many ways from her 'mother', because sheep DNA is not a complete list of instructions for 'how to build a sheep'. DNA is more like a recipe, and it assumes you already know how to set up your kitchen. So the recipe doesn't say 'put the mixture in a greased pan and place in an oven set to 400°F,' for instance: it says 'put the mixture in the oven' and assumes that you know it needs to go in a pan and that the oven should be set to a standard temperature. In particular, sheep DNA leaves out the vital instruction 'put the mixture inside a sheep', but that's the only place (as yet) where you can turn a fertil­ized sheep egg into a lamb. So even Surrogate Mum played a considerable role in determining what happened when the DNA recipe for Dolly was 'obeyed'.

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 codes 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.